Knowledge-Based Agents

1
Knowledge-Based Agents

• Jacques Robin

2
Introductory ExampleIs 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
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
Knowledge Base BDomain Model in Logic L
Inference Engine Theorem Prover for Logic L
Tell
Retract
Actuators
6
Example of Automated Reasoning with Knowledge
Base Is West Criminal?

• 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

7
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)))))
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Review of CFOLImplicative 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)))
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Review of CFOL Term Unification
Failure by Occur-Check
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Review of CFOLRefutation 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) ?
  ?(Pi1) ?...? ?(Pn) ? ?(Q1) ?...? ?(Qk))
  ? (?(C1) ?... ? ?(Cm) ? ?(D1) ?... ?
  ?(Dj-1) ? ?(Dj1) ?... ? ?(Dn))

11
Example of Automated Reasoning with Knowledge
Base Is West 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

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Example of Automated Reasoning with Knowledge
Base Is West Criminal?

• Step 2 repeatedly apply resolution rule to pair
  of clauses (A,B) where As premise unifies with
  Bs 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

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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

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Intentional 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,X1,Y) ?
  loc(wumpus,X-1,Y)? loc(wumpus,X,Y1) ?
  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)

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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,X1,Y,T)
  ? loc(wumpus,X-1,Y,T)? loc(wumpus,X,Y1,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

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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,Y1) ?
  loc(agent,X,Y1,T1).

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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,Y1) ?
  loc(agent,X,Y1,T1).
• 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,X1,Y) ?
  
  loc(wumpus,X-1,Y) ?
  loc(wumpus,X,Y1) ?
  loc(wumpus,X,Y-1)).

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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
  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) ? ? loc(wall,X,Y1) ?
  loc(agent,X,Y1,T1).
• 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).

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Certain x Uncertain Knowledge

• Certain knowledge
• Statement epistemologically guaranteed true or
  false
• ex, ? X,Y smelly(X,Y) ? ? smelly(X1,Y-1) ? ?
  smelly(X-1,Y-1) ? loc(wumpus,X,Y1).
• 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,X1,Y) ?
  loc(wumpus,X-1,Y) ?
  loc(wumpus,X,Y1) ?
  loc(wumpus,X,Y-1)).
• ex, ? X,Y ?smelly(X,Y,1) ? (?loc(wumpus,X1,Y) ?
  ?loc(wumpus,X-1,Y) ?
  ?loc(wumpus,X,Y1) ?
  ?loc(wumpus,X,Y-1)).
• ex, ? X,Y,U,V smelly(X,Y,1) ? (U ?1 X1) ? (U ?1
  X-1) ? (V ?1 Y1) ? (V ?1 Y-1) ?
  loc(wumpus,X1,Y,P1) ? loc(wumpus,X-1,Y,P2) ?
  loc(wumpus,X,Y1,P3) ?
  loc(wumpus,X,Y-1,P4) ? ?loc(wumpus,X1,Y,P5
  ) ? ?loc(wumpus,X-1,Y,P6) ?
  ?loc(wumpus,X,Y1,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,X1,Y) smelly(X,Y,1))
  0.25 ? p(loc(wumpus,X-1,Y)
  smelly(X,Y,1)) 0.25 ?
  p(loc(wumpus,X,Y1) smelly(X,Y,1)) 0.25 ?
  p(loc(wumpus,X,Y-1) smelly(X,Y,1))
  0.25.

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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 designers 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.

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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

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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,T1,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.

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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,X1,Y) ?
  loc(wumpus,X-1,Y) ?
  loc(wumpus,X,Y1) ?
  loc(wumpus,X,Y-1)).

26
Domain-Level x Meta-Level Knowledge

• Domain-level knowledge
• Knowledge modeling the agents 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)

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Roadmap of Automated Reasoning (AR)
28
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 XYZ ? 1?X ? X?Z
? X?Y ? Y?Z ? Z? 7w/ utility(X,Y,Z) X
Z Derive optimum X2 ? Y4 ? Z6
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 XYZ ? 1?X ? X?Z ?
X?Y ? Y?Z ? Z? 7 Into X2 ? 3?Y ? Y? 4 ? 5? Z ?
Z?6 or (X2 ? Y3 ? Z5) ? (X2 ? Y4 ? Z6)
Reasoning Task
29
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 (p ? naf q) ? (q ? naf p),then ask(p)
  ask(q) unknown
• Thus CWA with naf can require ternary logic

Boolean Logic CWA
30
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, Ddayweather -gt rain ? Gground ?
  Gstate(D) -gt 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
• GA gt T1, M(PT2) gt T2 ? trans()(S,G) ?
  SA gt T1, M(PT2) gt T2

High-Order OO
High-Order Relational
First-Order OO
First-Order Relational
Propositional
33
KBA 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

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
Ask
Tell
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(tn) model(t) ?
action(t) ? model(t1)
Hypothetical Future Environment Models
Action Choice Rules model(tn)
result(action1(t),...,actionN(tn) ?
model(tn) ? goal(t) ? do(action(t))
Effectors
37
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
Inference Engine 1
Abduction
Sensors
VKB Past and Current Environment Models
PKB Environment Model Update
Inference Engine 2
Belief Update
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 AgentTraining 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 Base facts, objects,
constraints or logical formulas
codifying representative sample of environment
entities
Ask
Performance Inference EngineAny 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