"APPROXIMATE REASONING ABOUT TEMPORALCONSTRAINTS IN REAL TIME PLANNING AND

SEARCH"

bySoumitra DUTTA*

andShashi SHEKHAR**

N° 90/13/TM

Assistant Professor of Information Systems, INSEAD, Boulevardde Constance, 77305 Fontainebleau, France

* * Department of Computer Science, University of Minnesota,Minneapolis, U.S.A.

Printed at INSEAD,Fontainebleau, France

APPROXIMATE REASONING ABOUT TEMPORAL CONSTRAINTS

IN

REAL TIME PLANNING AND SEARCH

Soumitra Dutta Shashi ShekharTechnology Management Area Dept. of Computer Science

INSEAD

University of MinnesotaFontainebleau, France 77305

Minneapolis, USA

Abstract

Artificial Intelligence (AI) systems are being increasingly applied to

challenging real time problems. Real time AI systems typically have to

operate under stringent temporal constraints. These temporal constraints

can be either explicit (e.g., a system may have to respond within a

given deadline) or implicit (e.g., there may be no explicit deadlines but

optimal response times may be desired). This paper is concerned with

approximate reasoning about temporal constraints present either

explicitly or implicitly in real time systems. A graph search procedure is

used as the conceptual representation for real time reasoning and

planning. The cost of a response consists of the cost to plan a solution

and the cost to execute the chosen solution. There is an intimate

tradeoff between these two costs. Algorithms for approximate reasoning

about such tradeoffs are discussed in this paper. In particluar, a

heuristic algorithm called NORA is proposed for determining the tradeoff

between planning and execution costs and a formal proof is given for.

the performance bound obtained. The application of NORA to the domains

of semantic query optimization and the A* heuristic search algorithm is

described.

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1 Introduction

As the application of AI systems evolves from an art to an engineering

science, more challenging applications shall be addressed. Some of the most

challenging and interesting applications can be found in real-time domains. In

real-time domains, the external world is dynamic and potentially ever-

changing. Three major phases can be identified in the solution structure:

[1] Data Collection : This phase refers to the collection of data about the

external world either through individual observations or from the

outputs of various sensors.

[2] Planning & Reasoning : This phase includes all planning and reasoning

that is done on the data (collected during the data collection phase)to obtain a solution. The terms planning and reasoning are used here

in a very general sense to include all different manipulations of data

used to obtain a solution and may include one or more of a diverse

range of techniques such as table lookups and heuristic rule-based

methods.

[3] Execution : This is the final phase during which the solution obtained in

the earlier phase (of planning ) is executed In the real world.

Figure 1 about here

Typically, these three phases occur sequentially (as shown in figure 1), i.e.,

first some data is collected, then a solution is found and finally the solution is

executed. In general however, the divisions between these three different

phases are neither crisp nor universal. Thus It Is quite possible for these

various phases to overlap, e.g., some additional data may have to be collected

during the planning phase or some replanning may be necessary during the

execution phase. For simplicity, it may be assumed that the three phases occur

sequentially and do not overlap (as shown in figure 1).

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Each of the three phases shown in figure 1 typically consume some time as

shown in figure 2. In figure 2, the time axis begins at some arbitrary point of

time, to, and the time periods consumed by each of the three phases are:

Data Collection: [ to, t1 3

Planning : [ t 1 , t2 3

Execution : [ t2, t3 3

Figure 2 about here

Let W(t) represent the state of the external dynamic world at time t. It can

be seen from figure 2, that the state of the world at the beginning of the

execution phase, W(t2 ) is potentially different from that at the beginning of

the data collection phase, W(to). Even if it is assumed (reasonably) that the

data used for planning correctly represents the state of the world at time t1,

the end of the data collection phase, it is useful to minimize the sum of the

planning and execution times in order to minimize the difference between the

state of the world, W(t 1 ), on which the planning is based and the states of

the world, W(t2 ) through W(t3 ), during which the solution is executed. This

would maximize the chances that the selected solution can actually be executed

(i.e., the world has not changed so as to prevent the execution of the chosen

solution) and would minimize the time necessary to observe the effects of the

executed solution. It is necessary to provide some capability for meta-level

reasoning about the relative durations of these three different phases. For

example, any excessive expenditure of time in the planning phase may offset

the gains (possibly obtained by the extra planning) in the execution time and

may also render the solution determined on the basis of data collected from

W(t 1 ) useless for execution in the world at time t 2, as W(t2 ) through W(t3 ) may

be drastically different from W(t 1 ). The situation is more complicated in the

case of deadlines.

Such kind of meta-level temporal constraints on planning and execution are

common in artificial intelligence (AI) systems operating in real-time situations

which typically have to respond within a certain deadline, or have optimal

response times (for planning and execution). For example in managing

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defensive weapons against nuclear missiles, the system has to respond within

a few seconds or minutes [1]. On the other hand, in credit approval systems

[2], optimal response times are desired, but there are no hard deadlines.

Previous research in artificial intelligence has typically focussed solely on

solution techniques for use during the planning and reasoning phase and

there is considerable literature on a variety of related topics, such as

knowledge representation, rule-based systems, etc. Real time systems and its

related issues of meta-level temporal constraints of deadlines and optima

response times have been largely ignored by the artificial intelligence

community. Current AI systems are usually designed to demonstrate technology

applications, and most planning and problem-solving algorithms are not

structured to meet real time (temporal) constraints. For example, planning

methods based on A* [3] and IDA* [4] may potentially take exponential time for

problem-solving. Within a given deadline to solve a certain problem, the

system may not produce any solution (complete or partial). Scaling them up-

with real world data and adequate knowledge bases, would amplify their

performance problems, including their inability to meet real time constraints

[5]. Korf [6, 7] has worked on planning and search algorithms to meet strict

deadlines which guarantee a partial solution within a fixed deadline. However,

his work has ignored execution time and essentially spent all available time for

planning. There has been no previous work on planning algorithms which

take into account the times spent on the data collection, planning and

execution phases of the total solution structure (figure 2). This paper

addresses this important aspect of real time problem solving and develops

algorithms that perform meta-level reasoning about temporal constraints on the

relative durations of the data collection, planning and execution times in real

time planning.

2 Motivation

Figure 3 about here

There are two important classes of real time constraints (see figure 3):

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[1] Deadlines : This is the case wherein a real time system has to produce a

response within a fixed deadline. For example, in some chess

tournaments, moves have to made within a fixed time limit. The deadline

problem is hard and many such problems (e.g., scheduling with

deadlines) are NP-complete [8]. Providing a guaranteed optimal response

within a fixed deadline is not possible always and usually at most a

partial solution or a coarse solution within the given time constraint can

be guaranteed. For many problems, partial solutions are acceptable. An

example of such a problem is robot path planning, where a robot is

required to move as far as possible within the deadline when asked to

move from point A towards point B within a fixed time limit.

[2] Optimal response times : This is the case where the desire is to produce

and execute a solution within the least amount of time. Ignoring data

collection time, the response time is the sum of planning time amp

execution time, and the application requires an optimal response time. As

shown in figure 4, in general, the optimal response time is not achieved

by solely minimizing execution time, as then planning time increases to

offset the gains. The American Express credit card transaction approva!

system [2] is an example of such a system. Here the system response

time should be as small as possible. Many on-line information retrieval

systems also fall under this class of real time systems.

Figure 4 about here

The above classification of real time systems has been mentioned in the

literature [9], which provides the following two common definitions of real

time:

[1] There is a strict time limit by which the system must have produced a

response, regardless of the algorithm employed [93.

[2] The system is predictably fast enough for use by the process being

serviced [10].

These definitions each emphasize different aspects of real time performance.

The first definition is representative of the deadline problem faced by many

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real time systems. Currently, ad hoc techniques are used for making a system

produce a response within a specified time interval, and these methods suffer

from poor extensibility, brittleness and a lack of a formal proof of "reliable

real time performance" [9].

The second definition is less stringent on the hardness of the deadline it

places on the performance of the real time system and is representative of the

optimal response time problem described earlier. Consider the case of Ft user

querying a database from a terminal. The user types in a query ant; waits fc-

an answer. He imposes no strict deadline, but also does not want to be kept

waiting for an inordinate period of time. Thus the system should minimize its

response time so as to provia the best possible real time service. There are

several other applications where providing the optimal response time is of

overridin2 importance.

Ur1der temporal constraints of either deadlines or optimal response times, a

real time system must perform some meta level reasoning to determine the

relative amounts of times to be spent on each of the three solution phases

shown in figures 1 and 2. When faced with a deadline to plan and execute a

solution, a real time system has to stop planning at some time instant ahead of

the deadline such that it has sufficient time left to execute the chosen

solution. The tradeoffs between the relative durations of the various phases

becomes more complex under the constraint of providing optimal response

times. For example, a greater planning cost may possibly lead to a lower

execution cost, but the extra time spent planning may also lead to a potential

Increase in the response time as shown In figure 4. Thus it is crucial that a

real time system be able to decide upon the appropriate moment to stop

planning and start executing the best solution plan obtained till then. Korf [6]

has mentioned the existence of this tradeoff and agreed that

"in principle we could find algorithms that minimized total solution time

by balancing thinking time and action time".

