Ontologies Reasoning Components Agents Simulations Knowledge-Based Agents Jacques Robin.

OntologiesReasoningComponentsAgentsSimulations

Knowledge-Based AgentsKnowledge-Based Agents

Jacques Robin

Introductory Example:Introductory Example:Is West criminal?Is West criminal?

Is West criminal? “US law stipulates that it is a crime for a US citizen to sell weapons

to a hostile country. Nono owns missiles, all of them it bought from Captain West, an american citizen”

How to solve this simple classification problem?

Using a knowledge-based agent: Identify knowledge about the decision domain Represent it using a formal language which it is possible to

perform automated reasoning Implement (or reuse) an inference engine that perform such

reasoning

Knowledge-Based AgentsKnowledge-Based Agents

Ask

Tell

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Effectors

Domain-Specific

KnowledgeBase

GenericDomain-

IndependentInference

Engine

Knowledge Representationand Acquisition

Automated

Reasoning

What is Knowledge?What is Knowledge?

Data, information or abstraction formatted in a way that allows a human or machine to reason with it, deriving from it new data, information or abstraction, ex: Classes and objects Logical formulas Prior and conditional probability distributions over a set of random variables

Q: What is reasoning? A: Systematic mechanism to infer or derive new knowledge from new

percepts and/or prior knowledge, ex: Inheritance of attributes from classes its sub-classes and objects Classical First-Order Logic (CFOL) theorem proving using refutation,

resolution and unification Computing posterior probability calculus from prior and conditional ones

using Bayes theorem

Example KBA: Logic-Based AgentExample KBA: Logic-Based Agent

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Actuators

KnowledgeBase B:

Domain Modelin Logic L

InferenceEngine:

TheoremProver

for Logic L

Given B as axiom, formula f is a theorem of L?B |=L f ?

Sensors

Example of Automated Reasoning Example of Automated Reasoning with Knowledge Base: Is West with Knowledge Base: Is West

Criminal?Criminal? From the knowledge:

1. It is crime for a US citizen to sell weapons to a hostile nation

2. Nono own missiles3. Nono bought all its missiles from

Captain West4. West is a US Citizen5. Nono is a nation6. Nono is an enemy of the USA7 . A missile is a weapon8. Enmity is the highest form of

hostilty9. The USA is a nation

Can we infer the knowledge Q:

0. West is a criminal?

Representing this knowledge in CFOL KB:

( P,W,N american(P) weapon(W) nation(N) hostile(N) sells(P,N,W) criminal(P)) //1

( W owns(nono,W) missile(W)) //2 ( W owns(nono,W) missile(W)

sells(west,nono,W)) //3 american(west) //4 nation(nono) //5 enemy(nono,america) //6 W missile(W) weapon(W) //7 N enemy(N,america) hostile(N) //8

nation(america) //9

A CFOL theorem prover can be usedto answer the query:

KB |= criminoso(west) //0

Review of CFOL: General SyntaxReview of CFOL: General Syntax

FCFOLFormula

Arg1..2

FCFOLAtomicFormula FCFOLTerm

Arg1..2

QuantifierExpression

Quantifier: enum{,}

*

FOLVariable

Arg1..*

Example formula: X,Y (p(f(X),Y) q(g(a,b))) ((U,V Z ((X = a) r(Z)) (U = h(V,Z)))))

PredicateSymbol

Functor

FCFOLNonFunctionalTerm

ConstantSymbol

FCFOLFunctionalTerm

Arg1..*

FunctionSymbol

Functor

FCLUnaryConnective

Connective: enum{}

FCLBinaryConnective

Connective: enum{, , , }

FCLConnectiveFunctor

Review of CFOL:Review of CFOL:Implicative Normal Form (INF)Implicative Normal Form (INF)

Implicative normal form: Conjunction of implications

from atom conjunctions to atom disjunctions with implicit, universal only quantifiers

For any CFOL formular there is an equivalent INF formula

Skolemization: Substitute each existentially

quantified variable by a new, distinct constant

ex, x míssil(x) by míssil(m1)

ex, Y,Z x míssil(x) by míssil(m1,Y,Z)

