Computer Science as Empirical Inquiry Computer Science as Empirical Inquiry : Symbols and Search : Symbols and Search

Allen Newell and Herbert A.Simon(1976)Allen Newell and Herbert A.Simon(1976)

Interdisciplinary Program in Cognitive Science Interdisciplinary Program in Cognitive Science

Lee Jung-WooLee Jung-Woo

March, 22, 1999March, 22, 1999

1. Introduction1. Introduction

• Computer Science is an empirical discipline.

– Each new machine and new program that are built are

experiments.

– It poses a question to nature, and its behavior offers clues to an

answer.

– As basic scientists we build machines and programs as a way of

discovering new phenomena and analyzing phenomena we already

know about.

2. Symbols and Physical Symbol System2. Symbols and Physical Symbol System

• 2.1 Laws of Qualitative Structure– All science characterize the essential nature of the systems they stu

dy. These characterizations are invariably qualitative in nature, for they set the terms within which more detailed knowledge can be developed.

– The Cell Doctrine in Biology / Plate Tectonics in Geology

– The Germ Theory of Disease / The Doctrine of Atomism

• 2.2 Physical Symbol Systems

• 2.3 Development of the Symbol System Hypothesis

• 2.4 The Evidence

2.2 Physical Symbol Systems(1)2.2 Physical Symbol Systems(1)

• Requirement for Intelligent Action– The ability to store and manipulate symbols

• Physical Symbol System– “Physical” : (1) obey the laws of physics(realizable by engineering)

(2) not restricted to human symbol systems

– Symbol(physical pattern), Expression(symbol structure), Process(creation,modification,reproduction,destruction)

– Designation : An expression designate an object or an process

– Interpretation : The system can interpret an expression

– Additional requirements

2.2 Physical Symbol Systems(2)2.2 Physical Symbol Systems(2)

• Physical Symbol System Hypothesis(PSSH)Physical Symbol System Hypothesis(PSSH)

– A physical symbol system has the necessary and sufficient A physical symbol system has the necessary and sufficient

means for general intelligent actionmeans for general intelligent action

• This is an empirical hypothesis.

– Scientifically, one can attack or defend it only by bringing forth

empirical evidence about the natural world.

• We need to trace the development of this hypothesis and

look at the evidence for it.

2.3 Development of the PSSH(1)2.3 Development of the PSSH(1)

• Formal Logic

– Program of Frege, Whitehead and Russell for formalizing logic

– Mathematical logic(propositional, first-order, and higher-order log

ics)

– “Symbol game” : Logic was a game played with meaningless toke

ns according to certain purely syntactic rules. All meaning had bee

n purged. One had a mechanical system about which various thing

s could be proved.

2.3 Development of the PSSH(2)2.3 Development of the PSSH(2)

• Turing Machines and Digital Computer

• The Stored Program Concept

• List Processing

• Lisp

2.4 The Evidence for PSSH(1)2.4 The Evidence for PSSH(1)

• The evidence for the hypothesis that physical symbol systems are capable of intelligent action, and that general intelligent action calls for a physical symbol system.– The evidence for the sufficiency of physical symbol systems for

producing intelligence(Attempt to construct and test specific systems that have such a capability) -- Artificial Intelligence

– The evidence for the necessity of having a physical symbol systems wherever intelligence is exhibited.(Attempt to discover whether Man’s cognitive activity can be explained as the working of a physical symbol system) -- Cognitive Psychology.

2.4 The Evidence for PSSH(2)2.4 The Evidence for PSSH(2)

• Constructing Intelligent Systems(A.I.)– Identify a task domain calling for intelligence, then construct a

program for a digital computer that can handle tasks in that domain

– Puzzles and games such as chess programs

– System that handle and understand natural language, systems for interpreting visual scenes, systems for hand-eye coordination, systems that design, systems that writhe computer programs, systems for speech understanding

– General Problem Solver(GPS), PLANNER, CONNIVER

– An initial burst of activity aimed at building intelligent programs for a wide variety of almost randomly selected tasks is giving way to more sharply targeted research aimed at understanding the common mechanisms of such systems.

2.4 The Evidence for PSSH(3)2.4 The Evidence for PSSH(3)

• The Modeling of Human Symbolic Behavior(Cognitive Psychology)– The search for explanations of man’s intelligent behavior in terms

of symbol systems has had a large measure of success to the point where information processing theory is the leading contemporary point of view in cognitive psychology.

– In the areas of problem solving, concept attainment, and long-term memory, symbol manipulation models now dominate the scene.

• Other Evidence – Negative evidence : the absence of specific competing hypotheses

as to how intelligent activity might be accomplished

– ex. Behaviorism and Gestalt theory

3. Heuristic Search3. Heuristic Search

• Question : “OK, so far. But how physical symbol systems accomplish such intelligent actions?”

• Answer : Symbol systems solve problems by using the processes of heuristic search

• Heuristic Search HypothesisHeuristic Search Hypothesis – The solution to problems are represented as symbol structures. The solution to problems are represented as symbol structures.

A physical symbol system exercises it intelligence in problem A physical symbol system exercises it intelligence in problem solving by search-that is, by generating and progressively solving by search-that is, by generating and progressively modifying symbol structures until it produces a solution modifying symbol structures until it produces a solution structurestructure.