This paper determines algorithms which consider this trade-off between

thinking time and action time and proves bounds on the optimality of the

results obtained. A general conceptual framework of planning (described in the

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next section) is chosen for the exposition of the results.

3 Problem Definition

Though the data collection phase extends from to to t 1 in figure 2, it is

reasonable to assume that the data collected accurately reflects the state of

the world at time t 1 . This data about W(t1 ) is then used for the planning

phase. It can be assumed that the sensors used to collect data about the

external world provide all the data at time t 1 . The data collection phase can

be considered to occur around the instant, t 0 = t 1 , which simplifies the

problem of meta-level temporal reasoning down to the study of the tradeoff

between the planning and execution times. This simplification is quite

reasonable for many applications. For example, in some engineering situations,

there are sensors that continuously monitor some aspect of the external woric

(such as a voltmeter monitoring the voltage) and the processing unit may at

some instant sample the values of these sensors for use in further planning

and execution.

The tradeoff between planning and execution times was graphically

represented in figure 4. It is desirable that the meta-level reasoning be able

to stop planning and start executing as close to the optimal point (of minimum

response time) as possible. Before attempting to provide any principles to

guide such action, it is necessary to state clearly the conceptual model of

planning in the context of which such principles can be stated.

Planning is a very general term, with different connotations to different

research communities. Within AI, it generally refers to reasoning about actions.

A recent book by Wilkins [11] provides a good survey and introduction of

relevant issues in the area. Search is an important part of any planning

system and there are several good reviews of search strategies in the context

of planning [12, 13, 14]. Researchers such as Korf [15] have supported the

thesis that planning can be viewed as problem solving search. Wilkins [16] has

drawn the distinctions between searching a space of partial plans as opposed

to searching a space of world states, and searching through plan

specializations rather than through plan modifications. This paper considers a

very general description of the search space. Conceptually, the search space

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can be modeled as a set of nodes with inter-connecting arcs. Each node is a

possible plan and has an associated execution time. Each arc represents either

plan specialization or plan modification. The planning time associated with each

node consists of the sum of the cost of planning at the node and the cost ot

reaching that node from the previous node. These costs are measured in some

common units (time). The cost of planning at a node includes all costs

associated with planning at that node, such as plan generation and pre-

condition checking. The cost of arc traversal includes the cost of generating

suitable plan refinements or plan modifications. The exact nature of the

division of costs between nodes and arcs is not crucial to the results

presented below. The conceptual idea important to the results is that planning

consists of a search in a graph of possible plans for the best possible plan

(i.e., the least cost plan) and that it is possible to keep track of total

planning cost (time) incurred at any stage. This view is very general and can

be applied to most current planning systems.

In this context, the problem addressed in this paper can be simply stated as:

To propose and study principles guiding the trade-offs between

planning and execution costs in real time systems

The aim is the determination of principles which help a real-time system to

perform meta-level reasoning to decide when to stop planning and when to

start executing the best solution found so far. The term best solution is

used in this paper to refer to the solution with the least execution cost. For

clarity and simplicity, this cost is equated to the execution time. Implicit in

this usage Is the simplification that the quality of the solution is being

measured primarily by the time taken for execution. This assumption is valid

only for certain domains, such as query optimization. In many other domains,

the quality of a solution is a multi-attribute vector, one component of which

is the execution time. However, the theoretical developments and analyses

developed here shall be equally valid for such situations as long as planning

and execution costs are expressed in some common units (some virtual units,

such as utils to express utility can be used).

The emphasis is also on cases where the planning and execution costs are

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comparable, as only then is it important to consider their relative trade-offs.

If either the planning or execution cost is small, some simplifications are

possible. For example, if planning cost is small, it may be possible to search

the entire search space quickly and find the optimal solution to execute. On

the other hand, if execution cost is small, it is again possible to spend most of

the time on planning. This latter case is the usual case considered by

algorithms like A*, which typically ignore exeuction costs.

In general, several factors can complicate the determination of optimal policies

deciding the trade-off between planning and execution costs. Most real life

situations are extremely complex and the associated search spaces are very

large. Searching the entire search space to determine the best solution may

require prohibitively large amounts of time and thus may not be possible in

real time situations. Also, it is often not possible to apriori specify the goal

node, i.e., the solution with the least execution cost. This is in contrast to the

requirements of popular search algorithms such as A* (and even its real

time equivalent RTA* proposed by Korf [6, 7]), which require that the goal

node be specified apriori .

4 Review of Previous Research

Surprisingly, there has been little research in artificial intelligence on real

time search and planning. Simple blind search algorithms like depth first

search, breadth first search and depth first iterative deepening [4] are useful

with small search spaces and large deadlines. However, the size and complexity

of most search spaces faced by real time AI systems preclude the use of these

algorithms. Heuristic search algorithms like A* and IDA* are not very useful

for real time problem domains as they may take exponential time in producing

a solution. These search strategies may be able to guarantee an optimal

solution in the absence of limits on search time, but cannot function with the

constraint of deadlines and response times. The notion of a deadline limits the

search horizon and often necessitates the taking of an action before the

optimal solution is found.

Korf [6] has formulated variations on minmax search and A* to adapt to the

real time scenario. His algorithm for bounded look-ahead search in single

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agent problems is called minmin search and it involves searching forward

from the current state to a fixed depth horizon determined by the deadline

and then applying the A* cost function of f(n) = g(n) + h(n), to the frontier

nodes. The minimum value is then backed up and a single move is made in the

direction of the minimum value. Making a single move at a time follows a

strategy of least commitment and allows a different second move to be

recommended by further search (caused by the dynamically changed

environment). He also proposes an analog to alpha-beta pruning that makes

the same decision as minmin search but by exploring fewer nodes.

Korf has further proposed a real time modification of A* called RTA* for

controlling the sequence of moves actually executed. The neighbors of the

current state are generated and a heuristic function, including look-aheac

search with alpha pruning is applied to each new state. The neighbor with the

minimum g+h value is chosen as the new current state, and the old current

state is stored in a table along with the second best g+h value, which is the

best value among the remaining children. This represents the best estimate of

the cost of finding the solution via the old current state from the perspective

of the new current state. Russell [17] has extended RTA* by adding meta-

greedy decision-theoretic search control to RTA*.

The real time algorithms proposed by Korf essentially deal with the problem of

deadlines in real time systems (see classification in figure 3). They ignore the

execution cost of any solution and essentially spend all available time (the

deadline) searching. This is not realistic in many situations where execution

time is appreciable.

The above-mentioned concerns about "thinking time and "action time in real

time situations is a special case of the more general issue of bounded

rationality [18] and computation under resource constraints. There has been

considerable research both within decision theory and conventional AI, on

normative bases for reasoning. Several researchers [18, 19, 20] have pointed

to deficiencies in using normative theories (i.e., probability theory and utility

theory) as a consistent axiomatic basis for complex, real world inferences. The

earliest discussion on the explicit integration of the costs of inference within

the framework of normative rationality was introduced by Good [21]. Good

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defined type I rationality as inference that is consistent with the axioms of

decision theory without regard to the cost of inference. Type II rationality

was defined as behavior that takes into consideration the costs of reasonina.

Several researchers such as Horvitz [18, 20], Matheson [22], Watson and Brown

[23], and Lindley [24] have explored the integration of computation costs into

inference strategies in the spirit of the work by Good. However, their primary

emphasis has been on techniques for reformulating a base problem into one

that will be of greater value than a complete analysis would be, given

computational resource constraints. The trade-offs considered by them have

been different from that considered in this paper, e.g., Horvitz [19] has

studied the trade-off between the immediacy and precision of the results

and how it affects the selection of alternative computation strategies. A

criticism of these approaches is that they often require detailed apriori

knowledge of relative utilities and precisions. An expert may have difficulty in

articulating such numbers and they may not be available always.

An alternative approach has been taken by researchers such as Bonissone and

Halverson [25] who have focussed on pre-compiled knowledge about the

problem solving domain in order to control the search strategy and to

select/construct the most valuable inference strategy. Such an approach is

applicable only for well structured situations in which such detailed knowledge

is available apriori for compilation. The approach taken in this paper is

different in that it does not assume the availability of any such knowledge for

compilation.

5 Planning & Search With Qptimal Response Times

The trade-offs between planning and execution costs become significant while

trying to provide optimal response times in real time systems where the

planning and execution costs are comparable (figure 4). Without explicit apriori

knowledge about the domain characteristics (both static and dynamic), it is not

easy to devise algorithms that can always provide the optimal response time.

