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Knowledge-Based Agents

Knowledge-Based Agents. Jacques Robin. Introductory Example: 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”

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Knowledge-Based Agents

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  1. Knowledge-Based Agents Jacques Robin

  2. Introductory Example: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

  3. Ask Tell Retract Knowledge-Based Agents Environment Automated Reasoning Sensors Domain-Specific Knowledge Base Generic Domain-Independent Inference Engine Knowledge Representation and Acquisition Effectors

  4. 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

  5. Example KBA: Logic-Based Agent Given B as axiom, formula f is a theorem of L? B |=L f ? Environment Sensors Ask Inference Engine: Theorem Prover for Logic L Knowledge Base B:Domain Model in Logic L Tell Retract Actuators

  6. From the knowledge: 1. It is crime for a US citizen to sell weapons to a hostile nation 2. Nono own missiles 3. Nono bought all its missiles from Captain West 4. West is a US Citizen 5. Nono is a nation 6. Nono is an enemy of the USA 7 . A missile is a weapon 8. Enmity is the highest form of hostilty 9. 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 Example of Automated Reasoning with Knowledge Base: Is West Criminal?

  7. FCLUnaryConnective FCLBinaryConnective Arg Arg Connective: enum{} Connective: enum{, , , } 1..* 1..* Functor Arg FCLConnective FCFOLAtomicFormula FCFOLTerm 1..2 Functor * FCFOLNonFunctionalTerm FCFOLFunctionalTerm PredicateSymbol Functor Arg 1..* FunctionSymbol FOLVariable ConstantSymbol QuantifierExpression Quantifier: enum{,} Review of CFOL: General Syntax FCFOLFormula Example formula: X,Y (p(f(X),Y) q(g(a,b)))  ((U,V Z ((X = a)  r(Z))  (U = h(V,Z)))))

  8. Premisse * * Arg Arg INFCFOLFormula 1..* 1..* Functor =  Conclusion * FCFOLAtomicFormula FCFOLTerm Functor FCFOLFunctionalTerm FCFOLNonFunctionalTerm PredicateSymbol * Functor FunctionSymbol INFCFOLClause INFCLPLHS INFCLPRHS FOLVariable ConstantSymbol Functor =  Functor =  Functor =  Review of CFOL: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) 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)))

  9. X/b X/b p p p p p p p p p fail X/a Y/a a b a a a a a a X X X X X X Y X b b X/f(c,Z) X/f(c,d) Y/a Y/a Z/d p p p p X/p(a,X) X X X/p(a,X) p fail Y Y f f a a f f a p Guarantees termination c c Z Z c c Z d c p Review of CFOL: Term Unification Failure by Occur-Check

  10. Review of CFOL: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))

  11. 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 Example of Automated Reasoning with Knowledge Base: Is West Criminal?

  12. 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 Resolve 0 with 1 unifying P/west: american(west)  weapon(W)  nation(N)  hostile(N)  sells(west,N,W)  F //10 2. Resolve 10 with 4: weapon(W)  nation(N)  hostile(N)  sells(west,N,W)  F //11 3. Resolve 11 with 7: missile(W)  nation(N)  hostile(N)  sells(west,N,W)  F //12 4. Resolve 12 with 2b unifying W/m1: nation(N)  hostile(N)  sells(west,N,m1)  F //13 5. Resolve 13 with 5 unifying N/nono: hostile(nono)  sells(west,nono,m1)  F //14 6. Resolve 14 with 8 unifying N/nono: enemy(nono,america)  sells(west,nono,m1)  F //15 7. Resolve 15 with 6: sells(west,nono,m1)  F //16 8. Resolve 16 with 3 unifying W/m1: owns(nono,m1)  missile(m1)  F //17 9. Resolve 17 with2a: missile(m1)  F //18 10. Resolve 18 with 2b: F Example of Automated Reasoning with Knowledge Base: Is West Criminal?

  13. Dimensions of Knowledge Classification • 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 Vague • Declarative x Procedural • Common Sense x Expert • Domain-Level x Meta-Level

  14. 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) Extensional knowledge: about specific entity instances and their particular 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) Intentional x Extensional Knowledge

  15. Persistent 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

  16. Structural 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).

  17. Causal 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)   loc(wall,X,Y+1)  loc(agent,X,Y+1,T+1). • 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)).