INFCFOLFormula

Functor =

INFCFOLClause

Functor = *

Example INF formula: ((p(f(X),Y) q(g(a,b)) c) ((X = a) r(Z))) ((p(U,V) q(a,U)) (d e p(c,f(V)))

INFCLPLHS

Functor =

Premisse

INFCLPRHS

Functor =

Conclusion

*

*

FCFOLAtomicFormula

PredicateSymbol

FCFOLTermArg1..* FCFOLFunctionalTerm

FCFOLNonFunctionalTermArg1..*

FunctionSymbol

ConstantSymbolFOLVariable

Functor

Functor

*

Review of CFOL: Term UnificationReview of CFOL: Term Unification

p

a X

p

Y b

p

a X

p

Y f

c Z

X/f(c,Z)

Y/a

p

a f

c Z

p

a b

X/b

Y/a

p

a X

p

X b

fail

X/b

X/a

p

a X

p

Y f

c Z

p

a f

c d

X/f(c,d)

Y/a

Z/d

p

a X

Xfail

X/p(a,X)

Failure by Occur-Check

p

a X

X X/p(a,X) p

a p

a pGuarantees termination

Review of CFOL:Review of CFOL:Refutation and Resolution Refutation and Resolution

Refutation: Proving KB |= Q is equivalent to proving (KB Q) |= true,

itself equivalent to proving (KB Q) |= false Why? (KB Q) (KB Q) (KB Q) (KB Q)

Resolution: Binary Propositional case: ((A B) (B C)) (A C)

A B and B C resolve in A C Binary First-Order Case:

(A B) (C D) (B) = (C) ((A) (D))where is a set of variable substitutions that unify B with CA B and C D resolve in (A) (D) through the unification of B and C with

N-ary First-Order Case: if (Pi) = (Dj) then (P1 ... Pn C1 ... Cm) and (Q1 ... Qk D1 ... Dl) resolve in (P1) ... (Pi-1) (Pi+1) ... (Pn) (Q1) ... (Qk)) ((C1) ... (Cm) (D1) ... (Dj-1) (Dj+1) ... (Dn))


Criminal?Criminal? Refutation proof: showing that

following KB’ = KB Q

( P,W,N american(P) weapon(W) nation(N) hostile(N) sells(P,N,W) criminal(P)) //1

( W owns(nono,W) missile(W)) //2 ( W owns(nono,W) missile(W)

sells(west,nono,W)) //3 american(west) //4 nation(nono) //5

enemy(nono,america) //6

W missile(W) weapon(W) //7 N enemy(N,america) hostile(N) //8 nation(america) //9 criminoso(west) //0

is inconsistent, i.e., that false can be derived from it

Step1: generate the implicative normal form KB’’ or KB’

(american(P) weapon(W) nation(N) hostile(N) sells(P,N,W) criminal(P)) // 1

T owns(nono,m1) // skolemização 2a T missile(m1) // 2b (owns(nono,W) missile(W)

sells(west,nono,W)) //3 T american(west) //4 T nation(nono) //5 T enemy(nono,america) //6 missile(W) weapon(W) //7 enemy(N,america) hostile(N) //8

T nation(america) //9

criminoso(west) F //0


Criminal?Criminal? Step 2: repeatedly apply resolution rule to pair of clauses (A,B) where A’s premise unifies with B’s conclusion

(american(P) weapon(W) nation(N) hostile(N) sells(P,N,W) criminal(P)) //1 (T owns(nono,m1)) //2a (T missile(m1)) //2b (owns(nono,W) missile(W) sells(west,nono,W)) //3

(T american(west)) //4 (T nation(nono)) //5 (T enemy(nono,america)) //6 (missile(W) weapon(W)) //7 (enemy(N,america) hostile(N)) //8

(T nation(america)) //9 (criminal(west) F) //0

1. Resolve 0 with 1 unifying P/west:american(west) weapon(W) nation(N) hostile(N) sells(west,N,W) F //102. Resolve 10 with 4:weapon(W) nation(N) hostile(N) sells(west,N,W) F //113. Resolve 11 with 7: missile(W) nation(N) hostile(N) sells(west,N,W) F //124. Resolve 12 with 2b unifying W/m1:nation(N) hostile(N) sells(west,N,m1) F //135. Resolve 13 with 5 unifying N/nono:hostile(nono) sells(west,nono,m1) F //146. Resolve 14 with 8 unifying N/nono:enemy(nono,america) sells(west,nono,m1)

