Lectures on Artificial Intelligence (CS 364)

1. Lectures on Artificial Intelligence (CS 364) Khurshid Ahmad Professor of Artificial Intelligence Centre for Knowledge Management September 2001 1

2. KNOWLEDGE REPRESENTATION ‘The idea of explicit representations of knowledge, manipulated by general purpose inference algorithms, dates back to the philosopher Leibniz, who envisioned a calculus of propositions that exceed in its scope and power the differential calculus he has developed’ (Brachman, Levesque and Reiter 1991:1)

3. KNOWLEDGE REPRESENTATION 'A representation is a set of conventions about how to describe a class of things. A description makes use of the conventions of a representation to describe some particular thing.' (Winston 1992:16). ‘Good representations make important objects and relations explicit, expose natural constraints, and bring objects and relations together’ (ibid: 44) The representation principle Once a problem is described using an appropriate representation, the problem is almost solved.

4. KNOWLEDGE REPRESENTATION A number of knowledge representation schemes (or formalisms) have been used to represent the knowledge of humans in a systematic manner. This knowledge is represented in a KNOWLEDGE BASE such that it can be retrieved for solving problems. Amongst the well-established knowledge representation schemes are: • Semantic Networks; • Frames; • Conceptual Dependency Grammar; • Conceptual Graphs; • Predicate and Modal Logic • Conceptual or Terminological Logics

5. KNOWLEDGE REPRESENTATION A number of knowledge representation schemes (or formalisms) have been used to represent the knowledge of humans in a systematic manner. This knowledge is represented in a KNOWLEDGE BASE such that it can be retrieved for solving problems. Amongst the well-established knowledge representation schemes are: • Procedural Schemes • (Production Rules) • Propositional Schemes • (Semantic Nets; Frames; ConceptualDependency Grammar, Conceptual Graphs; Logics) • Analogical Schemes • (Matrices)

6. KNOWLEDGE REPRESENTATION A Brief History of Knowledge Representation 1960's: Taxonomy, inheritance and knowledge 'networks‘ 1970's: Structuring the semantic network & the rise of logic 1980's: 'Semantic networks' with semantics & logic for change 1990's: Meta-knowledge representation, belief representation

7. KNOWLEDGE REPRESENTATION A Brief History of Knowledge Representation 1960's: Taxonomy, inheritance and knowledge 'networks‘ Semantic Nets, Frames, Predicate Logic 1970's: Structuring the semantic network & the rise of logic Structured Semantic Networks Logic for Problem Solving: Program = Logic + Control Fuzzy Logic and Uncertainty Representation 1980's: 'Semantic networks' with semantics & logic for change The 'epistemologically explicit' KL-ONE language; Temporal Logic, Deviant Logic, Non-monotonic Logics  1990's: Meta-knowledge representation, belief representation Theoretically well-grounded networks & Pierce movement Representing Belief Default Logics, Temporal reasoning Mixed representation systems

8. KNOWLEDGE REPRESENTATION: NETWORKS& MEANING • Ross Quillian (1966 and 1968) was among the early AI workers to develop a computational model which represented 'concepts' as hierarchical networks. • This model was amended with some additional psychological assumptions to characterise the structure of [human] semantic memory.

9. KNOWLEDGE REPRESENTATION: NETWORKS& MEANING • Collins and Quillian (1969) proposed that • Concepts can be represented as hierarchies of inter-connected concept nodes (e.g. animal, bird, canary) • Any concept has a number of associated attributes at a given level ( e.g. animal --> has skin; eats etc.) • Some concept nodes are superordinates of other nodes (e.g. animal >bird) and some are subordinates (canary< bird) • For reasons of cognitive economy, subordinates inherit all the attributes of their superordinate concepts • • Some instances of a concept are excepted from the attributes that help [humans] to define the superordinates (e.g. ostrich is excepted from flying) • Various [psychological] processes search these hierarchies for information about the concepts represented

10. canary can sing, is yellow bird can fly, has wings, has feathers ostrich runs fast, cannot fly, is tall animal can breathe, can eat, has skin fish can swim, has fins, has gills salmon lays eggs; swims upstream, is pink, is edible KNOWLEDGE REPRESENTATION : NETWORKS& MEANING • A Hierarchical Network is-a is-a is-a is-a is-a

11. canary can sing, is yellow is-a bird can fly, has wings, has feathers is-a ostrich runs fast, cannot fly, is tall animal can breathe, can eat, has skin is-a fish can swim, has fins, has gills is-a salmon lays eggs; swims upstream, is pink, is edible is-a KNOWLEDGE REPRESENTATION: NETWORKS& MEANING • From the above taxonomic organisation of knowledge about a number of different animals, and one can conclude, by ‘inheriting properties down thetaxonomy’, that canaries, ostriches and salmon all have skin and can breathe. • But we as humans can also make exceptions to inherited properties in that we can represent an unflighted bird in a (sub-) hierarchy of birds by simply noting the exception, 'can't fly'.

12. canary can sing, is yellow is-a bird can fly, has wings, has feathers is-a ostrich runs fast, cannot fly, is tall animal can breathe, can eat, has skin is-a fish can swim, has fins, has gills is-a salmon lays eggs; swims upstream, is pink, is edible is-a KNOWLEDGE REPRESENTATION: NETWORKS& MEANING Collins and Quillian showed carried out a number of test on human subjects and found that the subjects recognise propositions lower down the hierarchy (canary is a yellow bird) as compared to propositions higher up the hierarchy more readily than higher above (canary has skin).

