1 USC CS 541 AI Planning Lecture Notes Yolanda Gil Plan Representation and Reasoning with...

1USC CS 541 AI Planning Lecture Notes Yolanda Gil

Plan Representation and Reasoningwith Description Logics

and Ontologies

Yolanda Gil

Lecture Notes, October 4, 2000

CS 541 Artificial Intelligence Planning

www.isi.edu/~gil/cs541


Outline

Representing actions and plans with description logic Action taxonomies (CLASP) Plan taxonomies (SUDO-PLANNER) Goal taxonomies (EXPECT)

Planning ontologies Process Specification Language (PSL), NIST PLANET


Representing Knowledge in Description Logic (DL)

Description logics are extensions of frame-based systems where classes can be defined intensionally

Ex: SUVs are vehicles with 4 seats that weight between 1T and 2T

Class taxonomy is automatically generated through subsumption A subsumes B iff all instances of B are also instances of A

Instances can be automatically classified Ex: MyNewCar is a vehicle with 4 seats that weighs 1.3T => MyNewCar is an SUV

Relations can also have definitions and can be classified Tradeoff between expressivity and efficient reasoning Some well know description logic systems: CLASSIC,

LOOM, NIKL


Representing Planning Knowledge in Description Logics: Overview

Action taxonomies in CLASP extended language to represent action networks

Plan taxonomies in SUDO-PLANNER plan subsumption of partially ordered plans

Goal taxonomies in EXPECT expressive representations of goals and their

parameters

These systems can exploit the descriptions of all the objects in the domain (domain knowledge) in order to reason about action, goal, and plan descriptions


CLASP: CLAssification of Scenarios and Plans [Devanbu and Litman 94] Extension of a DL system (CLASSIC)

Language to express action networks– Sequence, loop, repeat, test, subplan

Subsumption and classification algorithms for that language– Action network subsumption viewed as DFA acceptance

Propositional, STRIPS-style representation of actions States (goals are represented as states) Actions Plans Scenarios (plan instances)

Reasoning based on these descriptions: Organizing plan classes Retrieving plan types Validation of scenarios


Core Classes in CLASP

(DEFINE-CONCEPT Action (PRIMITIVE (AND Classic-Thing

(AT-LEAST 1 Actor) (ALL ACTOR Agent) (EXACTLY 1 PRECONDITION) (ALL PRECONDITION State) (EXACTLY 1 ADD-LIST) (ALL ADD-LIST State) (EXACTLY 1 DELETE-LIST) (ALL DELETE-LIST State)

(EXACTLY 1 GOAL) (ALL GOAL STATE))))

(DEFINE-CONCEPT State (PRIMITIVE Classic-Thing)) (DEFINE-PLAN Plan (PRIMITIVE

(AND Clasp-Thing (EXACTLY 1 INITIAL) (ALL INITIAL State)

(EXACTLY 1 GOAL) (ALL GOAL State) (EXACTLY 1 PLAN-EXPRESSION) (ALL PLAN-EXPRESSION (LOOP Action)))))


Defining Actions, States and Plans in CLASP in a Telephony Domain

(DEFINE-CONCEPT System-Act (AND Action

(ALL ACTOR System-Agent)))

(DEFINE-CONCEPT Connect-Dialtone-Act (AND System-Act

(ALL PRECONDITION (AND Off-Hook-State Idle-State)) (All Add-LIST Dialtone-State) (ALL DELETE-LIST Idle-State (ALL GOAL (AND Off-Hook-State

Dialtone-State))))

(DEFINE-CONCEPT Callee-Off-Hook-State (PRIMITIVE State))(DEFINE-CONCEPT Callee-On-Hook-State (PRIMITIVE State))(DEFINE-CONCEPT Callee-Off-Caller-On-State (AND Callee-Off-Hook-State

Caller-On-Hook-State))

(DEFINE-PLAN Pots-Plan (AND Plan (ALL PLAN-EXPRESSION

(SEQUENCE (SUBPLAN Originate-And-Dial-Plan) (TEST (Callee-On-Hook-State

(SUBPLAN Terminate-Plan))(Callee-Off-Hook-State (SEQUENCE Non-Terminate-Act Caller-On-Hook-Act Disconnect Act)))))))