In general, such explicit knowledge is very difficult to obtain and probably

very unreliable, as the external world can change in a myriad number of

ways. Also, as the goal is often not known apriori , it is very difficult (in

general) to come up with a precise stopping rule. Under such circumstances,

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it is best to try to devise algorithms that provide some heuristic principles to

guide the tradeoff between planning and execution costs. While good heuristics

are often useful, it is even better If bounds can be proved on the obtained

response. This section describes NORA, a simple and intuitively appealing

planning and search algorithm for providing near optimal response times in

real time systems.

5.1 NORA: A Near Optimal Resoonse-time Algorithm

Blind exhaustive searches like BFS or DFS are generally applicable to limited

domains. Most heuristic search algorithms, e.g., A*, require the ability to

recognize the goal node (i.e., the best plan). This is not always possible, (e.g.,

recognizing the cheapest query execution plan in semantic query optimizatior

[26]) and one needs other heuristics to control the search.

Given a search algorithm, a real time system has to make a crucial decision of

determining the time when to stop searching and start executing so as to

provide a near optimal response time. This problem is simplified for small

search spaces (where it may be possible to search the entire search space

quickly) or when it is possible to recognize the best plan (so that the search

can be stopped as soon as the best plan is found). In general it may not be

possible to characterize apriori the best plan and even if it is possible to

characterize it for recognition, reaching it in many search traversal algorithms

may require prohibitively large time.

The basic idea of NORA is simple and intuitive. As mentioned earlier, the

search space is modeled as a set of nodes with inter-connecting arcs. Eacn

node represents one possible plan (or set of actions) and has an associated

execution cost. A search traversal algorithm specifies the order of traversal of

these nodes and there are costs associated with this traversal. These costs

include the cost of actually moving from node to node and any associated

computation costs at each node (for example, to estimate the execution cost of

the plan at the node). The chosen search traversal algorithm goes from node

to node in some order to find the node with the best plan (i.e., the least

action time or equivalently least execution time ). At each node, NORA keeps

track of two metrics:

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[1] Planning cost so far: this represents all associated costs of planning so

far and includes all cost of traversing the search space. This metric isan umbrella that includes costs of all activities associated with al,

different planning methods used.

[2] Least execution cost so far: this is the least execution cost so far, or

equivalently the best plan found till then.

The search space traversal is terminated whenever the planning cost exceeds

some fraction of the best execution cost found so far, i.e., when the following

condition is satisfied:

least executioncost so far planning cost so far > 6

where (3 defines the fraction. As shown below, this simple heuristic allows the

achievement of a near-optimal response time (when the search is terminated

with the above condition satisfied) satisfying the following bound: iResponse time (optimal) max (1 + 6, 1 +)

This is for the general case, in which we are unable to recognize apriori the

best plan. In cases, where it is possible to recognize the best plan, tighter

bounds can be obtained. Evidently, the choice of 13 is crucial and heuristics

for the choice of an appropriate 13 are also described below. It should be

noted that the bound holds for any given search space traversal ordering

(e.g. A*).

5.2 Correctness of NORA

Response time (obtained whenstopped)

Some notation used in the correctness proof below is introduced in table 1.

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As mentioned earlier, the search space is structured as a set ot

interconnected nodes Q i , each node representing a possible plan.

Table 1 about here

Correctness Theorem for NORA: If the following search terminating criterion

is used,_,„ ;rki) _ 6

the following upper bound on RT(i) is obtained:RT(i) max(1+13,14)RT(opt)

Proof: Supposing the search terminates after examining Q i . There are thefollowing two cases:

[a] Clopt SP(i), i.e., Qopt has been visited.

[b] Qopt has not been visited.

CASE [a]RT(i) r(i)+t(i)

RT(opt) r(opt) +t(opt)

t4.t.(04. 6

r(opt)+t(opt)since r(i) = ICU ,

B

A positive quantity, ö is added to the numerator to make the equality hold. Itsatisfies the condition, 0 5 5 5. Opt cost in current step.

RT(i) < 6 RT(opt) t(opt) since T(opt) 0

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(g+1)t(opt)+6

t(opt)

because Q e SP(i) => t(i) t(opt)Opt

1 + 16

Because optimization cost in current step t(opt) => (5/t(opt));-- •0. This

assumption is satisfied by many planners, e.g., query optimizers.

Case [b]:

RT(i) 7(1)+t(1) RT(opt) T(opt)+t(opt)

70)+61-(1) t(i) -r(-Da_ ,Tkopt)+t(opt)

< r(i)+67(i) - T(opt)

< (1+6)T(i)- T(i) -

t(opt)a0

1 + 6

Since Q opt SP(i) => r(opt) r(i). Combining the results of the cases above,

we have:

RIM < max ( 1 +6,1+8)RT( opt)

The bound provided by the above theorem is loose, but the termination

criterion usually performs much better In practice. This was observed during

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experimental validation, the details of which are discussed in the next section.

Corollary 1: If the planning is the compile-and-store type, and is expected tobe executed 1.1 times, the above bound changes to:

RT(i) < max( 1+134, 1+k)RT(opt)

Proof: Identical to the above theorem. (It should be noted that RT(i) = r(i)+mt(i) and represents an integrated cost.)

I5.3 Heuristics For Choice Of @

The choice of 13 depends on the size of the search space, and the timeavailable for planning. For a small search space, Case [a] would be expected tooccur when the search terminates, i.e. Qopt 6- SP(i). ThusSmall space z> Case [a]=> Choose large 13 for tight bound=> Less planning is good for response time

For a large search space, Case [b] would be expected to occur when thesearch terminates, i.e., Qo pt * SP(i). Thus,Large space --z> Case [b]=> Choose small 13 for tight bound=> More planning is good for response time

Rule of Thumb for Choosing 13: The above analysis shows that the thumb ruleis to coose a large 13 if the search space is small and vice versa. It is not acertain rule because of the approximate implications (2:>) shown above.

5.4 General Comments

NORA provides a simple and intuitive, heuristic algorithm for determining thetradeoff between planning and execution times in real time planning andsearch. Two points concerning NORA should be emphasized:

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[1] The validity of NORA is independent of the search procedure chosen. Thus

any order of traversal specified by any search procedure can be usec

to traverse the search space, and as long as the termination criterion of

NORA is satisfied, the performance bound of NORA shall be satisfied by

the response time obtained.

[2] NORA does not require any apriori domain knowledge. The only metric

needs are some estimates of the execution times of different solutions

and a clock to keep track of the planning time used up so far. It aiso

does not require the goal to be specified apriori .

6 Real-Time Aoolications of NORA

This section demonstrates the application of NORA in two sample domains.:

query optimization (planning) and the A* heuristic search algorithm.

6.1 Semantic Query Optimization

NORA was implemented for optimizing the response time of the query optimizer

for data-bases. Most databases are on-line and users access desirecinformation via a query, represented in a suitable query language. Queries

involve operations like join, select, and project, and can be answered by

executing several different execution-plans each with different execution-times.

The query optimizer generates and examines many execution plans to choose

the one with the least execution-time. The planning time increases

exponentially, as the optimizer expands its search of possible execution plans.

Conventional query optimization (planning) is based on syntacticrearrangements [27] , query decomposition [28], and optimal usage of indices,

join algorithms and database statistics [29]. An orthogonal optimization

technique utilizing application specific knowledge, is Semantic Query

Optimization (SQO) [26]. SQO uses semantic information about the database

instance, eg. semantic integrity constraints and functional dependencies, for

optimization. The original query Q0, is transformed into syntactically different,

but semantically equivalent queries Q1 , Q2 , ..., Qn, with the hope that one of

the semantically equivalent queries may possibly yield a more efficient

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execution plan than the original query.

Conventional query optimizers select the optimal query execution plan by

searching through QP(0), where QP(0) is the space of query plans

corresponding to the original user query, Q0. In contrast, semantic query

optimizers search the much larger space of execution plans, Q tot,

QP tot = QP( 0 ) QP(1) +....+QP(n)

where QP(i) is the space of query execution plans corresponding to query Qi

and "+" represents the union of the various query plan spaces, QP(i).

The search space can be conceptually modeled in two levels:

[1] Level 1: The space of semantically equivalent but syntactically different

queries, Q0 , Q 1 , ..., Qn.

[2] Level 2: The spaces QP(0), QP(1), QP(n), which are the spaces of

possible execution plans for the queries Q 0, Q 1 , ..., Qn , respectively.

This has been illustrated in figure 5. The solid lines represents the edges at

level_1 search space, and the dotted triangles together constitute the level_2

search space.

Figure 5 about here

As evident, semantic optimization increases the search space of possible plans

by an order of magnitude. This causes the optimization cost to become

comparable to the cost of query execution. The tradeoff between optimization

time and the cost of query execution becomes a major issue In optimizing the

total cost of query processing.

A semantic query optimizer has to search at level_1 of figure 5 to find the

query Q i that leads to the least execution cost. While traversing the space of

semantically equivalent queries, it is not possible to apriori specify the querywith the least execution cost. Thus in the experimental validation, a best first

19

search was used to order the traversal at level_1. The cost of planning hereconsists of the time required to move from one node to the other (i.e., to get

a new semantically equivalent query) and the computational cost at each node

to estimate the execution cost at that node. The execution cost associated with

each node consists of the estimated execution time of the best query executionplan for the query corresponding to that node (obtained by running a

conventional query optimizer on the query associated with that node).

