Artificial Intelligence Knowledge representation

1. Artificial IntelligenceKnowledge representation Fall 2008 professor: Luigi Ceccaroni

2. Introduction • Knowledge engineers and system analysts need to bring knowledge forth and make it explicit. (Why?) • They display the implicit knowledge about a subject in a form that programmers can encode in algorithms and data structures. • To make the hidden knowledge accessible to computers, knowledge-based systems and object-oriented systems are needed. 2

3. Introduction • Knowledge-based and object-oriented systems are built around declarative languages: • Forms of expression closer to human languages • Such systems help to express the knowledge in a form that both humans and computers can understand. • This part of the course is about knowledge-base analysis and design: • To analyze knowledge about the real world and map it to a computable form 3

4. Logic, ontology and computation • Knowledge representation (KR) is an interdisciplinary subject that applies theories and techniques from three fields: • Logic provides the formal structure and rules of inference. • Ontology defines the kinds of things that exist in the application domain. • Computation supports the applications that distinguish KR from pure philosophy. 4

5. Principles of knowledge representation • Knowledge engineering is the application of logic and ontology to the task of building computable models of some domain for some purpose. • In 1993, three experts in KR, Davis, Schrobe and Szolovits, wrote a critical review and analysis of the state of the art: • Five basic principles about knowledge representations (KRs) and their role in artificial intelligence 5

6. What is a knowledge representation? • A surrogate • Imperfect surrogates mean incorrect inferences are inevitable • A set of ontological commitments • Commitment begins with the earliest choices • The commitments accumulate in layers • Reminder: a KR is not a data structure • A fragmentary theory of intelligent reasoning • What is intelligent reasoning? • Which inferences are sanctioned? • Which inferences are recommended? • A medium for efficient computation • A medium of human expression

7. A KR is a surrogate • Description of something else • Abstract, simplified view of a domain • Symbolic structure with formal symbol-manipulating rules • Rules are based only on the syntactic form of the representation • Requires specification of mapping to intended referent: • an interpretation • Contains simplifying assumptions and inaccuracies • Susceptible to supporting incorrect reasoning results

8. A KR is a set of ontological commitments • What to consider in thinking about a world: concepts, relations, objects • Example: representing an electric circuit • “Lumped element model” • Components with connections between them • Signals flowing instantaneously along the connections • “Electrodynamics model” • Signals propagating at finite speeds • Locations of and distances between components • Components through which electromagnetic waves flow • KR is not about data structures

9. A KR is a set of ontological commitments An ontological commitment is an agreement to use a vocabulary (i.e., ask queries and make assertions) in a way that is consistent (but not complete) with respect to the theory specified by an ontology. We build agents that commit to ontologies. We design ontologies so we can share knowledge with and among these agents. Tom Gruber (KSL, Stanford)

10. A KR is a fragmentary theory of intelligent reasoning • It provides different strategies for reasoning. • These strategies can be used by humans and computers. • It sanctions a set of inferences. • “What can we infer from what we know?” • It recommends a set of inferences. • “What ought we to infer from what we know?”

11. A KR is a medium for efficient computation • Reasoning in machines is a computational process: • Both the procedural and the declarative approaches can be transformed to a computable form. • Computational efficiency is a central design goal. • Expressivity and tractability of reasoning are traded off.

12. A KR is a medium of human expression • How useful is it as a medium of expression? • How general is it? • How precise is it? • For what tasks does it provide expressive adequacy? • How useful is it as a medium of communication? • Can we easily “talk” or think in the representation language? • What kinds of things are easily said in the language? • What kinds of things are so difficult to say in the language as to be pragmatically impossible?

13. KRs vs. data bases • Both “represent” knowledge. • Standard data bases do not contain: • disjunctions (e.g., “The ball is either red or blue.”) • quantifiers (e.g., “Every person has two parents.”) • Data base schema provide some quantified information • Deductive data bases include implications • Data base research concerns: • Efficient access and management of large distributed data bases • Concurrent updating • KR research concerns: • Expressivity • Effective reasoning

14. What is a knowledge base (KB)? • An informal term for a collection of information that includes an ontology as one component. • Besides an ontology, a KB may contain information specified in a declarative language such as logic or expert-system rules. • It may also include unstructured or unformalized information expressed in natural language or procedural code. 14

15. Issues in KR research • What knowledge needs to be represented to answer given questions? • How is incomplete or noisy information represented? • How is qualitative or abstracted knowledge represented? • How can knowledge be encoded so that it is reusable? • How are assumptions represented and reasoned with?