  18. Synchronous x Diachronous Knowledge • 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 sameproperty 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)   loc(wall,X,Y+1)  loc(agent,X,Y+1,T+1). • Synchronous knowledge: • Describes the relation between the value of two distinctproperties 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).

  19. Certain 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.

  20. Precise 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

  21. Explicit 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.

  22. Implicit x Explicit Knowledge: Illustrative 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

  23. Implicit x Explicit Knowledge: Illustrative 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.

  24. Declarative 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

  25. Common 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 • FFluents, T2Time (holds(F,T2)  (EEvents, TTimes, happens(E,T)  initiates(E,F)  (T  T2)  clipped(F,T,T2)) (clipped(F,T,T2)  (EEvents, T1Times, 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)).

  26. Domain-Level x Meta-Level Knowledge • 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)

  27. Knowledge Representation Language Ontological Commitment • Inference engine characterized • by 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 High-Order OO High-Order Relational First-Order OO First-Order Relational Propositional Deduction Abduction Inheritance Belief Revision Belief Update Constraint Solving Planning Optimization Induction Analogy Fuzzy ? Boolean Logic CWA Boolean Logic OWA Ternary Logic CWA Ternary Logic OWA Reasoning Task Probabilistic Plausibilistic Possibilistic Knowledge Representation Language Epistemological Commitment Roadmap of Automated Reasoning (AR)

  28. Deduction Abduction Inheritance Belief Revision Planning Belief Update Constraint Solving Optimization Induction Analogy Dimensions of AR Services From: X,Y p(X,a)  q(b,Y)  r(X,Y)  p(1,a)  q(b,2) Deduce: r(1,2) 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) From: X,Y,Z  N X+Y=Z  1X  XZ  XY  YZ  Z 7w/ utility(X,Y,Z) = X + Z Derive optimum: X=2  Y=4  Z=6 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: 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 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)] From: G G instanceOf g  p(G)  s subclassOf g  s1 instanceOf s Inherit: p(s1) 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) From: X p(X) ~ r(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) Solve: X,Y,Z N X+Y=Z 1X XZ  XY YZ  Z 7 Into: X=2  3Y  Y 4  5 Z  Z6 or (X=2  Y=3  Z=5)  (X=2  Y=4  Z=6) Reasoning Task

  29. Probabilistic Plausibilistic Possibilistic Ternary Logic OWA Boolean Logic OWA Ternary Logic CWA Epistemological Commitment • Open-World Assumption (OWA): • f  KB xor f  KB • ask(q) = true iff KB |= q • ask(q) = false iff KB |= q • 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 logic Boolean Logic CWA

  30. Probabilistic Plausibilistic Possibilistic Ternary Logic OWA Boolean Logic OWA Ternary Logic CWA Epistemological 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

  31. Ontological 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-Order OO High-Order Relational First-Order OO First-Order Relational Propositional

  32. Ontological 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-Order OO High-Order Relational First-Order OO First-Order Relational Propositional

  33. 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 KBA Architectures

  34. Non-Adaptive KBA Environment Persistent Knowledge Base (PKB): rules, classes, logical formulas or probabilities representing generic laws about environment class Sensors Ask Inference Engine for Deduction, Abduction, Inheritance, Belief Revision, Belief Update, Planning, Constraint Solving or Optimization Non-Monotonic Engine Ask Tell Retract Volatile Knowledge Base (VKB): facts, objects, constraints, logical formulas or probabilities representing environment instance in current agent execution Effectors

  35. Analogical KBA Environment Persistent Knowledge Base (PKB): facts, objects, constraints, logical formulas or probabilities representing environment instances in past agent executions structured by similarity measure Sensors Ask Inference Engine for Analogy Tell Ask Retract Volatile Knowledge Base (VKB): facts, objects, constraints, logical formulas or probabilities representing environment instance in current agent execution Effectors

  36. Remember the Planning Agent? Environment (Past and)Current Environment Model Percept Interpretation Rules: percept(t)  model(t)  model’(t) Sensors Model Update Rules:model(t-1)  model(t) model’(t)  model’’(t) Goal Update Rules:model’’(t)  goals(t-1)  goals’(t) Goals Prediction of Future Environments Rules: model’’(t)  model(t+n) model’’(t)  action(t)  model(t+1) Hypothetical Future Environment Models Action Choice Rules: model(t+n) = result([action1(t),...,actionN(t+n)] model(t+n) goal(t)  do(action(t)) Effectors

  37. Environment Sensors PKB: PerceptInterpretation VKB: Past and Current Environment Models PKB: Environment Model Update PKB: Goals Update Inference Engine VKB: Goals VKB: Hypothetical Future Environment Models PKB: Prediction of Future Environments PKB: Acting Strategy Effectors How would be then aknowledge-based planning agent?