6.2 Experimental Validation

NORA was tested for semantic query optimization for a shipping database of

six relations. The database schema, the relation sizes, semantic integrity

constraints and the various indexes available are reported in detail in [34, 35].

The first step in the experiment was to generate the entire space of

semantically equivalent queries. This was done manually by applying rules, i.e.semantic integrity constraints, giving rise to queries Q 1 through Q15, which

were all semantically equivalent to an input user query Q .0

Figure 6 about here

Figure 6 shows the entire generated search space of semantically equivalent

queries. In figure 6, each node (box) represents a semantically equivalent but

syntactically distinct query. The numbers on top of each node are the query

numbers, i.e., alternative semantically equivalent queries. The arrow points to

the input user query Q0 . Each query is a conjunction of the clauses Ci's.

Rules (semantic/Integrity constraints) R i 's perform the transformationsbetween queries by adding or deleting clauses. Each of the queries Q0

through Q15 was optimized by the R* [30] optimizer to obtain the best

estimated execution costs associated with each query (shown as numbers

within the boxes in figure 6). The NORA algorithm was hand-simulated on the

graph shown in figure 6. NORA terminates the search when the following

condition becomes true.

r(i) > t 8i

20

Table 2 about here

The stopping rule was examined for the values 13 = 2, 1, and The results

are presented in Table 2. The result can be verified for all the three values

of 13. The validation for 13=1 is illustrated below. The search stops after

iteration 3, since the stopping criterion of NORA evaluates to

(163)(1) + (5)(5) > 127.0 => 188 > 127.0

which is true. The optimization cost and best execution cost estimate are

r(i) = 188 ms; t(i) = 127.0 ms

The bound of the Correctness Theorem is satisfied since

7(1)+t(i) 315,r(opt)+t(opt)-224-2

It can be seen that stopping rule is quite effective. Especially notable is the

fact that even though it was attempted to minimize a weighted sum of t(i) anc

t(i), and not t(i) alone, the value of t(i) obtained is actually quite close to the

minimum.

6.3 Ax Heuristic Search Algorithm

A* [14] is probably the best known heuristic search algorithm for finding a

solution path from an initial start node to a final goal node. A* essentially

does a best-first search of the search space, where the merit of a node, f(n),

is the sum of the actual cost, g(n), of reaching that node, n, from the start

node, S, and the estimated cost, h(n), of reaching a goal node, G, from that

node. A* has the property that it will always find an optimal solution to a

problem if the heuristic function, h(n), is admissible, i.e., it never

overestimates the actual cost of reaching a goal node. Further if the heuristic

function h, satisfies the monotone restriction [14] (i.e., the estimate of the

optimal cost to a goal from node n i not be more than the cost of the arc from

node n i to ni plus the estimate of the optimal cost from ni to a goal), then it

can be proved that A* already has found an optimal path to any node that it

selects for expansion, i.e., it never expands any nodes not on the optimal path

from the start node to a goal node.

21

A* ignores execution time and essentially concentrates on searching the search

space for an optimal solution path. As described in section 4, even the real-

time variations on A* proposed by Korf ignore execution time. This is not

desirable always as explained earlier. To apply NORA or any related formalism

to A*, the notion of execution time has to be introduced. It should be notea

that in A*, the implicit assumption of being able to apriori recognize the goa'

node is made (necessary for the evaluation of the heuristic function h). This

is in contrast to the more general framework of NORA and the semantic query

optimization example described earlier where it is assumed that it is not

possible to apriori recognize the goal node.

Associated with any node n, in the search space, let ge(n) represent the

execution time for executing the partial solution (corresponding to the path

from the start node to node n) found so far and let he(n) represent the

heuristic estimate of the cost of execution from the partial solution to a goa!

node (corresponding to a path from node n to a goal node). Note that ge(n)

and h e(n) are distinct from the conventional g(n) and h(n) functions of A*.

While g(n) estimates the cost of expanding the search tree from the start node

to the node n and thus contributes to the planning costs for finding a

solution path from the initial state to the partial solution state specified by

node n, ge(n) estimates the cost of actually executing that partial solution

path in the real world. Similarly, while h(n) estimates the planning cost of

further expanding the search tree from node n to a goal node, he(n)

estimates the actual cost of execution while trying to reach a goal from the

partial solution state specified by node n. These functions can be computed as

it is possible to apriori precisely characterize the goal state.

While trying to optimize response times in real time planning and search using

A*, it is possible to decide to terminate the search (based on a chosen search

stopping criterion, e.g., as specified by NORA) at some node i, before reaching

a goal node, G. Assume that stopping at some node i, (i * goal node), forces a

penalty for execution in real time (caused by the distance from the partial

solution state, node i, to a goal node), and let it be represented by p(i). It is

assumed that p(i) is proportional to the distance of the node i from a goal

node and that it monotonically decreases as i approaches a goal node.

22

6.3.1 Aoolyino NORA to A*

Assume that the A* heuristic evaluation function, h, satisfies the monotone

restriction and is admissible. Under these conditions, A* never expands ar,.,

nodes other than those that lie on the optimal solution path and thus at any

node, (i goal node), on the optimal solution path, the total planning cos:

so far is given by g(i) and the best estimate of the execution cost is given ty

the sum of g e(i) and p(i). Let G represent the goal node, and thus the

planning cost for reaching the goal node is g(G) and the execution cost is

given by the sum of g e(G). The stopping criterion as specified by NORA woulc

be:

stop when:

9(i) e (1)+P(1)

Writing the ratio of the response time, RT(i), when stopping at node i over the

response time, RT(G), when stopping at goal node, G, we have:

RT(i) 9(i)+9.(i)+P(i) RT(G) g(G) + 9.(G)

g(i)+9,(i)+p(i)- g(i)+h(i)+9.(G)

Note that g(G) g(i) + h(i) as h is assumed to be admissible. Since h(i) is

positive, it can be ignored from the denominator and on substituting the

stopping condition, we have:

(1+6)g(i)- 9(1)+9.(G)

Dividing both numerator and denominator by g(i):

1+6 g '(G)

1+ 9(i )

23

as both ge(G) and g(i) are positive. Thus the bound of NORA is satisfied. Note

that as 13 is positive, 1+13, is greater than 1.

D.3.2 An Alternative Heuristic Far A*

Having apriori precise characterization of the goal allows the formulation of

another stopping criterion for A*, which uses more domain specific knowledge

(and thus is less general than NORA), but leads to a crisper bound. Assume

that the function h e(i) never over-estimates the real execution time and that

the heuristic evaluation function, h, is admissible and satisfies the monotone

restriction.

Intuitively, it is clear that a useful stopping criterion is to stop when the

following condition holds:

h(i) + he(i) ap(i)

where a is some positive constant decided by the user. Here the LHS is the

sum of the estimated planning and execution costs for reaching a goal state

from the current node, i, and the RHS give the penalty incurred for stopping

at node i and not continuing onto reach a goal node. It is obviously better to

stop at node (i) and incur the corresponding penalty, p(i), rather than spend

the effort, h(i) + h e(i), towards planning and execution from that node i

(towards the goal). Under these conditions:

RT(i) 9(i)+9.(1)+p(i) RT(G) g(G) + g,(G)

Note that as both functions, h(.) and he(.) never overestimate values, the

following substitution can be made in the denominator:

g(i )+g , (i )+p(i)

g(i)+h(i)+g.(i) +h,,(1)

Substituting the stopping criterion, we have:

24

g(i)+Mi)+p(i)- 9(i)+9.(i)+0P(i)

where

p(i)x - g(1)+g.(1)

Here X can be interpreted as the ratio of the penalty p(i), for stopping

prematurely at node i, and the total response time, g(i) + g e(i). Note that the

bound obtained in this case is less than 1, when o is positive and greater

than 1. In contrast, the bound obtained using NORA was larger than 1.

7 Planning & Search Under Deadlines

Section 4 reviewed some work by other researchers on planning under

deadlines. Korf [6, 7] proposed RTA*, a real time modification of A*, to perform

real time heuristic search under deadlines. Korf's algorithms essentially spends

all available time searching/planning and ignore execution costs. To introduce

execution costs into the scenario, RTA* would have to be modified to keep

track of a dynamic deadline , which is in effect the real deadline less the best

execution cost found so far, i.e.,

dynamic deadline = real deadline - least execution cost

The real deadline is set by the external world, and the dynamic deadline is a

virtual deadline of which the system keeps track. The search/planning is to

be terminated as soon as the dynamic deadline is reached. The dynamic

deadline can vary in run time depending upon the variations of the least

execution costs. This is a simple extension to the work of Korf and thus most

of Korf's analysis shall carry over to this modified RTA* also.