F //157. Resolve 15 with 6:sells(west,nono,m1) F //168. Resolve 16 with 3 unifying W/m1:owns(nono,m1) missile(m1) F //179. Resolve 17 with 2a:missile(m1) F //18

10. Resolve 18 with 2b: F

Dimensions of Knowledge Dimensions of Knowledge ClassificationClassification

Knowledge in a KBA can be characterized along the following (largely orthogonal) categorization dimensions: Intentional x Extensional Persistent x Volatile Structural x Behavioral Diagnostic x Causal Synchronous x Diachronous Certain x Uncertain Explicit x Implicit Precise x VaguePrecise x Vague Declarative x ProceduralDeclarative x Procedural Common Sense x ExpertCommon Sense x Expert Domain-Level x Meta-LevelDomain-Level x Meta-Level

Intentional x Extensional KnowledgeIntentional x Extensional Knowledge

Intentional knowledge: : about classes of entities and their generic relationships Domain concept hierarchy: ex, X, wumpus(X) monster(X). Domain integrity constraints: ex, X,Y wumpus(X) wumpus(Y)

X = Y. Domain behavior laws: ex, X,Y smelly(X,Y)

(loc(wumpus,X+1,Y) loc(wumpus,X-1,Y) loc(wumpus,X,Y+1) loc(wumpus,X,Y-1).

Database schema Object-Oriented Programming (OOP)

classes Universally quantified CFOL

formulas Document Schema (XML schema)

ExtensionalExtensional knowledge: about knowledge: about specific entity instances and their specific entity instances and their particular relationshipsparticular relationships Facts, propositions about concept

instances ex, loc(wumpus,2,1)

loc(wumpus,1,2) loc(wumpus,2,3) alive(wumpus,4)

ex, alive(wumpus,7).

Data (databases) Examples (machine learning) Cases (case-base reasoning) OOPobjects Ground CFOL formulas Classical Propositional Logic (CPL)

formula Document (XML)

Persistent x Volatile KnowledgePersistent x Volatile Knowledge

Persistent Knowledge: Valid during the lifetime of the agent across several task that it

carries out ≈ a program ex, X,Y,T smelly(X,Y,T) (loc(wumpus,X+1,Y,T) loc(wumpus,X-

1,Y,T) loc(wumpus,X,Y+1,T) loc(wumpus,X,Y-1,T)

Generally but not necessarily intentional

Volatile Knowledge: Temporary, buffer knowledge, valid only during the execution

context of one particular task of the agent lifetime ≈ data ex, loc(wumpus,2,1,T4) loc(wumpus,1,2,T4) loc(wumpus,2,3,T4)

alive(wumpus,4,T4) ex, alive(wumpus,T7). Generally but not necessarily extensional

Structural x Behavioral KnowledgeStructural x Behavioral Knowledge

Structural knowledge: Specifies the properties, relations and types of domain entities Key part are a generalization taxonomy and integrity constraints ex, M, wumpus(M) monster(M).

M,T monster(M) alive(M,T) dangerous(M,T). M,X,Y,T dangerous(M,T) loc(M,X,Y,T) safe(X,Y,T).

Behavioral knowledge: Specifies the state changes of domain entities, the event they

participates to, the actions they perform ex , X,Y,T loc(agent,X,Y,T) orientation(0,T) forward(T)

loc(wall,X,Y+1) loc(agent,X,Y+1,T+1).

Causal x Diagnostic KnowledgeCausal x Diagnostic Knowledge

Causal knowledge: Predictive model from cause to effect ex, X,Y,T loc(agent,X,Y,T) orientation(0,T) forward(T)


Diagnostic knowledge: Hypothesis forming model from observed effects to plausible

causes ex, X,Y,T smell(stench,X,Y,T) smelly(X,Y).

X,Y smelly(X,Y) (loc(wumpus,X+1,Y) loc(wumpus,X-1,Y) loc(wumpus,X,Y+1) loc(wumpus,X,Y-1)).

