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

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Lectures on Artificial Intelligence (CS 364)

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Khurshid AhmadProfessor of Artificial Intelligence

Centre for Knowledge ManagementSeptember 2001

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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)

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'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 principleOnce a problem is described using an appropriate

representation, the problem is almost solved.

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•Semantic Networks;• Frames;

• Conceptual Dependency Grammar;• Conceptual Graphs;

• Predicate and Modal Logic• Conceptual or Terminological Logics


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:

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• Procedural Schemes (Production Rules)

•Propositional Schemes (Semantic Nets; Frames; ConceptualDependency Grammar, Conceptual Graphs; Logics)

• Analogical Schemes •(Matrices)


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:

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A Brief History of Knowledge Representation1960's: Taxonomy, inheritance and knowledge

'networks‘1970's: Structuring the semantic network & the

rise of logic1980's: 'Semantic networks' with semantics &

logic for change1990's: Meta-knowledge representation, belief

representation

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A Brief History of Knowledge Representation1960's: Taxonomy, inheritance and knowledge 'networks‘

Semantic Nets, Frames, Predicate Logic1970's: Structuring the semantic network & the rise of logic

Structured Semantic NetworksLogic for Problem Solving: Program = Logic + Control

Fuzzy Logic and Uncertainty Representation1980'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 movementRepresenting Belief

Default Logics, Temporal reasoningMixed representation systems

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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.

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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

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: NETWORKS& MEANINGA Hierarchical Network•

salmonlays eggs; swims upstream,

is pink, is edible

ostrichruns fast, cannot fly,

is tall

canarycan sing, is yellow

birdcan fly, has wings,

has feathers

fishcan swim, has fins, has gills

animalcan breathe, can eat,

has skin

is-a

is-a

is-a

is-a

is-a

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NETWORKS& MEANING

is-a


is pink, is edible


is tall



has feathers



has skin

is-a

is-a

is-a

is-a

•From the above taxonomic organisation of knowledge about a number of different animals, and one can conclude, by ‘inheriting properties down the taxonomy’, 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'.

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NETWORKS& MEANING

is-a


is pink, is edible


is tall



has feathers



has skin

is-a

is-a

is-a

is-a

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).

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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

•


has skin


is pink, is edible


is tall



has feathers


is-a

is-a

is-a

is-a

is-a

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NETWORKS& MEANINGConcepts 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).•

C1C1’s attributes

C121C121’s attributes





is-a

is-a

is-a

is-a

is-a

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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.

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NETWORKS& MEANINGType Hierarchies

Lattices: Sharon is an experimental physicist and is a professional singer

Person

Artist Scientist

Performer Musician Theorist Experimentalist

Singer Physicist

Lecturer

Sharon

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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

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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

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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.

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NETWORKS& MEANING

Schema and Frames

The term frames appears to have at least five senses in The Oxford Dictionary of Computing:

1. The total amount of information presented in a display at any one time.

2. ……………………………………………..

5. 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

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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.

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NETWORKS& MEANING

Schema and FramesLiving objects 1 & 2 can be described as follows:

Opus Penguin member subset

Birds

Animal

subset

Bill Cat member subset Mammalssubset

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NETWORKS& MEANING

Schema and FramesEvents 3 & 4 can be described as follows:

ChichesterFlood Flood

a kind ofDisaster

Event

a kindof

Sophie & EdwardsWedding

Wedding Celebration

a kind of

a kindof a kind of a kind of

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NETWORKS& MEANING

Schema and FramesPenguin

Opus

member

Billlikes

Penguin

Opus

member

Billlikes

A semantic network

A frame network

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NETWORKS& MEANING

Schema and Frames: Representing Instances

Penguin

Opusmember

likes

subset

flight

Bill

Birds

Legs

flight

Animalsubset

vitality

subset

NoYes

2

flight No

Yes

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NETWORKS& MEANING

Schema and Frames: Handling ExceptionsMammals

Legs

Feeds young

Animalsubset

vitality

subset

Yes

4

flight No

Yes

subset

Legs

flight Yes

2

Bats

Catssubset

Climbs trees Yes

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NETWORKS& MEANINGEventAKO

Time

Date

Place

Disaster

AKO

Damage

Fatalities

Earthquake

AKO

Magnitude

Fault

FloodAKO

Depth

Area

FireAKO

Engines

Firemen

Inheritance of

Properties

Documents

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