Bonissone's [31] approach to planning/search under deadlines has been to

25

precompile the proof tree/search space along with detailed estimates of thecosts of traversing any one path. In run time, depending upon the deadline,

some or all different paths in the proof tree/search space are explored. Some

heuristics are also provided to decide which path to explore next. This

approach is well-suited for well structured domains in which such knowledge

is available apriori.

8 Plannina & Search Using Diminishing Marginal Returns

Heuristic search strategies like best-first usually have diminishing marginal

returns as the search progresses. This can be illustrated by the example

discussed

in section 6.2. Database access paths are tailored to suit the

frequently occurring queries, and semantic transformation based on integrity

constraints can not keep improving the execution cost for a long time. The

optimal query Q min

Q min = Q 1 such that C E (i) 5 CE (j), j=0,1,..,n

has a finite positive cost, and as it is approached during the search, the

chances of substantial improvement in query execution cost keep diminishing.

At the same time the optimization cost remains approximately the same for

every step of searching in the space of queries. Thus, net improvement in

query execution cost keeps diminishing.

A heuristic based on diminishing marginal returns can be used to consider the

net benefit obtained from the last step and decide appropriately the tradeoff

between planning and execution costs. The basic Idea, simply put, is to stop

searching when the improvement in execution costs is dominated by the

planning cost from the last step (node). This termination criterion leads to the

optimal solution only if the law of diminishing marginal utility holds. However,

even if this is not true, the criterion Is still useful for small search spaces.

The searching process may be terminated at a local minima. This can be

partially overcome by using probabilistic search guiding strategies, eg.

simulated annealing [32, 33]. Note however that this heuristic cannot guarantee

either a near optimal response time or response within a deadline.

This can be illustrated with the example of sections 6.1 and 6.2. Search stops

when the optimization cost in last step dominates the improvement in query

26

execution cost, i.e., (using the data of table 2):

Reduction in t(i) < Optimization Cost

At step 1,

Reduction in t(i) in step 1 = inf - 605.7 = m ms

Optimization Cost in step 1 = (10)(1) = 10 ms

At step 2,

Reduction in t(i) in step 2 = 605.7 - 127.8 = 477.9 ms

Optimization Cost in step 2 = (66)(1) + (3)(5) = 81 ms

At step 3,

Reduction in t(i) in step 3 = 127.8 - 127.0 = 0.8 ms

Optimization Cost in step 3 = (87)(1) + (2)(5) = 97 ms

Thus, the search stops after step 3, having incurred a total optimization cos:

of 188 ms, and having generated a query execution plan with an estimated

cost of 127.0 ms. The above analysis shows that this heuristic based or

diminishing marginal returns can be useful since even for an interactive

query (i.e. execute only once) the savings obtained are

605.7 - (188.0 + 127.0) = 605.7 - 315.0 = 290.7 ms .

If the query is to be executed many times the savings are even greater.

9 Summary

This paper has tried to formalize ideas relating to approximate reasoning about

meta-level temporal constraints. The results obtained in this paper are

important because they are the among the first in this area within artificial

intelligence. As mentioned earlier, real time problems are gaining importance in

artificial intelligence. The algorithm developed in this paper will enable

decisions to be made regarding the crucial tradeoff of relative durations of

planning and execution times. Issues from this paper worth emphasizing are:

■ Reasoning about the relative durations of planning and execution times is an

important problem in real time problem solving.

■ Real time reasoning and problem solving can be conceptually modeled as a

search among possible solutions.

27

■ Heuristics are best for determining the tradeoff between planning and

execution times in general search spaces, with weak domain knowledge.

■ NORA uses the following intuitively, simple heuristic as the termination

condition: stop planning when planning costs exceed a fixed fraction of

the best execution cost found so far .

■

NORA is valid for any search algorithm and requires no apriori domain

knowledge.

■ NORA

has been applied in the sample domains of semantic query optimization

and the A* heuristic search algorithm.

■ For planning under deadlines, Korf's algorithms can be modified with a

dynamic deadline .

■ The

concept of diminishing marginal returns can also be used as a stopping

heuristic in real time reasoning.

References

1. R.P. Bonaso, "What AI Can Do for Battle Management: A Report of the AAAIWorkshop on AI Applications to Battle Management," AI Magazine, vol. 9,no. 3, Fall 1988.

2. H.P. Newquist III, "American Express and AI: Don't Leave Home WithoutThem", AI EApert, vol. 2, no. 4, Miller Freeman Publications,500Howard St., San Francisco, CA 94105., April 1988.

3. D. Galperin, "On the optimality of A*," Artificial Intelligence, vol.8, no. 1,pp. 69-76, 1977.

4. R.E. Korf, "Depth-First Iterative Deepening: An Optimal Admissible TreeSearch, " Artificial Intelligence, vol. 27, pp. 97-109, North-Holland,1985.

5. T.J. Laffey and P.A. Cox, "Real-Time Knowledge Based Systems," AIMagazine, vol. 9, no. 1, Spring 1988.

6. R.E. Korf, "Real-Time Heuristic Search: First Results," Proc. AAAIConference, 1987.

7. R.E. Korf, "Real-Time Heuristic Search: New Results," Proc. AAAIConference, 1988.

28

8. M.R. Garey and D.S. Johnson, "Computers and intractability: A guide to thetheory of NP-completeness", W.H. Freeman, New York, 1979.

9. C.A. O'Reilly and A.S. Cromarty, "Fast is not Real-Time in DesigningEffective Real-Time AI Systems," Applications of Artificial IntelligenceII, International Society of Optical Engineers, Bellingham, Washington,1985.

10. J. Marsh and J. Greenwood, "Real Time AI: Software Architecture Issues,Proc. IEEE Nat'l Aerospace and Electronics Conference, WashingtonD.C., 1986.

11. D.E. Wilkins, "Practical planning", Morgan Kaufmann Publishers,PaloAlto, CA, 1988.

12. D. Chapman, "Planning for conjunctive goals," Artificial Intelligence, vol.32, pp. 333-378, July 1987.

13. M.P. Georgeff, "Planning," Annual review of computer science, vol. 2, pp.359-400, 1987.

14. N.J. Nilsson, "Principles of Artificial Intelligence", Tioga, Palo Atto, CA,1980.

15. R.E. Korf, "Planning as search: a quantitative approach," ArtificialIntelligence, 1987.

16. D.E. Wilkins, "Domain-independent planning: representation and plangeneration," Artificial Intelligence, vol. 22, pp. 269-301, April 1984.

17. S. Russell and E. Wefald, "Decision Theoretic Control of Reasoning: Genera,Theory and an Algorithm to Game Playing," Report No. UCB/CSDB8/435,Computer Science Division, U.C. Berkeley, 1988.

18. E.J. Horvitz, G.F. Cooper, and D.E. Heckerman, "Reflection and actionunder scarce resources: theoretical principles and empirical study," InProc. of IJCAI, Detroit, MI, Aug., 1989.

19. E.J. Horvitz, "Reasoning about beliefs and actions under computationalresource constraints", Proc. of the 3rd workshop on uncertainty inartificial intelligence, AAAI, Seattle, WA, July, 1987.

20. E.J. Horvitz, "Reasoning under varying and uncertainresourceconstraints," In Proc. of AAAI, pp. 111-116, Minneapolis, MN,Aug.,1986.

21. I.J. Good, "Rational decisions," J.R. Statistics Soc. B, vol. 14, pp./07-114,1952.

22. J.E. Matheson, "The value of analysis and computation," IEEE Transactionson Systems, Science and Cybernetics, vol. 4, pp. 211-219, 1968.

23. S.R. Watson and R.V. Brown, "The value of decision analys=s," J.R.

29

Statistical Soc. A., vol. 141, pp. 69-78, 1978.

24. D.V. Lindley, "Reconciliation of decision analyses," Operations Research,vol. 34 (2), pp. 289-295, 1986.

25. P.P. Bonissone and P. Halverson, "Time constrained reasoning withuncertainty," Workshop on Real Time Problems in AI, IJCAI, Detroit,MI, 1989.

26. J.J. King, "QUIST: A system for semantic query optimization in relationaldatabases," Proc. 7th VLDB Conf., 1981.

27. Smith and Chang, "Optimizing the performance of a relational algebradatabase interface," CACM, vol. 18:10, 1975.

28. E.

Wong and K. Youseffi, "Decomposition - A strategy for quer;optimization," ACM TODS, Sept. 1976.

29. P. Selinger, "Access path selection in a Relational Database ManagementSystem," RJ 2883, IBM San Jose, 1979.

30. L.F. Mackert and G.M. Lohman, "R* Optimizer Validation and PerformanceEvaluation for Local Queries," Proc. ACM-SIGMOD, pp. 84-95, ACM,1986.

31. P. Bonissone, Private communication, 1989.

32. Y.E. Ioannidis and Wong, E., "Query Optimization by Simulated &Annealing," Proc. SIGMOD, 1987.

33. S. Kirkpatrick, et. al., "Optimization by Simulated Annealing, Science,"pp. 671-680, May 1983.

34. S.

Dutta, "Approximate reasoning with temporal and spatial concepts", Ph.D.thesis, Dept. of Computer Science, U.C. Berkeley, 1990.