Synchronous x Diachronous Synchronous x Diachronous KnowledgeKnowledge

Diachronous knowledge: Describes the relation between the value of a given property of the

environment before the occurrence of an event, with the value of that same property after the occurrence of that event

Links an event occurrence with its pre and post-conditions ex, X,Y,T loc(agent,X,Y,T) orientation(0,T) forward(T)


Synchronous knowledge: Describes the relation between the value of two distinct properties

of the environment that hold at the same time Domain event invariants ex, M,X,Y,T dangerous(M,T) loc(M,X,Y,T) safe(X,Y,T).

Certain x Uncertain KnowledgeCertain x Uncertain Knowledge

Certain knowledge: Statement epistemologically guaranteed true or false ex, X,Y smelly(X,Y) smelly(X+1,Y-1) smelly(X-1,Y-1)

loc(wumpus,X,Y+1). Uncertain knowledge:

Statement which truth value is uncertain The truth of the statement is merely possible, plausible or probable ex, X,Y smelly(X,Y,1) (loc(wumpus,X+1,Y) loc(wumpus,X-1,Y)

loc(wumpus,X,Y+1) loc(wumpus,X,Y-1)). ex, X,Y □smelly(X,Y,1) (◊loc(wumpus,X+1,Y) ◊loc(wumpus,X-1,Y)

◊loc(wumpus,X,Y+1) ◊loc(wumpus,X,Y-1)). ex, X,Y,U,V smelly(X,Y,1) (U 1 X+1) (U 1 X-1) (V 1 Y+1) (V 1 Y-1)

loc(wumpus,X+1,Y,P1) loc(wumpus,X-1,Y,P2) loc(wumpus,X,Y+1,P3) loc(wumpus,X,Y-1,P4) loc(wumpus,X+1,Y,P5) loc(wumpus,X-1,Y,P6) loc(wumpus,X,Y+1,P7) loc(wumpus,X,Y-1,P8)) loc(wumpus,U,V,P9) loc(wumpus,U,V,P10) P1 = P2 = P3 = P4 = P10 P5 = P6 = P7 = P8 = P9.

ex, X,Y p(loc(wumpus,X+1,Y) | smelly(X,Y,1)) = 0.25 p(loc(wumpus,X-1,Y) | smelly(X,Y,1)) = 0.25 p(loc(wumpus,X,Y+1) | smelly(X,Y,1)) = 0.25 p(loc(wumpus,X,Y-1) | smelly(X,Y,1)) = 0.25.

Precise x Vague KnowledgePrecise x Vague Knowledge

Precise (or crisp) knowledge: size(wumpus, 2.80), loc(wumpus) = (X,Y)

Vague (or soft) knowledge: tall(wumpus), loc(wumpus) = around(X,Y)

Fuzzy approach to vague knowledge: Class membership function of entities map to [0,1] instead of {true,false} Class membership statements are atomic formula of fuzzy logic Connective semantics generally defined by:

fuzzyValue(a b) = min(fuzzyValue(a),fuzzyValue(b)) fuzzyValue(a b) = max(fuzzyValue(a),fuzzyValue(b)) fuzzyValue(a) = 1 – fuzzyValue(a)

Example: Given: fuzzyValue(tall(wumpus)) = 0.6 fuzzyValue(heavy(wumpus)) = 0.4, derive: fuzzyValue(tall(wumpus) heavy(wumpus)) = 0.4 but also derive: fuzzyValue(tall(wumpus) tall(wumpus)) = 0.4

Debate still raging on: Whether vagueness and uncertainty are orthogonal characteristics or two

aspects of the same coin Fuzzy sets and logic have any inherent advantage to represent either

Explicit x Implicit KnowledgeExplicit x Implicit Knowledge

Explicit knowledge: Sentences in the KB

Implicit knowledge: Axioms, simplifying assumptions, integrity constraints,

commitments which are not encoded explicitly as sentences in the KB but which must hold for the KB to be a correct model of the environment

Should be at least present as comments in the KB or in an external documentation, but is often present only in the KB designer’s head

Turn the KB simpler, more computationally efficient, concise and easy to understand (for one with knowledge of the implicit assumptions), but also far less extensible and reusable.

Implicit x Explicit Knowledge: Implicit x Explicit Knowledge: Illustrative ExampleIllustrative Example

The Wumpus World agent KB sentence (explicit knowledge): see(glitter) pick.