  38. Alternative Planning KBA Architecture Environment PKB: PerceptInterpretation Inference Engine 1 Sensors VKB: Past and Current Environment Models PKB: Environment Model Update Inference Engine 2 PKB: Goals Update Inference Engine 3 VKB: Goals VKB: Hypothetical Future Environment Models PKB: Prediction of Future Environments Inference Engine 4 PKB: Acting Strategy Inference Engine 5 Effectors

  39. Why Using Multiple Inference Engines? Environment PKB: PerceptInterpretation Abduction Inference Engine 1 Sensors VKB: Past and Current Environment Models PKB: Environment Model Update Belief Update Inference Engine 2 PKB: Goals Update Inference Engine 3 Deduction VKB: Goals VKB: Hypothetical Future Environment Models PKB: Prediction of Future Environments Inference Engine 4 Constraint Solving PKB: Acting Strategy Optimization Inference Engine 5 Effectors

  40. 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)

  41. Knowledge Engineering • Develop methodologies, processes and tools to built knowledge bases and knowledge base systems • Many common issues with software engineering: • Robustness, scalability, extensibility, reusability • Distributed development, trade-off between quality, cost and time • Added difficulties of knowledge engineering: • 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 part of the development team • Users not only needs to use the system but also to understand how it reason • Lack of standard knowledge representation languages and industrial strength CAKE tools • Declarative knowledge processed by non-deterministic engines harder to debug than step-by-step algorithms (more is left to the machine) • Common paradigms: object-oriented methods, formal methods • Most processes: spiral development at 3 abstraction levels: • Knowledge level, formalization level, implementation level

  42. Knowledge Base Engineering Knowledge Elicitation • Knowledge level: • Using the vocabulary of the domain experts • Natural language, domain-specific graphical notation Knowledge Formalization • Formal level: • Unambiguous notation w/ mathematical formal semantics (Logic, probability theory) • Consistency verification • Semi-formal level: • Standard structured textual notation (XML) • Standard graphical notation (UML) • Validation with Expert Knowledge Implementation • Implementation: • Inference engine or programming language • Prototype testing

  43. Knowledge Base Engineering Knowledge Elicitation Knowledge level: Using the vocabulary of the domain experts Natural language, domain-specific graphical notation Knowledge Formalization Formal level: Unambiguous notation w/ mathematical formal semantics (Logic, probability theory) Consistency verification Semi-formal level: Standard structured textual notation (XML) Standard graphical notation (UML) Validation with Expert Knowledge Implementation Implementation: Inference engine or programming language Prototype testing

  44. Knowledge Base Engineering • Structured interviews with domain expert • Data preparation Elicitação do conhecimento Knowledge level: Using the vocabulary of the domain experts Natural language, domain-specific graphical notation • Ontologies • Semi-formal KR languages • Formal KR Language • Machine Learning Formalização do conhecimento Formal level: Unambiguous notation w/ mathematical formal semantics (Logic, probability theory) Consistency verification Semi-formal level: Standard structured textual notation (XML) Standard graphical notation (UML) Validation with Expert • Compilers • Inference Engines • Machine Learning Implementação do conhecimento Implementation: Inference engine or programming language Prototype testing

  45. Off-Line Inductive Agent:Training Phase Ask InductiveInference Engine Intentional Knowledge Base (IKB): rules, classes or logical formulas representing generic laws about environment class Tell Hypothesis Formation Retract Ask Hypothesis Verification Data, Examples or Case Basefacts, objects, constraints or logical formulas codifying representative sample of environment entities Ask Performance Inference Engine:Any Reasoning Task Except Analogy and Induction Tell Retract Ask

  46. Off-Line Inductive Agent: Usage Phase Inductively Learned Persistent Knowledge Base (PKB): rules, classes, logical formulas or probabilities representing generic laws about environment class Environment Sensors Ask Inference Engine for Deduction, Abduction, Inheritance, Belief Revision, Belief Update, Planning, Constraint Solving or Optimization Ask Tell Retract Volatile Knowledge Base (VKB): facts, objects, constraints, logical formulas or probabilities representing environment instance in current agent execution Effectors

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