35. J. Srivastava, S. Shekhar & S. Dutta, "A formal model of trade-off betweenoptimization and execution costs in semantic query optimization", In theProc. of the Int'l Conf. on Very Large Databases, LA, Aug. 1988.

Figure 1: Three phases of real time problem solving

Data Planning & Execution of

Collection

Reasoning I Solution

• •To TI T2 T3

Time

Figure 2: Temporal decomposition of real time problem solving

Deadlines

z

Real Tune Constraints

on Response Time

Optimal ResponseTimes

CompleteSolution

Rana!

Solution

Figure 3: Classification of Real Time Systems

TIME

EXECUTION............ TIME

OPTIMIZATION EFFORT

Figure 4: Planning, Execution and Response Times

Fig. 5 Conventional Vs. Semantic Query Optimization.

4

(CI ,C3,C4 ,C5127.8

>

7.

R2

_ _ R6_ _

R2

1 'R7 - -

R7_ .1 - •

- - / R116

2

,A1

Fig. 6 Space of Semantically Equivalent Queries.

Symbol Meaning

SP (i)1

space of nodes already exploredupto and including Q,

CE v)

cost of best executionplan for node Qi

-c(i )total planning cost upto

node Q,

t (i)cost of best plan

so far, i.e min CE(j)Q, c SP (1)

RT (i)response time if search

terminates at Q,,i.e. TO )+ t (i)

RT (opt)

theoretically minimum response time

possible for the given search algorithm

RT (opt) .-- min RT (i)osi se,

t(opt), t (opt) components of RT (opt)

QopI node corresponding to RT (opt)

Table I Notation

Iteration t(i) t(i) TO )+t (i ) Stopping(p) RT (i )/ RT (opt)

1 10 605.7 615.7

2 97 127.8 224.8 p =2 I

3 188 127.0 315.0 p =I 1.5

4 359 127.0 486.01

fiz----i. 2.5

5 647 127.0 774.0

Table 2 Performance of NORA

INSEAD 908E180 PAPERS SERIES

1986

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Arnoud DE MEYER

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*Subjective estimation In Integratingcommunication budget and allocationdecisions: a case study', January 1986.

• Sponsorship and the diffusion oforganisational innovations a preliminary vlev'.

' Confldeoce Intervals: an empiricaliovestigetion for the series in the K-

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'The role of public policy In insuringfinancial stability: • crass-country,cooperative perspective', August 1986, Revised

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erns as indicator of aanufacturing strategy',

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'On the 41[111t•GO of equilibrium in hottalling'•model', November 1986.

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•METAYORICASTING: Vays of improvingForecasting. Accuracy and Usefulness',

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•Managers' cognitive maps for upvard anddownward relationships', June 1987.

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policy regime. Hay 1987.

'A nev approach to statistical forecasting',

June 1987.

'Strategy formulation: the impact of national

culture', Revised: July 1987.

'Conflicting ideologies: structural and

motivational consequences', August Iwo.

'The demand for retail product, and thehousehold production 'model: nev views on

complemientarity and substitutability'.

87/07 Rolf BANE andGabriel NAVAVINI

87/08 Manfred KETS DE VRIES

87/09 Lister VICKERY,

Mark PILKINCTON

and Paul READ

87/10 Andre LAURENT

87/11 Robert FILDES and

ShYrosNAKEIDAXIS

87/12 Fernando BARTOLOMEand Andre LAURENT

87/13 Sumantra CHOSHALand Nitin NOIIRIA

07/14 Landis GABEL

87/15 Spyros KAKRIDAKIS

87/16 Susan SCHNEIDER

and Roger DUNBAR

87/17 Andre LAURENT and

Fernando BARTOLOME

87/10 Reinhard ANGEL/4AR and

Christoph LIEBSCHER

87/19 David BEGG and

Charles VTPLOSt

07/20 Spyros MAKAIDAXIS

87/21 Susan SCHNEIDER

87/22 Susan SCHNEIDER

87/23 Roger BETANCOURTDavid GAUTSCIII

'Spatial competition and the Core', August

1987. 1988

88/10 Bernard SINCLAIR-

DESCAGNé

87/40 88/11 Bernard SINCLAIR-DESCACNt

Carmen NATIVES andPierre RECIBEA0

'The robust t some standard auction game

torus*, February 1988.

'ifheo stationary strategies are equilibriumbidding strategy: The single-crossing

property • , February 1988.

87/39 Manfred KM'S DE VRIES 'The dark side of CEO succession", November

1987

'Product compatibility and the scope of entry•,November 1987

•The internal and external careers: atheoretical and cross-cultural perspective',Spring 1987.

•The robustness of KDS configurations In theface of incomplete data • , March 1987, Revised,July 1987.

"Deanna cosplementarities, household productionand retail assortments', July 1907.

'Is there • capital shortage In Europe?',August 1987.

'Controlling the Interest-rate risk of bonds:an introduction to duration analysts andImmunisation strategies', Sep t ember 1987.

•Interpreting strategic behavior: basicassumptions themes in organizations', September1987

'On the optimality of central places*,September 1987.

*German, French and British manufacturingstrategies less different than one thinks',September 1987.

*A process framework for analyzing cooperationbetveen firms • , September 1987.

•European manufacturers: the dangers ofcomplacency. Insights from the 1907 Europeanmanufacturing futures survey, October 1987.

*Privatisation: its motives and likely

consequences', October 1907.

•Strategy formulation: the impact of nationalculture • , October 1987.

87/41 cavriel HAVAVIN1 andClaude VIALLET

87/42 Nolen NEVER andJacques-P. THISSE

87/43 Jean GABSZEV1CZ andJacques- F.

87/44 Jonathan HAMILTON,Jacques-P. THISSEand Anita VESKANP

87/45 Karel COOL,David JEMISON andIngenar DTERICK1

87/46 Ingenar DIERICKIand Karel COOL

88/01 Michael LAVRENCE andSpyros KAKAIDAKIS

88/02 Spyros MAKRIDAKIS

88/03 James TEBOUL

88/04 Susan SCHNEIDER

88105 Charles VTPLOSZ

88/06 Reinhard ANSELMAR

88/07 Ingenar DIERICKX

and Karel COOL

88/08 Reinhard ANGELMAA

and Susan SCHNEIDER

88/09 Bernard SINCLAIR-DESGACNf

'Seasonality, sire premium and the relationshipbetween the risk end the return of Frenchcommon stocks', November 1987

'Combining horizontal and verticaldifferentiation: the principle of mma-mindifferentiation • , December 1987

'Location', December 1907

'Spatial discrimination: Bertrand vs. Cournotin • model of location choice • , December 1907

"Business strategy, ameket structure and risk-return relationships, • causal interpretation',December 1987.

'Asset stock accumulation and sustainabilltyof coapetitive advantage', December 1187.

'factors affecting judgemental forecasts and

confidence intervals', January 1908.

"Predicting recessions and other turningpoints", January 1988.

'De-Industrialize service for quality • , January

1988.

'National vs. corporate culture: Implicationsfor human resource management', January 1900.

"The swinging dollar: Is Europe out of step?".

January 1988.

• Les conflits dans les canal,* de dIstributine,

January 1988.

'Competitive advantage: a resource

perspective', January 1980.

'Issues in the study of organizational

cognition*, February 1989.

"Price formation and product design throughbidding', February 1908.

87/24 C.B. DERR andandr4 LAURENT

87/25 A. K. JAN,N. K. MALHOTRA andChristian PINSoN

87/26 Roger BETANCOURTand David GAUTSGHI

87/27 Michael BURDA

07/28 Gabriel HAVAVINI

87/29 Susan SCHNEIDER andPaul SHRIVASTAVA

87/30 Jonathan HAMILTONV. Bentley 'MCLEODand J. 7. THISSE

87/31 Martine OUINEII andJ. P. TIIISSE

87/32 Arnoud DE METER

87/31 Yves DOZ andAmy SHUN

82/34 Kasra FERDOVS andArnoud DE METER

87/37 Landis GABEL

87/38 Susan SCHNEIDER

87/35 P. J. LEDERER and •Competitive location on networks under

J. F. THISSE discriminatory pricing • , September 1987.

87/36 Manfred KETS DE VRIES "Prisoners of leadership', Revised version

October 1987.

88/12 Spyros MAKRIDAKIS

88/13 Manfred KETS DC VRIES

08/14 Alain NOEL

88/15 Anil DEOLALIKAA andLars-Hendrik ROLLER

88/16 Gabriel HAVAVINI

88/17 Michael BURDA

88/18 Michael BUROA

88/19 M.J. LAVRENCE andSpyros MAKAIDAKIS

88/20 Jean DERMINE,Damien NEVEM andJ.F. TIIISSE

88/21 James TEBOUL

88/22 Lars-Hendrik RULER

88/23 Sjur Didrik FLANand Georges ZACCOUR

88/24 B. Espen DCKBO andHervig LANCOHR

88/25 Everette S. GARDNERand Spyros MAKAIDAKIS

88/26 Sjur Didrik FIRMand Georges ZACCOUR

88/27 Murugapps KRISHNANLars-Hendrik ROLLER

88/28 Su•antra GAOSRAL andC. A. BARTLETT

' 8ustness firms and managers In the 21stcentury • , February 1988

'Alexithymis to organisational life: theorganisation tun revisited', February 1988.