3.1 Problem Solving(1)3.1 Problem Solving(1)

• Ability to solve problem is generally taken as a prime

indicator that a system has intelligence

• To state a problem is to designate (1) a test for a class of

symbol structures(solutions of the problem) and (2) a

generator of symbol structures(potential solutions).

• To solve a problem is to generate a structure, using (2),

that satisfies the test of (1)

3.1 Problem Solving(2)3.1 Problem Solving(2)

• The physical symbol systems can represent problem spaces and possess move generators.– Problem space : a space of symbol structures in which problem

situations, including the initial and goal situations, can be represented.

– Move generator : the processes for modifying one situation in the problem space into another.

• The physical symbol systems’ task, when it is presented with a problem and a problem space, is to use its limited processing resources to generate possible solution, one after another, until it finds one that satisfies the problem-defining test.

3.2 Search in Problem Solving(1)3.2 Search in Problem Solving(1)

• The study of problem solving was almost synonymous with

the study of search processes

• Extracting Information from the Problem Space

– A condition for the appearance of intelligence is that the space of

symbol structures exhibit at least some degree of order and pattern.

– Pattern in the space of symbol structures be more or less detectable

– The generator of potential solutions be able to behave differentially,

depending on what pattern it detected.

– Ex) AX+B = CX+D --> X = E

3.2 Search in Problem Solving(2)3.2 Search in Problem Solving(2)

• Search Trees

– Programs that play chess VS. Strongest human players

– Search is a fundamental aspect of a symbol system’s exercise of

intelligence in problem solving but that amount of search is not a

measure of the amount of intelligence being exhibited.

– When the symbolic systems that is endeavoring to solve a problem

knows enough what to do, it simply proceeds directly towards its

goal.

3.2 Search in Problem Solving(3)3.2 Search in Problem Solving(3)

• The Forms of Intelligence

– An intelligent system generally needs to supplement the selectivity

of its solution generator with other information-using techniques to

guide search, that is, to generate only structures that show promise

of being solutions or of being along the path toward solutions.

– In serial heuristic search, the basic question always is : “What shall

be done next?”

– That question has two components : (1) from what node in the tree

shall we search next, and (2) what direction shall we take from that

node?

3.2 Search in Problem Solving(4)3.2 Search in Problem Solving(4)

• A Summary of the Experience

– First conclusion : from what has been learned about human expert

performance in tasks like chess, it is likely that any system capable

of matching that performance will have to have access, in its

memories, to very large stores of semantic information.

– Second conclusion : some part of the human superiority in tasks

with a large perceptual component can be attributed to the special-

purpose built-in parallel processing structure of the human eye and

ear.

3.3 Intelligence Without Much Search(1)3.3 Intelligence Without Much Search(1)

• Our analysis of intelligence equated it with ability to extract and use information about the structure of the problem space, so as to enable a problem solution to be generated as quickly and directly as possible

• Nonlocal Use of Information– Information gathered in the course of tree search was usually only

used locally, to help make decisions at the specific node.

– In recent years, a few exploratory efforts have been made to transport information from its context of origin to other appropriate contexts.

– Berliner(1975) : use causal analysis to determine the range over which a particular piece of information is valid.

3.3 Intelligence Without Much Search(2)3.3 Intelligence Without Much Search(2)

• Semantic Recognition Systems– A second active possibility for raising intelligence is to supply the

symbol system with a rich body of semantic information about the task domain it is dealing with.

– What is new is the realization of the number of patterns and associated information that may have to be stored for master-level play.

– A particular, and especially a rare, pattern can contain an enormous amount of information, provided that it is closely linked to the structure of the problem space.

3.3 Intelligence Without Much Search(3)3.3 Intelligence Without Much Search(3)

• Selecting Appropriate Representations– A third line of inquiry is concerned with the possibility that search

can be reduced or avoided by selecting an appropriate problem space.

4. Conclusion(1)4. Conclusion(1)

• Physical Symbol Systems– Intelligence resides in physical symbol systems. This is computer

science’s most basic law of qualitative structure.

– Symbol systems are collections of patterns and processes, the latter being capable of producing, destroying and modifying the former.

– The most important properties of patterns is that they can designate objects, processes, or other patterns, and that, when they designate processes, they can be interpreted.

– Interpretation means carrying out the designated process.

– Symbolic system : Formal logic, The Turing machine, The stored-program concept, List processing

4. Conclusion(2)4. Conclusion(2)

• Heuristic Search

– A second law of qualitative structure for AI is that symbol systems

solve problems by generating potential solutions and testing them,

that is, by searching.

– Since they have finite resources, the search cannot be carried out

all at once, but must be sequential.

– They exercise intelligence by extracting information from a

problem domain and using that information to guide their search,

avoiding wrong turns and circuitous bypaths.

PostscriptPostscript

• There remain intellectual positions that stand outside the e

ntire computational view and regard the hypothesis as und

oubtedly false(Dreyfus 1979, Searle 1980)

– Philosopher : The central problem of semantics or intentionality-h

ow symbols signify their external referents-is not addressed by ph

ysical symbol systems.

– Connectionists : There are forms of processing organization that w

ill accomplish all that symbol systems do, but in which symbols wi

ll not be identifiable entities.