cha2555 week2: knowledge representation, logic and planning

CHA2555 Week2:Knowledge Representation,Logic and Planning

Lee McCluskey

Email [email protected]

First term:http://scom.hud.ac.uk/scomtlm/cha2555/

mailto:[email protected]

Artform Research Group

Introduction

What is Knowledge Representation? Does a book represent knowledge? Does a database represent knowledge?

What are the requirements for representing knowledge? Separation of behaviour and knowledge –

implement ‘generic’ behaviours Maintaining knowledge needs to be easy -

add more knowledge and behaviour improves

Which ways are used to represent knowledge? Why do we want to represent knowledge? Can

we have AI without it?


Symbolic Knowledge Representation

Databases – represent sets of facts “Knowledge” tends to be represented using

“Logic”, where facts are considered as simple assertions

E.g. current law on marriage / gender etcmale(fred), married(fred,hilary) [FACTS]For all people x,y :

married(x,y) and male(x) => female(y)male(x) v female(y) not (male(x) and female(x))[LOGIC about people – properties and relations]

Encode “reasoning” into programs => hilary is female


Deeper example of Knowledge Representation: Representing an Action in “PDDL”

Example - moving a block from x to z

PDDL uses an “operator schema” or simply operator:

(:action move

:parameters (?x – block ?y – block ?z - block)

:precondition (and (clear ?x) (clear ?z)

(on ?x ?y) (not (= ?x ?z)))

:effect

(and (not (clear ?z)) (clear ?y)

(not (on ?x ?y)) (on ?x ?z)))


Implementing “AI” behaviour

Languages for implementing behaviours

Symbolic Conceptualisation and/or Model

Including knowledge representation

Application Domain

Prolog

C++

Java“Semantic difference”

LISPHaskell


Automated (AI) Planning

The scope of ‘AI Planning’ is the synthesis (generation) and execution of PLANS. That is, AI Planners reason with actions and generate plans.


Automated Planning i/o definition

Input: a Goal or Task, Current SituationProcess: Reasoning with [knowledge

representations] of Actions, Objects Goal, States, Constraints, Resources etc

Output: an ordering of specific actions [= plan] which achieves/solves/enacts the Goal or Task


Planning: Example Applications AUTOMATIC CONTROL: of industrial processes, traffic,

industrial robots, spacecraft, underwater robots Eg NASA’s Remote Agent Experiment CRISIS MANAGEMENT: in military applications,

accidents, evacuations, forest fire fighting USER SUPPORT: for project planning, web search, web

service composition SIMULATION: games software

AI Planning has to be cost effective - makes plans more quickly than people? more reliable

plans? more efficient plans?


r1

r3

r5

r7

r4

r2

bracelet

locket

ring

+Sword+Cudgel

Key-1

r6

p45

Key-2

troll

orc

p57

p25

p35

p24

p47

Sample Goal =Boromir kills TrollAragorn acquiresThe Ring

p56Aragorn

Boromir

+Knife

shield

spell

golem

lock-2

lock-1lock-1

p67

p12

8: MOVE_INTO_ROOM P67 BOROMIR R6 R7 9: MOVE_TO_PASSAGE P67 BOROMIR R7 10: EXCHANGE BOROMIR R7 P67 LOCKET KEY1 11: MOVE_INTO_ROOM P67 BOROMIR R7 R6 12: MOVE_TO_PASSAGE P56 BOROMIR R6 13: MOVE_INTO_ROOM P56 BOROMIR R6 R5 14: UNLOCK_PASSAGE BOROMIR R5 P25 KEY1 15: MOVE_TO_PASSAGE P25 ARAGORN R5 16: MOVE_INTO_ROOM P25 ARAGORN R5 R2 17: FIGHT_AND_KILL_MONSTER ARAGORN R2 P12 SHIELD SWORD ORC 18: MOVE_TO_PASSAGE P12 ARAGORN R2 19: MOVE_INTO_ROOM P12 ARAGORN R2 R1 20: PICK_UP_TREASURE ARAGORN R1 RING

0: MOVE_TO_PASSAGE P67 BOROMIR R7 1: MOVE_TO_PASSAGE P45 ARAGORN R4 2: MOVE_INTO_ROOM P45 ARAGORN R4 R5 3: GET_PROTECTION ARAGORN R5 SHIELD 4: MOVE_INTO_ROOM P67 BOROMIR R7 R6 5: KILL_SLEEPING_MONSTER BOROMIR R6 P56 KNIFE TROLL 6: PICK_UP_TREASURE BOROMIR R6 LOCKET 7: MOVE_TO_PASSAGE P67 BOROMIR R6

PLAN generated by AI is shown opposite(its not optimal!)


Usual (abstract) Architecture of Planners

DOMAIN

MODEL

PLANNINGENGINE

INTERFACE

Knowledge representation “Behaviour” generation


Simple Planning Domain Models Domain models typically contain

Types or classes of objects + Actions (represented by Operators) that change state of objects

A TASK associated with a domain model is defined by:

An INITIAL STATE + a GOAL CONDITION or STATE

(:action move :parameters (?x – block ?y – block ?z - block):precondition (and (clear ?x) (clear ?z)(on ?x ?y) (not (= ?x ?z))):effect (and (not (clear ?z)) (clear ?y) (not (on ?x ?y)) (on ?x ?z)))


Simple Planning Domain Models

Domain models typically contain1. Types or classes of objects

2. Actions (represented by Operators) that change state of objects

A TASK associated with a domain model is defined by:1. An INITIAL STATE

2. GOAL(S)


Environmental Assumptions In simple ‘applications’ we ASSUME: finite world (rather than infinite) deterministic actions (rather than non-

deterministic) completely observable (rather than partially

observable) instantaneous (rather than durative actions) unchanging, closed world (rather than open,

dynamic world) infinite resources


Environmental Assumptions The “FRAME PROBLEM” is a source of

concern in AI planning – the logic of stating what is not affected by an action

Often we assume an action satisfies the RULE of DEFAULT PERSISTENCE:

If an action specification does not specify that a fact / object changes

then it does not change .. things persist by default


PLANS in AI Planning

Are what planners generate.They are partial orders or sequences of instantiated operator

schemas.

They should be:

CORRECT - preconditions are satisfied

COMPLETE - achieve the final goal

OPTIMAL - no shorter or more efficient plan exists


Summary

Knowledge Representation – encoding knowledge so that it can be updated and used by reasoning processes

Planning is an important subarea of AI. Planning programs input “domain models” (often lists of operator schema), problem specifications (often initial states and goal conditions) and generate plans.

cha2555 week2: knowledge representation, logic and planning

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