Is correct only under the following simplifying assumptions (implicit knowledge): There is only one agent in the environment See is a percept Pick is an action The scope of the see percept is limited to the cavern where the

agent is correctly located The gold is the sole glittering object in the environment The gold is the sole object to be picked in the environment The gold is a treasure A treasure an object worth picking

Implicit x Explicit Knowledge: Implicit x Explicit Knowledge: Illustrative ExampleIllustrative Example

Without these implicit assumptions, the same piece of behavioral knowledge must be represented by the far more complex sentence: (A,C,T,X,Y agent(A) loc(C,[(X,Y)]) time(T) in(A,C,T)

horizCoord(X) verticCoord(Y) percept(A,C,T,vision,glitter) O physObj(O) emit(O,glitter) in(O,C,T)) (O physObj(O) emit(O,glitter) ouro(O)) (O ouro(O) treasure(O))

(A,C,T,X,Y,O agent(A) loc(C,[(X,Y)]) time(T) in(A,C,T) horizCoord(X) verticCoord(Y) in(O,C,T) treasure(O) chooseAction(A,T+1,pick(O))).

This sentence is reusable in more sophisticated versions of the Wumpus World with multiple agents, multi-cavern vision scope, and multiple treasure objects to be picked that are observable through a variety of sensors.

Declarative x Procedural KnowledgeDeclarative x Procedural Knowledge

Declarative knowledge: Sentences (data structures) merely declaring what is true, known

or believed Declarative KB: modular, unordered set of largely independent

sentences which semantics is defined independently of any specific control structure

Combined at run time to carry out a task by the generic control structure of an inference engine

Rules, logical formulas, classes, relations Procedural knowledge:

Algorithmic, step-by-step specification of how to carry out a specific task

Procedures, functions, workflows Sub-steps combined and ordered at design time by the knowledge

engineer Integrate data and control structure

Common Sense x Expert KnowledgeCommon Sense x Expert Knowledge

Common Sense Knowledge: Recurrent across domains and tasks Decomposable into orthogonal aspects of the world, ex, space, time,

naive physics, folks psychology, etc. Shared by all humans, acquired instinctively by everyday life

experience ex, event calculus axioms about persistence of environment state

changes following occurrences of events FFluents, T2Time

(holds(F,T2) (EEvents, TTimes, happens(E,T) initiates(E,F) (T T2) clipped(F,T,T2)) (clipped(F,T,T2) (EEvents, T1Times, happens(E,T1) terminates(E,F,T1) (T T1) (T1 T2)

Expert Knowledge: Specialized for particular domain and task Possess only by a few experts, acquired through specialized higher

education and professional experience ex, X,Y smelly(X,Y,1) (loc(wumpus,X+1,Y) loc(wumpus,X-1,Y)

loc(wumpus,X,Y+1) loc(wumpus,X,Y-1)).

Domain-Level x Meta-Level Domain-Level x Meta-Level KnowledgeKnowledge

Domain-level knowledge: Knowledge modeling the agent’s environment and used by it

reason and take autonomous decisions Meta-level knowledge:

Knowledge about domain-knowledge level Explicitly Describes:

Its structure (reuse meta-knowledge) Its assumptions and limitations (reuse meta-knowledge) How reason with it efficiently (control meta-knowledge) How to explain inferences made with it (user-interface meta-knowledge) How to augment and improve it (learning meta-knowledge)

Roadmap of Automated Reasoning Roadmap of Automated Reasoning (AR)(AR)

Propositional

First-OrderRelational

First-Order OO

High-OrderRelational

High-Order OO

Knowledge Representation LanguageOntological Commitment

Reasoning Task

DeductionAbduction BeliefRevision

BeliefUpdate

ConstraintSolving

OptimizationInductionAnalogyInheritance Planning

Knowledge Representation LanguageEpistemological Commitment

Boolean Logic OWA

Ternary Logic CWA

Ternary Logic OWA

Probabilistic

Boolean Logic CWA

Possibilistic

Plausibilistic

Fuzzy ?