' The interpretation of strategies: ■ study ofthe Impact of CEOs on the corporation',March 1988.

"The production of and returns from industrialInnovations an econometric analysis for •developing country • . December 1987.

' Market efficiency and equity pricing,international evidence and implications forglobal I int', March 1908.

' Monopolistic competition, coats of adjustmentand the behavior of guropean employment',September 1987.

'Reflections on Walt Unemployment* inEurope• , November 1987, revised February 1988.

' Individual bias In judgements of confidence',March 1988.

'Portfolio selection by mutual fund,. nnequilibrium andel'. March 1988.

"De-industrialite service (or quality".March 1988 (88/03 Revised).

"Proper Ouadratic Functions vith an Applicationto AT&T', May 1987 (Revised March 1988).

'BquIllbres de Mash-Cournot dans le marchfeuroplen du gal: man gag oil len solutions enboucle ouverte et en feedback coincident•,Mars 1988

' Inforiaation disclosure, 'Deans of payment, andtakeover pees's. Public and Private tenderoffer, in France', July 1985, Sixth revision,Apr11 1988.

' The future of forecasting', April 1988.

' Seal-competitive Cournot equilibrium inmultistage oligopolies • , April 1908.

Matey game vith resalable capacity',April 1988.

'The multinational corporation a, • network:pe-apectives from interorg•nizational theory',w. w toes

80/29 Haccah K. MALROTRA,Christian PINSON andArun K. JAIN

88/30 Catherine C. ECKELand Theo VERMAELEN

88/31 Sumantra GROSMAL andChristopher BARTLE1T

88/32 Kiwi FERDOVS andDavid SACKAIDER

88/33 Mihkel M. TOMBAK

88/34 Mihkel N. TOMBAK

88/35 Mihkel N. TOMBAK

88/36 Vikas TIBREVALA andBruce BUCHANAN

80/37

Nurw trappa KR1SRNANLars-Hendrik ROLLER

00/38 Manfred KETS DE VRIES

88/39 Manfred VETS DE VRIES

88/40 Josef LAXONISROK andTheo VERAAELEN

88/41

Charles VIPLOSZ

88/42 Paul EVANS

88/43 B. SINCLAIR-DESCAGNE

88/44 Essam MAHMOUD andSpyros KAKRIDAXIS

88/45 Robert KORAJCZYKAnd Claude VIALLFT

88/46 Yves DOZ andAmy SHUEN

'Consvwer cognitive coaplex1ty and thedimensionality of multidieensional scalingconfigurations', May 1988.

'The financial fallout from Chernobyl: riskperceptions and regulatory response'. May 1988.

'Creation. adoption, and diffusion ofinnovations by subsidiaries of multinationalcorporations', June 1988.

' International manufacturing: positioningplants for success', June 1988.

' The isportance of flexibility Inmanufacturing', June 1988.

' Flexibility: an important diatenxion inmanufacturing', June 1988.

' A strategic analysis of investment In flexiblemanufacturing systeas • , July 1988.

'A Predictive Test of the N110 Model thatControls for Non.stationarity% June 1988.

' Regulating Price-Liability Competition ToUprose Velfare', July 1988.

'The Motivating Role of Envy : A ForgottenFactor in management, April 88.

' The Leader as Mirror : Clinical Reflections',July 1908.

' Anomalous price behavior around repurchasetender offers', August 1988.

' Assymetry in the EMS, intentional orsystemic'', August 1988.

'Organizational development In thetransnational prise', June 1988.

'Croup decision support systems implementBayesian rationality'. September 1988.

'The state of the art and future directionsin combining forecastn • , September 1988.

' An empirical investigation of internationalasset pricing'. November 1986, revised August1988.

'Prom Intent to outcome: • process fraaevorkfor partnerships', August I980.

88/52 Susan SCHNEIDER andReinhard ANGELMAR

"Cognition and organizational analymiso vho'sminding the store", September 1988. 1989

88/47 Alain BULTEZ,Els CIJSBRECHTS,Philippe NAERT andPiet VANDEN ABEELE

88/48 Michael BURDA

88/49 Nathalie DIERKENS

88/50 Rob VEITZ andArnoud DE MEYER

88/51 Rob VEITZ

' Asymmetric cannibalism betveen substituteitems listed by retailers', September 1988.

"Reflections on 'Veit unemployment' inEurope. II", April 1988 revised September 1988.

"Information asymmetry and equity Issues',September 1988.

' Managing expert systems: from inceptionthrough updating', October 1987.

"Technology, mock, and the organization: thel.pact of expert system', July 1988.

88/63 Fernando NASCIMENTOand Viltried R.VANHONACKER

88/64 Kasra FERDOVS

88/65 Arnoud DE METERand Kasra FERDOVS

88/66 Nathalie DIERKENS

88/67 Paul S. ADLER andKasra FERDOVS

'Strategic pricing of differentiated consumerdurables in • dynamic duopoly: ■ numericalanalysis*. October 1988.

"Charting strategic roles for internationalfactories• , December 1988.

'Quality up, technology dove", October 1988.

"A discussion of exact measures of informationasymmetry: the example of Myers and NONEmodel or the importance of the asset structureof the firm', December 1988.

"The chief technology officer", December 1988.

88/53 Manfred KETS DE VRIES

88/54 Lars-Hendrik RULERand Mihkel M. TOMBAK

88/55 Peter BOSSAERTSand Pierre MILLION

88/56 Pierre MILLION

88/57 Vilfried VANHONACKERand Lydia PRICE

88/58 B. SINCLAIR-DESCAGNEand Mihkel M. TOMBAK

88/59 Martin KILDUFF

88/60 Michael BURDA

88/61 Lars-Hendrik RULER

88/62 Cynthia VAN HULLE,Theo VERNAELEN andPaul DE VOUTERS

'Whatever happened to the philosopher-king: theleader's addiction to pover, September 1988.

*Strategic choice of flexible productiontechnologies and welfare implications•,October 1988

•Method of moments teats of contingent claimsasset pricing models', October 1988.

"Size-sorted portfolios and the violation ofthe random valk hypothesis: Additionalempirical evidence and implication for testsof asset pricing models', June 1988.

'Data transferability: estimating the responseeffect of future events based on historicalanalogy", October 1988.

'Assessing economic inequality", November 1988.

"The interpersonal structure of decisionmaking: a social comparison approach toorganizational choice", November 1988.

"Is mismatch really the problem/ Some estimatesof the Chelvood Cate II model vith US data",September 1988.

'Modelling cost structure: the Bell Systemrevisited', November 1988.

' Regulation, taxes and the market for corporatecontrol in Belgium", September 1908.

"The impact of language theories on VSSdialog• . January 1989.

"DSS software selection, a multiple criteriadecision methodology". January 1989.

'Negotietiom support: the effects of computerinterventian and confliet level on bargainingoutcomes. January 1989.'Lasting improvement tomanuEsetorIngperformaace: Im search of • mew theory",January 1989.

'Shared history or shared culture? The effectsof time, culture, and perfernanee oninstitetionalization in simulatedorganisations*, January 1989.

°Coordiaating manufacturing and businessstrategies: February 1989.

'Structural adjustment fa Barone= retailtanking. Some view from industrialorganisation", January 1989.

'Trends in the development of technology andtheir effects on the production structure inthe European Community', January 1989.

"Brad proliferation and entry deterrence",February 1989.

'A market based approach to the valuation ofthe assets in place and the grovthopportunities of the firs', December 1988.

89/01 Joyce K. DIRER andTavfik JELASSI

89/02 Louts A. LE BLANCand Tavfik JELASSI

89/03 Beth 8. JONES andTavfik JELASSI

89/04 Kasra FERDOVS andArnoud DE METER

89/05 Martin KILDUFF andReinhard ANGEIJIAR

89/06 Mihkel N. TOMBAK andB. SINCLAIR-DESCAGNE

89/07 Damien J. NEVEN

89/08 Arnaud DE METER andRelimut SCRUM

89/09 Damien NEVEN.Carmen MATURES andMarcel CORSTJENS

89/10 Nathalie DIERKENS,Bruno GERARD andPierre BILLION

89/111 Manfred KETS DE VRIESend Alain NOEL

89/12 Untried VANUONACKFA

89/13 Manfred KETS DE TRIES

89/14 Reinhard ANGELMAR

89/15 Reinhard ANGELMA1

89/16 Vilified YAHOO/METER,Donald LEI:KANN andr SULTAN

89/17 Cilles APIADO,Claude FAUCNCUf andAndrd LAURENT

89/18 Scini IIALAK-RIS8MAN andMitchell KOLA

89/19 VilfrIed VANVONACKEA,Donald LERMANN and► SULTAN

69/20 Vilfried VAIDIONACKEAand Russell VIPER

89/21 Arnoud de METER andKau& FERMIS

89/22 Manfred KITS DC VRIESand Sydney PERZOV

69/21 Robert KORAJCZFK andClaude VIALLET

19/2a martin KILDUFF andMitchel AROLAFIA

09/25 Roger *ETAT:COURT andDavid GAUTSCHI

69/26 Charles SEAN,Edmond KALINVAUD,Peter BEINNOLZ,Francesco GIAMATTIand Charles VTPIOSZ

•Understanding the leader- tttttt a interface:applicAtios of the strategic relationshiplestroley netho • , rebruary 1989.

