Knowledge Representation. Knowledge Representation Hypothesis Knowledge representation is an essential problem of symbolic-based artificial intelligence

Download Knowledge Representation. Knowledge Representation Hypothesis Knowledge representation is an essential problem of symbolic-based artificial intelligence

Post on 24-Dec-2015




0 download

Embed Size (px)


  • Slide 1
  • Knowledge Representation
  • Slide 2
  • Knowledge Representation Hypothesis Knowledge representation is an essential problem of symbolic-based artificial intelligence Knowledge Representation Hypothesis (Smith): Any mechanically embodied intelligent process will comprise of structural ingredients, that will represent the propositional account of knowledge the overall process exhibits independently of such a formal semantics will play formal and causal role in performing the behavior that manifests the knowledge
  • Slide 3
  • Knowledge Representation In symbolic functionalism we represent intelligence via manipulation of our beliefs about the surrounding world and knowledge we know. Therefore we have to address two fundamental issues how to represent knowledge how to implement the process of reasoning State space is a space of possible courses of inference when combining actual beliefs about current world general knowledge rules of inference
  • Slide 4
  • The Knowledge Level Three levels of the Knowledge-based System conceptualization: - system engineering level physical realization of the system - symbol level symbol system (program ) specification - knowledge level knowledge (to be represented) specification Knowledge Level Hypothesis There is a distinct computer level lying immediately above the program (symbol level), which is characterized by knowledge as the medium and principle of rationality as the law of behavior.
  • Slide 5
  • AI research Software Engineering Knowledge Level Symbol LevelSystem Level Intelligent Behaviour Requirements Specification Functional Specification System Implementation
  • Slide 6
  • What is Knowledge? data primitive verifiable facts, of any representation. Data reflects current world,often voluminous frequently changing. information interpreted data knowledge relation among sets of data (information), that is very often used for further information deduction. Knowledge is (unlike data) general. Knowledge contains information about behavior of abstract models of the world. Knowledge Classification: according to source : empirical, theoretical according to orientation : domain, heuristic, inference according to type : declarative, procedural
  • Slide 7
  • Knowledge Representation Schemas Logic based representation first order predicate logic, Prolog Procedural representation rules, production system Network representation semantic networks, conceptual graphs Structural representation scripts, frames, objects
  • Slide 8
  • Mathematical Logic Propositional Logic syntactical primitives: , , , , symbols, true, false rule of inference: de Morgan rule, modus ponens, semantic interpretation rains blows-wind sun-will-shine First Order Predicate Logic enriched by variables, predicates, functions quantifiers , friends(father(david),father(andrew)) Y friends(Y, petr) X likes(X,ice_cream) X Y Z parent(X,Y) parent(X,Z) siblings(Y,Z)
  • Slide 9
  • Mathematical Logic cont inference representation proof system rules of inference example: modus ponens if p is true and p q is true, then mp infers q to be true X(man(X) mortal(X)) man(socrates) (man(socrates) mortal(socrates)) mortal(socrates) rules of inference can be sound if all conclusions the rule infers logically follows complete if it infers all conclusions that logically follows modus ponens is sound but not complete
  • Slide 10
  • Mathematical Logic cont inference representation resolution theorem proving transform the knowledge system into clausal normal form (conjunction of disjunction of literals) add negation of what has to be proved keep resolve new disjuncts unless you produce an empty set dog(X) animal(X) dog(X) animal(X) ( dog(X) animal(X)) ( animal(Y) die(Y)) (dog(fido))) ( die(fido) 4 ----------------------- ( dog(Y) die(Y)) 1+2 (die(fido)) 1+2+3 1+2+3+4 1 2 3
  • Slide 11
  • Logic Based Financial Advisor savings(inadequate) investment(savings) savings(adequate) income(adequate) investment(stocks) savings(adequate) income(inadequate) investment(combined) X saved(X) Y dependents(Y) greater(X,5000*Y) savings(adequate) X saved(X) Y dependents(Y) greater(X, 5000*Y) savings(inadequate) X earnings(X,steady) Y dependents(Y) greater(X,(15000+(4000*X)) income(adequate) X earnings(X,steady) Y dependents(Y) greater(X,(15000+(4000*X)) income(inadequate) X earnings(X,unsteady) income(inadequate) saved(22000) earnings(25000,steady) dependents(3) prolog code example
  • Slide 12
  • Production System procedural representation of knowledge in the form of if then rules inference mechanism is firing the rules subject of Expert System lecture jug problem example if small=0 then small=3 if big=0 and small=3 then big=3 and small= 0 5l 3l
  • Slide 13
  • Conceptual Graphs network knowledge representation schema rooted in association theory of meaning very much used in the problem of natural language processing Conceptual Graph is complete bipartite oriented graph, where each node is either a concept or a relation between two concepts, there is one or two edges each going to concepts, and each concept may represent another conceptual graph dogbrown colour
  • Slide 14
  • Conceptual Graphs A monkey scratches its ear with a pawn monkeyscratch agentobject ear instrument paw part of
  • Slide 15
  • Conceptual Graphs each concept has got its type and an instance general concept a concept with a wildcard instance specific concept a concept with a concrete instance there exists a hierarchy of types subtype: concept w is specialisation of concept v if type(v)>type(w) or instance(w)::type(v) dog:Emmabrown colour dog:*Xbrown colour animal dogcat
  • Slide 16
  • Conceptual Graphs canonic conceptual graph is sensible representation of knowledge that can be but does not necessary need to be true canonic formation rules formalise rules of inference between two graph for while preserving canonicity copy identical cloning of a graph restriction substituting a concept in a graph with its specialisation join joining two graphs via shared concept simplification deleting identical relations
  • Slide 17
  • Restriction of Concepts personeat agentobject pie girleat agentobject pie person:Sueeat agentobject pie girl:Sueeat agentobject pie person
  • Slide 18
  • Joining Concepts personeat agentobject pie girl:Sue personeat agentmanner pie fastgirl:Sue person eat agentobject pie agent manner fast
  • Slide 19
  • Simplification of Concepts personeat agentobject pie agent manner fast personeat agent object pie manner fast
  • Slide 20
  • Conceptual Graphs FOPL transformation to CG for each node predicate general concept variable, specific concept atom type:instance type(instance) relation n-ary predicat relation(in1, in2, , inn) with arguments conncecting neighbouring concepts CG is existencionally quantified conjunction of these predicates X (dog(emma) color(emma,X) brown(X)) dog:Emmabrown colour
  • Slide 21
  • Frames instance of structured representation (schemes) static data-structure representing stereotyped situation predecessor of object-oriented systems hotel bed superclass:bed use:sleeping size:king part:mattress frame mattress superclass:cushion firmness:firm hotel room special of:room location:hotel contains: hotel chair hotel phone hotel bed hotel phone special of:phone use: calling room service billing: through room hotel chair special of:chair legs:four use:sitting default slots daemons procedural attachment (infoseek)
  • Slide 22
  • Scripts Schanks formalisation of stereotyped sequence of events in a particular context knowledge base representation in terms of the situations that the system is supposed to understand a restaurant script


View more >