Inference engine characterizedby a volume in this 3D Space:• can execute a subset of the reasoning tasks• with knowledge encoded in a language, which semantics implies certain ontological and epistemological commitments

Dimensions of AR ServicesDimensions of AR Services

Reasoning Task

DeductionAbduction BeliefRevision

BeliefUpdate

ConstraintSolving

OptimizationInductionAnalogyInheritance

From: X,Y p(X,a) q(b,Y) r(X,Y) p(1,a) q(b,2)Deduce: r(1,2)

From: X,Y p(X,a) q(b,Y) r(X,Y) p(X,c) n(Y) r(X,Y) p(1,a) r(1,2) p(1,c)w/ bias: q(A,B)Abduce: q(b,2)

From: I I instanceOf g p(I) S S subclassOf g p(S) s subclassOf g s1 instanceOf sInherit: p(s1)

From: ~X p(X) r(X) X (q(X) n(X) (r(X) n(X)) p(a))Believe by default: r(a)But from new fact: q(a)Revise belief r(a) into n(a)

From: A si(A) do(A,k) sj(A) p(A) si(A) p(a)Initially believe: si(a)But after executing do(a,k)Update belief si(a) into sj(a)

Solve: X,Y,Z N X+Y=Z 1X XZ XY YZ Z 7Into: X=2 3Y Y 4 5 Z Z6 X+Y=Zor (X=2 Y=3 Z=5) (X=2 Y=4 Z=6)

From: X,Y,Z N X+Y=Z 1X XZ XY YZ Z 7w/ utility(X,Y,Z) = X + ZDerive optimum: X=2 Y=4 Z=6

From: p(1,a) q(b,2) r(1,2) p(1,c) n(2) ... p(3,a) q(b,4) r(3,4) p(3,c) n(4)w/ bias: F(A,B) G(C,D) H(A,D)Induce: ~X,Y p(X,a) q(b,Y) r(X,Y)

From: a ~1 b a ~2 c a ~11 d p(b,x) p(c,x) p(d,y)Derive by analogy: p(a,x)

Planning

From: A sa(A) do(A,i) sb(A) ... sh(A) do(A,k) sg(A) sa(a) goal(a) = sg(a)Plan to execute: [do(a,i), ... , do(a,k)]

Epistemological CommitmentEpistemological Commitment

Open-World Assumption (OWA): f KB xor f KB1. ask(q) = true iff KB |= q2. ask(q) = false iff KB |= q3. ask(q) = unknown iff

(KB | q) (KB | q), when agent does not know enough to conclude

Logically sound w/ Boolean logic, requires agent to

always possess enough knowledge to derive truth of any query

Closed-World Assumption (CWA): f KB (only positive facts) From: KB | q Assume: q is false (under naf and

not semantics) Not logically sound Negation As Failure (NAF)

connective: naf f = true iff KB | f If KB = (naf p q) (naf q p),

then ask(p) = ask(q) = unknown Thus CWA with naf can require

ternary logicBoolean Logic CWA

Possibilistic

Ternary Logic OWA

Boolean Logic OWA Ternary Logic CWA

Plausibilistic

Probabilistic

Epistemological CommitmentEpistemological Commitment

Possibilistic commitment Unary modal connectives: □f, f is necessarily true ◊f, f is possibly true inference rules to combine them

with classical connectives Plausibilistic commitment

(Partial) order, “strenght of belief” rank the plausibility of each formula

inference rules to derive plausibility of a complex formula with connectives from its atoms

Probabilistic commitment Element of [0,1] give probability

of truth for each formula Laws of probability applied to

derive probability of complex formula from its atom

Boolean Logic CWA

Possibilistic

Ternary Logic OWA

Boolean Logic OWA Ternary Logic CWA

Plausibilistic

Probabilistic

Ontological CommitmentOntological Commitment

Propositional: Only propositions with no internal structure,

simple symbol (i.e., whole KB can only describe properties of one individual instance)

No variables, relations, classes or objects ex, rain wetGrass

First-Order Relational: Predicates (relations) with universally quantified

variable arguments and recursive functions, but no structural aggregation of properties nor distinguished generalization relation

ex, D,G day(D) rain(D) ground(G) state(G,D,wet)

First-Order Object-Oriented: Classes, sub-classes, attributes, associations

(relations), operations, objects, links ex, D:day[weather -> rain] G:ground

G[state(D) -> wet].