•Istlaettas dynamic response models otsen thedata are subject to differeet teApormlaggregation', January 1989.

•The Impostor syndrome: a disquietingpbeneratbon in organisational life". February1989.

'Product innovations • tool for coopetitiveadvantage', March 1989.

'Evaluating a firu's product innovationperformance*, March 1989.

'Coe:bluing related and sparse data in linearregression models*. February 1989.

• Changeeent organisationnel et rfallt4sculturelloss comtraates franco-meariceins..March 1989.

• Information asymmetry, market failure andjoint-ventures: theory and evidence',March 1989

•Combining related and sparse data in linearregressloo models••Revised March 1989

• A rational rondo. behavior model of choice'.Revised March 1989

• Imfluenee of nanufecturing improve...ear mores on performance'. April 1989

•Shot is the role of character inpsychoanalysts? April 1989

'Ilquity risk priests and the pricing of foreignexchange risk• April 1989

'The social destruction of reality:Organisational conflict es social duneApril 1969

•Too loll characteristics of retailmarkets and their economic consequences'March 1989

• Macroeconomic policies for 1992: thetransition and after', April 1989

89/27 David KRACKHARDT andMartin KILDUFF

89/28 Martin KILDUFF

89/29 Robert COCEL andJean-Claude LARRECHE

89/30 Ltrs-Hendrik ROLLERand Mihkel M. TOMBAK

89/31 Michael C. BURDA andStefan GERLACH

89/32 Peter HAUL andTavfik JELASSI

89/33 Bernard SINCLAIR-DESGAGNE

89/34 Sumantra GHOSHAL andNittin NORRIA

89/35 Jean DERMINE andPierre BILLION

89/36 Martin KILDUFF

89/37 Manfred KETS DE VRIES

89/38 Manfrd KETS DR VRIES

89/39 Robert KORAJCZTK andClaude VIALLET

89/40 Balaji CRAKRAVARTHT

89/41 B. SINCIAIR-DRSCAQIEand Nathalie DIERKENS

89/42 Robert ANSON andTavfik JELASSI

89/43 Michael RURDA

89/44 Balaji CBAKRAVARTHTand Peter LORANCE

'Friendship patterns and cultural attributions:the control of organizational diversity,April 1989

'The interpersonal structure of decisionmaking: • social comparison approach toorganizational choice• , Revised April 1989

•The battlefield for 1992: product strengthand geographic coverage • , May 1989

•Competition and Investment in FlexibleTechnologies', May 1989

' Durables and the US Trade Deficit', May 1989

'application end evaluation of a multi-criteriadecision support system for the dynamicselection of U.S. manufacturing locations•,May 1989

' Design flexibility in nonopsonisticindustries• , May 1989

'Requisite variety versus shared values:managing corporate-division relationships inthe M-Fore organisation• , May 1989

'Deposit rate ceilings and the market value ofhanks: The case of France 1971-1981 • , May 1989

'A dispositional approach to social netvorks:the case of organizational choice', Nay 1989

'The organisational fool: balancing • leader'shubris• , May 1989

"The C60 blues • , June 1989

' an empirical investigation of internationalasset pricing•, (Revised June 1989)

' Management systems for innovation andproductivity', June 1989

'The strategic supply of precisions', June 1989

"A development framework for computer-supportedconflict resolution', July 1989

'A note on firing costs and severance benefitsin equilibrium unemployment', June 1989

' Strategic adaptation in multi-business firms•,June 1989

89/45 Rob VEITZ and

"Managing expert systems: a framevork and easeArnoud DE METER study*, June 1989

89/46

89/47

Marcel CORSTJENS, 'Entry Encouragement', July 1989

Carmen MAIMS and

89/64(TM)

Enver YUCESAN andLee SCHRUBEN

"Complexity of simulation models: A graphtheoretic approach", November 1989

Damien NEVER89/65 Soumitra DUTTA and "MARS: A mergers and acquisitions reasoning

Manfred KETS DE VRIES 'The global dimension in leadership and

and Christine MEAD organization: issues and controversies',(TM,AC, PIN)

Piero BONISSONE system", November 1989

April 1989

89/48 Damien NEVEM andLars-Bendrik ROLLER

89/49 Jean DERMINE

89/50 Jean DERMINE

89/51 SpyrosMAKRIDAKIS

89/52 Arnoud DE METER

89/53 Spyros MAXRIDAKIS

89/54 S. BALAKRISHNAN

and Mitchell KOZA

89/55 H. SCHUTTE

89/56 Vilfried VANHONACKPRand Lydia PRICE

89/57 Taekvon KIM,

Lars-Bendrik ROLLERand Mihkel TOMBAK

89/58 Lars-Hendrik ROLLER(EP,TM) and Mihkel TOMBAK

89/59 Manfred KETS DE VRIES,

(08) Daphne ZEVADI,

Alain NOEL and

Mihkel TOMBAK

89/60 Enver YUCESAN and(TM) Lee SCHRUBEN

89/61 Susan SCBNEIDER and(All) Arnoud DE METER

89/62 Arnoud DE MEYER

(TM)

89/63 Enver TUCESAN and

(TM) Lee SCRRUBEN

•European integration and trade flows',

August 1989

'Home country control and mutual recognition',

July 1989

"The specialization of financial institutions,

the EEC model", August 1989

'Sliding simulation: a new approach to time

series forecasting', July 1989

'Shortening development cycle times: a

manufacturer's perspective', August 1989

'Sly combining works?', July 1989

'Organisation costs and ■ theory of joint

ventures', September 1989

'Euro-Japanese cooperation in information

technology', September 1989

'On the practical usefulness of ■eta-analysis

results', September 1989

'Market growth and the diffusion ofmultiproduct technologies', September 1989

'Strategic aspects of flexible productiontechnologies', October 1989

'Locus of control and entrepreneurship: a

three-country comparative study', October 1989

'Simulation graphs for design and analysis of

discrete event simulation models', October 1989

'Interpreting and responding to strategicissues: The impact of national culture',

October 1989

'Technology strategy and international R 6 D

operations', October 1989

'Equivalence of simulations: A graph theoretic

approach • , November 1989

"On the regulation of procurement bids",November 1989

"Market microstructure effects of government

intervention in the foreign exchange market",December 1989

89/66 B. SINCLAIR-DESGAGNE(TM,BP)

89/67 Peter BOSSAERTS and(PIN) Pierre BILLION

1990

90/01/TM B. SINCLAIR-DESGAGNE "Unavoidable Mechanisms", January 1990EP/AC

90/02/EP Michael BURDA "Monopolistic Competition, Costs ofAdjustment, and the Behaviour of EuropeanManufacturing Employment", January 1990

"Management of Communication in InternationalResearch and Development", January 1990

"The Transformation of the European FinancialServices Industry: From Fragmentation toIntegration", January 1990

"European Equity Markets: Toward 1992 andBeyond", January 1990

"Integration of European Equity Markets:Implications of Structural Change for KeyMarket Participants to and Beyond 1992",January 1990

"Stock Market Anomalies and the Pricing ofEquity on the Tokyo Stock Exchange", January1990

"Modelling vith MCDSS: What about Ethics?",January 1990

"Capital Controls and International TradeFinance", January 1990

"The Impact of Language Theories on MSDialog", January 1990

"An Overview of Frequency Domain Methodologyfor Simulation Sensitivity Analysis",January 1990

90/03/TM Arnoud DE MEYER

90/04/ Gabriel HAWAWINI andPIN/EP Eric RAJENDRA

90/05/ Gabriel HAWAWINI andFIN/EP Bertrand JACOUILLAT

90/06/ Gabriel HAWAWINI andFIN/EP Eric RAJENDRA

90/07 Gabriel HAWAWINIFIN/EP

90/08/ Tavfik JELASSI andTM/EP B. SINCLAIR-DESGAGNE

90/09/ Alberto GIOVANNINIEP/FIN and Jae VON PARK

90/10/TM Joyce BRIER andTavfik JELASSI

90/11/TM Enver YUCESAN

90/12/EP Michael BURDA

"Structural Change, Unemployment Benefits andHigh Unemployment: A U.S.-EuropeanComparison", January 1990