(DEFINE-PLAN Originate-And-Dial-Plan (AND Plan

(ALL PLAN-EXPRESSION(SEQUENCE Caller-Off-Hook-Act Connect-Dialtone-Act Dial-Digits-Act))))


Defining Instances in CLASP

(CREATE SCENARIO pots-busy-scenario (AND Plan

(FILLS INITIAL state-u1on-u2off) (FILLS GOAL state-u1on)

(FILLS PLAN-EXPRESSION (caller-off-hook-u1 connect-dialtone-on-u1 dial-digits-u1-to-u2 non-terminate-on-u2 caller-on-hook-u1 disconnect-u1))))

(CREATE-IND state-u1on-u2off (AND state-U1on State-U2off)) (CREATE-IND connect-dialtone-on-u1 (AND Connect-Dialtone-Act (FILLS ACTOR switching-system) (FILLS PRECONDITION state-u1off-idle)))


SUDO-PLANNER [Wellman 88]

Exploits subsumption to control the search during plan generation

Actions represented in DL (NIKL), organized in taxonomy

Plans represented as partially ordered sets of actions Eliminate search nodes whose plan is subsumed (dominated)

by other nodes SUDO-PLANNER had other features not discussed here:

Uncertainty reasoning and partial goal satisfaction Policy constraints that relate actions to external events Conditional effects Qualitative probabilistic networks


Action Taxonomy in SUDO-PLANNER

(defconcept surgery :is (:and action (:the route invasive-path-into-body)))

(defconcept biopsy :is-primitive action ...))

(defconcept open-lung-biopsy :is (:and biopsy (:the route open-lung-path)))

(defconcept open-lung-path :is (:and invasive-path-into-body ...))

System deduces that open-lung-biopsy is a surgery


Plan Representation and Subsumption in SUDO-PLANNER Plan is described as a set of action types

associated with identifiers [(surgery, id1) (CABG, id2)]

Plan is simplified if action subsumption and same id [(surgery, id1) (CABG, id1)] -> [(surgery, id1)]

Plan subsumption Action network viewed as bipartite graph matching

a1 a2 a5

a3 a4 a6

a1 a4 a5

a2 a3 a6


A* a1 A*

a2 A*

a2 b7 A*

A* a1 b5 A*

a1 b5 A*

...

XA* = {ai…aj} ai subsumes aj when i<j

Eliminating Redundant Paths in Plan Space Search Dominance-based planning:

Generate new nodes by adding constraints to search nodes Derive dominance (i.e., subsumption) based on domain

knowledge Eliminate nodes in the plan graph that are dominated by others


Reasoning about Goals in EXPECT [Swartout et al 98]

Highly declarative representation of goals Goals as verb-based expressions Rich language of goal parameter types

– Qualification parameters that describe the type of task– Intentional and extensional sets

Given a goal, matcher looks for methods (operators) that have the capability of achieving that goal can match variabilized goals can decompose goal into subgoals through reformulations

Goal representations have been used in several contexts: representing planning goals problem solving agent matchmaking


Representing Goals in EXPECT

Represented as a case grammar (verb + roles) ex: ESTIMATE OBJ duration OF trip

Roles can be filled by: a specific instance: add OBJ 3 TO 5 a concept: compute OBJ (spec-of factorial) OF 7 a type of instance: divide OBJ number BY 2 extensional sets: find OBJ (spec-of maximum) OF (54 15

256) intensional sets: add OBJ (set-of number) find OBJ (set-of (spec-of violated-

constraint)) IN configuration

Roles filled by concepts express task qualification parameters declaratively

(compute-factorial ?n) -> (compute (obj (spec-of factorial)) (of number)))


Matching Goals in EXPECT Desired goals and available capabilities are automatically translated to LOOM concepts Classifier is used to find most specific method capability that subsumes the posted goal

Self-organizing method taxonomy

movecargo

aircraft

OBJ

WITH

movecargo

truck

OBJ

WITH

movecargo

vehicle

OBJ

WITH

movecargo

C-140

OBJ

WITH

Goal: (move (OBJ (inst-of cargo)) (WITH C-140))