High-OrderOO


First-OrderOO


Propositional

Ontological CommitmentOntological Commitment

High-Order Relational: Universally quantified variables in

predicates, functions, and formula positions

ex, R,X,Y trans(R)(X, Y) (R(X, Y) (R(X, Z) trans(R)(Z,Y))

High-Order Object-Oriented: Universally quantified variables not only

as object names, but also as class names, attribute names, association names, operation names

G[A => T1, M(P:T2) => T2] trans(“:”)(S,G) S[A => T1, M(P:T2) => T2]

High-OrderOO


First-OrderOO


Propositional

KBA ArchitecturesKBA Architectures

A KBA is a: Reflex Agent? Automata Agent? Goal-Based Agent? Planning Agent? Hybrid Agent? Utility-Based Agent? Adaptive Agent? Layered Agent?

Can be anyone !

Is there any constraint between the reasoning performed by the inference engine and the agent architecture?

Adaptive agent requires analogical or inductive inference engine

AskTell

Non-Adaptive KBANon-Adaptive KBA

Enviro

nm

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Sensors

Effectors

Persistent Knowledge Base (PKB):rules, classes, logical formulas or probabilities

representing generic laws about environment class

Inference Engine for Deduction, Abduction, Inheritance, Belief Revision, Belief Update,

Planning, Constraint Solving or Optimization

Volatile Knowledge Base (VKB):facts, objects, constraints, logical formulas or probabilities

representing environment instance in current agent execution

Retract

Ask

Non-Monotonic

Engine

AskTell

Analogical KBAAnalogical KBA

Enviro

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Sensors

Effectors

Persistent Knowledge Base (PKB):facts, objects, constraints, logical formulas or probabilities

representing environment instances in past agent executionsstructured by similarity measure

Inference Engine for Analogy



Retract

Ask

Remember the Planning Agent?Remember the Planning Agent?

Enviro

nm

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Sensors

Effectors

(Past and)Current

EnvironmentModel

Percept InterpretationRules: percept(t) model(t) model’(t)

Action ChoiceRules: model(t+n) = result([action1(t),...,actionN(t+n)] model(t+n) goal(t) do(action(t))

Model UpdateRules: model(t-1) model(t) model’(t) model’’(t)

Goal UpdateRules: model’’(t) goals(t-1) goals’(t)

GoalsPrediction of Future EnvironmentsRules: model’’(t) model(t+n) model’’(t) action(t) model(t+1)

HypotheticalFuture

EnvironmentModels

How would be then aHow would be then aknowledge-based planning agentknowledge-based planning agent??

En

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Sensors

Effectors

VKB: Past and Current

Environment Models

VKB: Hypothetical FutureEnvironment Models

VKB: Goals

PKB: PerceptInterpretation

PKB: EnvironmentModel Update

PKB: GoalsUpdate

PKB: Prediction ofFuture Environments

PKB: ActingStrategy

InferenceEngine

Alternative Planning KBA Alternative Planning KBA ArchitectureArchitecture

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Environment Models


VKB: Goals



PKB: GoalsUpdate


PKB: ActingStrategy

InferenceEngine 1

InferenceEngine 2

InferenceEngine 3

InferenceEngine 4

InferenceEngine 5

Why Using Multiple Inference Why Using Multiple Inference Engines?Engines?

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Environment Models


VKB: Goals



PKB: GoalsUpdate


PKB: ActingStrategy

InferenceEngine 1

InferenceEngine 2

InferenceEngine 3

InferenceEngine 4

InferenceEngine 5

Abduction

BeliefUpdate

Deduction

ConstraintSolving

Optimization

How to Acquire Knowledge?How to Acquire Knowledge?