16. Issues in KR research • How can knowledge be reformulated for a given purpose? • How can effective automatic reasoning be done with large-scale knowledge bases? • How can computer-interpretable knowledge be extracted from documents? • How can knowledge from multiple sources be combined and used?

17. Issues in KR research • This is a world where massive amounts of data and applied mathematics replace every other tool: • Out with every theory of human behavior, from linguistics to sociology. • Forget taxonomy, ontology, and psychology. • Who knows why people do what they do? • The point is they do it, and we can track and measure it with unprecedented fidelity. • With enough data, the numbers speak for themselves. Chris Anderson

18. Historical background • The words knowledge and representation have provoked philosophical controversies for over 2500 years. • 500 B.C.: Socrates claims to know very little, if anything. • He destroyed the self-satisfaction of people who claimed to have knowledge of fundamental subjects like: • Truth • Beauty • Virtue • Justice 18

19. Historical background • For his impiety in questioning cherished beliefs, Socrates was condemned to death as a corrupter of the morals Athenian youth. • Socrates’ student Plato established the subject of epistemology: • the study of the nature of knowledge and its justification • Plato’s student Aristotle shifted the emphasis of philosophy from the nature of knowledge to the less controversial but more practical problem of representing knowledge. 19

20. Historical background • Aristotle’s work resulted in an encyclopedic compilation of the knowledge of his day. • But before he could compile that knowledge, he had to invent the words for representing it. • He established the initial terminology and defined the scope of logic, physics, metaphysics, biology, psychology, linguistics, politics, ethics, rhetoric and economics. 20

21. Historical background • Terms that Aristotle coined or adopted have become the core of today’s international technical vocabulary: • category • metaphor • hypothesis • quantity • quality • species • noun ... and then artificial intelligence arrived. 21

22. Early history of KR (60’s - 70’s) • Origins • Problem solving work primarily at CMU and MIT • Natural language understanding • Many ad hoc formalisms • “Procedural” vs. “declarative” knowledge • Procedural: functions, rules, conventional programming languages • Declarative: logic, Prolog • No formal semantics

23. Emerging paradigms (70’s - 80’s) • Semantic nets • Unstructured node-link graphs • No semantics to support interpretation • No axioms to support reasoning • Reference: “What’s in a Link: Foundations for Semantic Nets”; Woods, W. A. In Representation and Understanding: Studies in Cognitive Science; edited by D. Bobrow and A. Collins; Academic Press; 1975.

24. Emerging paradigms (70’s - 80’s) • Frames • Structured semantic nets • Object-oriented descriptions • Prototypes • Class-subclass taxonomies • Reference: “A Framework for Representing Knowledge” M. Minsky Mind Design; J. Haugeland, editor; MIT Press; 1981.

25. Example: Frames: class-subclass taxonomy

26. Example: Frames: Class frame

27. Example: Frames: Instance frame

28. Emerging paradigms (70’s - 80’s) • Production rule systems • If-then inference rules • If (warning-light on) then (engine overheating) • If (warning-light on) then ((engine overheating) 0.95) • Situation-action rules • If (warning-light on) then (turn-off engine) • Hybrid procedural-declarative representation • Basis for expert systems

29. Emerging paradigms (70’s - 80’s) • Qualitative physics • Representing and reasoning: • With incomplete knowledge • About physical mechanisms • Qualitative descriptions • Capture distinctions that make an important qualitative difference and ignores others • Aggregate values that have no qualitative difference

30. Emerging paradigms (70’s - 80’s) • Symbolic Logic • Primarily first-order logic “Everybody loves somebody sometime.” (forall ?p (implies (Person ?p1) (exists (?p2 ?t) (and (Person ?p2) (Time ?t) (Loves ?p1 ?p2 ?t))))) • Resolution theorem proving

31. KR in the 90’s and 00’s • Declarative representations • Easier to change • Multi-use • Extendable by reasoning • Accessible for introspection • Formal semantics • Defines what the representation means • Specifies correct reasoning • Allows comparison of representations/algorithms • KR rooted in the study of logics • temporal, context, modal, default, nonmonotonic... • Rigorous theoretical analysis