13. canary can sing, is yellow is-a bird can fly, has wings, has feathers is-a ostrich runs fast, cannot fly, is tall animal can breathe, can eat, has skin is-a is-a fish can swim, has fins, has gills salmon lays eggs; swims upstream, is pink, is edible is-a KNOWLEDGE REPRESENTATION: NETWORKS& MEANING • A semantic network is a structure for representing knowledge as a pattern of interconnected nodes and arcs.Nodes in the net represent concepts of entities, attributes, events, values. Arcs in the network represent relationships that hold between the concepts

14. C111 C111’s attributes C11 C11’s attributes is-a C112 C112’s attributes C1 C1’s attributes is-a is-a C12 C12’s attributes C121 C121’s attributes is-a is-a KNOWLEDGE REPRESENTATION: NETWORKS& MEANING • Concepts labeled C111 and C112 inherit all the attributes of C11 which, in turn, inherits all the attributes of C1; similarly C121 inherits attributes of C12 and C12 of C1. All arcs are labeled is-a, which relates superordinates (C1) to subordinates (C11, C12) to instances (C111, C112, C121).

15. KNOWLEDGE REPRESENTATION: NETWORKS& MEANING Quillian’s semantic network: A graph theoretic data structure whose nodes represent word senses and whose arcs express binary semantic relationships between these word senses. Quillian gave an account, perhaps first used by a computer scientist, of the associate features of human memory that incorporated a spreading activation model of computation.

16. KNOWLEDGE REPRESENTATION: NETWORKS& MEANING Type Hierarchies Lattices: Sharon is an experimental physicist and is a professional singer Person Artist Scientist Performer Musician Theorist Experimentalist Singer Physicist Lecturer Sharon

17. KNOWLEDGE REPRESENTATION: NETWORKS& MEANING • The problem with the nets had been the interpretation associated with the nodes which in turn relates to the two problems of 'logical' and 'heuristic' adequacy. There are five major area of concerns here: • • First, what does or should the node represent: a class of objects or does the node represent an instance of an object?   • • Second, it is not clear whether the nodes represent the canonical instance of a concept or does the node represent the set of all instances of the object. • • Third, the semantics of a link that define new objects and a link that relate existing objects, particularly those dealing with 'intrinsic' characteristics of a given object. • Fourth, how does one deal with the problems of comparison between objects (or classes of objects) through their attributes: essentially the problem of comparing object instances: • Fifth, what mechanisms there are to handle 'quantification' in semantic network formalisms

18. KNOWLEDGE REPRESENTATION: NETWORKS& MEANING The above five problem lead to the conclusion that the semantic representation is beset by the twin problems of logical and heuristic inadequacy: •Logical inadequacy: A semantic network is representationally inappropriate because the semantic nets could not make many of the distinctions, even pretty simple logical systems can make: between a specific instance of an object, a class of objects, all objects, no object, some objects, etc. • Heuristic inadequacy: Semantic networks do not contain the knowledge which helps in searching a given network

19. KNOWLEDGE REPRESENTATION: NETWORKS& MEANING Schema and Frames The schema for the psychologist Otto Selz is a network of concepts for 'guiding the thinking process', and for the experimental psychologist Fredrick Bartlett it was an active organisation of past experiences and reactions used in thinking and in perception. In its later rendering this notion of schema was taken over by AI researchers during the 1950's and 1960's as a basic building block for organising, storing and retrieving knowledge. There are two dominant and interlinked themes to be found in the knowledge representation literature of that time.

20. KNOWLEDGE REPRESENTATION: NETWORKS& MEANING • Schema and Frames • The term frames appears to have at least five senses in The Oxford Dictionary of Computing: • The total amount of information presented in a display at any one time. • …………………………………………….. • A frame is a list of named SLOTS. Each slot can hold a fact, a POINTER to a slot in another frame, a RULE for deriving the value of the slot, or a PROCEDURE for calculating the value. • Frames can be used to represent the knowledge about a particular object or event

21. KNOWLEDGE REPRESENTATION: NETWORKS& MEANING Schema and Frames Consider the following objects and events: 1. Bill is a cat; 2. Opus is a penguin 3. The year 2000 flood in Chichester 4. Sophie and Edward’s wedding.

22. KNOWLEDGE REPRESENTATION: NETWORKS& MEANING Schema and Frames Living objects 1 & 2 can be described as follows: member subset Opus Penguin Birds subset Animal subset Bill member Cat subset Mammals

23. KNOWLEDGE REPRESENTATION: NETWORKS& MEANING Schema and Frames Events 3 & 4 can be described as follows: a kind of a kind of Chichester Flood Flood Disaster a kind of Event a kind of Sophie & Edwards Wedding a kind of a kind of Celebration Wedding

24. KNOWLEDGE REPRESENTATION: NETWORKS& MEANING Schema and Frames Penguin A semantic network member likes Opus Bill A frame network Penguin member likes Opus Bill

25. KNOWLEDGE REPRESENTATION: NETWORKS& MEANING Schema and Frames: Representing Instances Birds Penguin subset subset 2 Legs flight No flight Yes Animal Opus subset member vitality Yes likes Bill flight No

26. KNOWLEDGE REPRESENTATION: NETWORKS& MEANING Schema and Frames: Handling Exceptions Mammals Cats subset subset 4 Legs Climbs trees Yes Feeds young Yes Bats Animal subset subset vitality Yes Legs 2 flight No flight Yes

27. Event AKO Time Date Place Inheritance of Properties Disaster AKO Damage Fatalities Earthquake AKO Magnitude Fault Flood AKO Depth Area Fire AKO Engines Firemen KNOWLEDGE REPRESENTATION: NETWORKS& MEANING