Method capability: (move (OBJ (inst-of cargo)) (WITH (inst-of aircraft)))


Flexible Matching through Goal Reformulation

When no capability matches a posted goal, but more specific versions of the goal match ex: no method to estimate round-trip time (rtt) of a vehicle,

but there are methods to estimate rtt of aircraft and trucks Use descriptive knowledge to reformulate goal

reexpress goal into subgoals by breaking down one of the arguments

recombine the results of solving subgoals Conjunctive (disjunctive) subgoals produce conjunctive

(disjunctive) reformulations Types of reformulations

Covering reformulation: subgoals cover partitions of a class Set reformulation: subgoals iterate over elements of a set Input reformulation: subgoals handle each of the subtypes


Find route from location1 to location2

Find egress route from Ryad to Kuwait city

Calculate RTTfor transport aircraft

Calculate round-trip time (RTT)for aircraft

Calculate RTTfor combat aircraft

A) Subsumption-based match: the posted goal is subsumed by a capability

B) Reformulation-based match: the posted goal can besatisfied by combining two or more existing capabilities

Find route from city1 to city2

Find route from location1 to location2

C) Reverse subsumption-based match: a capability can satisfy some aspect of the goal

Find addresses of US citizens in Kuwait

Find phone numbers of US citizens in Kuwait

D) Partial match: a capability is similar/related to the posted goal

Goal Matching in EXPECT


Overview of Planning Ontologies

Why planning ontologies knowledge reuse knowledge sharing knowledge modeling

Process descriptions in PSL temporal constraints resources

Describing plans in PLANET can represent state-based, plan-based search,

hierarchical plans captures plan representations understandable by people


Process Specification Language (PSL) [NIST, 99]

National Institute of Standards and Technology (NIST), Manufacturing Systems Division Academic and industrial collaborators

Proposed to Int’l Standards Organization (ISO) PSL core represents widely accepted

commitments activity, activity-occurrence, object, timepoint

PSL extensions accommodate possible shareable agreements

Contains axioms defining terms and constraints Available at http://www.mel.nist.gov/psl/


PSL Overview


PSL Modules for Activities and Orderings


PSL Core

Activity Generic activity: occurrences, interruptions,

nondeterministic, subactivities Ordering: ordering over activities, complex ordering

relations, junctions Objects

Resources: capacities, homogeneous sets, inventories, divisibility, usage, resource paths, pools, requirements, resource roles, substitutability

States: defined, constraints Timepoints

Duration theory, activity durations, temporal orderings


PLANET: a PLAN Semantic nET [Gil & Blythe 00]

Capture unifying views on planning algorithms constraints, commitments, task templates, alternative choices state-based and objective-based goals operator-based and HTN-based plans

Represent manually created plans typically include unintended flaws (incomplete, unjustified,

inconsistent) Capture planning context

initial constraints (guide, user advice, preferences) and restraints initial state, constraints and goals may be incompatible

Available from http://www.isi.edu/expect/projects/planet/


Some Terms Defined in PLANET

Planning problems Planning problem context: world state, desired goals,

external constraints Planning problem: candidate plans (rejected, feasible,

selected) Goals and effects

Goals: state-based goals, objective-based goals Human readable descriptions

Actions, operators, and tasks Plan task descriptions: plan task templates, plan tasks, Capabilities, preconditions, effects, subtasks, primitive tasks,

plan steps Plans

Commitments, sub-plans, planning level


PLANET: An Ontology for Representing Plans

justified

complete

consistent

feasible

plan-commitments

plan-refinements

sub-plans

capabilityeffectspreconditions

planning-level

human-readable description

sub-taskstask-of

Plan-task-templatePlan-task-templatetask-template

commitments

accomplishesordering

temporal

planning-problems

initial-stateworld-state

desired-goals

external constraints

candidate-plans

rejected

feasible

selected

unexplored

planning - level

state-based-goal-spec

objective-based-goal-spec

resource-neededamount

when-needed

Plan-task-descriptionPlan-task-description

Plan-taskPlan-task

PlanPlan

Planning-problem-contextPlanning-problem-context

Goal-specificationGoal-specification

Resource-requirements

Resource-requirements

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