Development time: Persistent knowledge and initial volatile knowledge

Manually by direct coding Semi-automatically through a knowledge acquisition interface Using a knowledge engineering methodology Semi-automatically with machine learning (off-line induction, analogy

and reinforcement learning in simulated situations) Using a knowledge discovery methodology

Run time: Volatile knowledge

Automatically through perceptions and deduction, abduction, inheritance, belief revision, belief update, constraint solving, optimization or analogy

Persistent knowledge Automatically through machine learning (analogy, on-line induction or

situated reinforcement learning)

Knowledge EngineeringKnowledge Engineering

Develop methodologies, processes and tools to built knowledge bases Develop methodologies, processes and tools to built knowledge bases and knowledge base systemsand knowledge base systems

Many common issues with software engineering:Many common issues with software engineering: Robustness, scalability, extensibility, reusability Distributed development, trade-off between quality, cost and time

Added difficulties of knowledge engineering:Added difficulties of knowledge engineering: Non-computing domain expert not contributing merely requirements (what Non-computing domain expert not contributing merely requirements (what

to do?) but often the core knowledge (how to do it?), (s)he is thus a critical to do?) but often the core knowledge (how to do it?), (s)he is thus a critical part of the development teampart of the development team

Users not only needs to use the system but also to understand how it Users not only needs to use the system but also to understand how it reasonreason

Lack of standard knowledge representation languages and industrial Lack of standard knowledge representation languages and industrial strength CAKE toolsstrength CAKE tools

Declarative knowledge processed by non-deterministic engines harder to Declarative knowledge processed by non-deterministic engines harder to debug than step-by-step algorithms (more is left to the machine)debug than step-by-step algorithms (more is left to the machine)

Common paradigms: object-oriented methods, formal methodsCommon paradigms: object-oriented methods, formal methods Most processes: spiral development at 3 abstraction levels:Most processes: spiral development at 3 abstraction levels:

Knowledge level, formalization level, implementation level

Knowledge Base EngineeringKnowledge Base Engineering

Knowledge Elicitation

Knowledge Formalization

Knowledge Implementation

Knowledge levelKnowledge level:• Using the vocabulary of the domain experts• Natural language, domain-specific graphical notation

Semi-formal levelSemi-formal level:• Standard structured textual notation (XML) • Standard graphical notation (UML)• Validation with Expert

Formal level:Formal level:• Unambiguous notation w/ mathematical formal semantics (Logic, probability theory)• Consistency verification

Implementation:Implementation:• Inference engine or programming language• Prototype testing


Knowledge Elicitation

Knowledge Formalization

Knowledge Implementation

Knowledge levelKnowledge level: Using the vocabulary of the domain experts

Natural language, domain-specific graphical notation

Implementation:Implementation: Inference engine or programming language

Prototype testing

Semi-formal levelSemi-formal level: Standard structured textual notation

(XML) Standard graphical notation (UML)

Validation with Expert

Formal level:Formal level: Unambiguous notation w/

mathematical formal semantics (Logic, probability theory)

Consistency verification


Elicitação do conhecimento

Formalização do conhecimento

Implementação do conhecimento

Knowledge levelKnowledge level: Using the vocabulary of the domain experts

Natural language, domain-specific graphical notation

Semi-formal levelSemi-formal level: Standard structured textual notation (XML)

Standard graphical notation (UML) Validation with Expert

Formal level:Formal level: Unambiguous notation w/

mathematical formal semantics (Logic, probability theory)

Consistency verification

Implementation:Implementation: Inference engine or programming language

Prototype testing

• Structured interviews with domain expert• Data preparation

• Ontologies • Semi-formal KR languages

• Formal KR Language• Machine Learning

• Compilers• Inference Engines• Machine Learning

Off-Line Inductive Agent:Off-Line Inductive Agent:Training PhaseTraining Phase

Data, Examples or Case Base facts, objects, constraints or

logical formulas codifyingrepresentative sample of

environment entities

PerformanceInference Engine:

Any Reasoning TaskExcept Analogyand Induction

Intentional Knowledge Base (IKB):rules, classes or logical formulas

representing generic lawsabout environment class

InductiveInference Engine

Ask

Tell

Retract

HypothesisFormation

HypothesisVerification

Ask

Tell

Retract

Ask

Ask

Off-Line Inductive Agent: Usage Off-Line Inductive Agent: Usage PhasePhase

AskTell

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Inductively LearnedPersistent Knowledge Base (PKB):

rules, classes, logical formulas or probabilitiesrepresenting generic laws about environment class

Inference Engine for Deduction, Abduction, Inheritance, Belief Revision, Belief Update,

Planning, Constraint Solving or Optimization



Retract

Ask

Ontologies Reasoning Components Agents Simulations Knowledge-Based Agents Jacques Robin.

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