Knowledge Rep1 Knowledge Representation Peggy Israel Doerschuk.

Knowledge Rep 1

Knowledge Representation

Peggy Israel Doerschuk

Copyright P. Doerschuk

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Requirements

adequately reflect the types of knowledge needed

allow new knowledge to be added and existing knowledge to be updated

permit the derivation of new knowledgepromote efficient processing of the

information


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Common representation schemes

Logical representation predicate logic, propositional logic

Procedural representation hard-coded sequential programs production systems

network representation graph representation - semantic nets,

conceptual dependencies, conceptual graphs


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Common representation schemes cont.

relational representation relational databases

knowledge represented by tuples or recordslanguages like Structured Query Language

(SQL) used to manipulate data

hierarchical databasesallow links between related groups of data


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Common schemes cont.

structured representation frames, scripts, object databases,

object-oriented programming languagesknowledge is inheritablegroups similar objects togethercompact representationallows reasoning at different levels of

abstraction


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Semantic Networks (Quillian)

Models human information storage and retrieval association of concepts hierarchical organization - info is stored

at its most abstract levelcanary is a type of bird; canary is yellow

and can fly• flying is stored with bird• traits specific to canary (yellow) are stored with

canary


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Semantic Networks cont.

consists of nodes that represent an object, concept or event and arcs that represent a relationship between two nodes

nodes are represented as rectangles or circlesarcs are represented as directed arrowsExamples: p. 202 of Luger, p. 65 of Bigus, other

examples in Richstrength: inferencing via links, inheritance,

flexibilityweakness: too unconstrained


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Conceptual Dependency Roger Schank (1974) models the deep semantic structure of natural

language uses primitive conceptualizations to represent

meaning primitives define conceptual dependency

relationships conceptual dependency relationships are

conceptual syntax rules used to construct internal representation of

English sentence p. 206-210 of Luger


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Scripts (Schank and Abelson)

used to represent common sequences of events

contains background information and a collection of slots used to describe the scenes

scenes are grouped into different tracks, depending on the particular situation

scripts are limited to common scenes and can't be used for novel situations


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Components of a script

Entry conditions - must be true for script to be entered

results - true when script is exitedpropsrolesscenesex: Fig 6.11


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Frames

consists of a collection of slots (attributes) and fillers (values) associated with the object of the frame

slots can contain descriptive information (data), procedural information (functions), and pointer information (references to other frames)

supports inheritance and inferencing frames are often linked to show has-a and is-a

relationships example p. 63 of Bigus, Fig 6.12 of Luger,

other examples in Rich frames can be represented as objects in OOP


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Frames cont.

Let complex object be represented by a single frame

good for representing classes, inheritance, default values


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Conceptual graphs John Sowa (1984)

two types of nodes in the graph concepts (concrete or abstract)- boxes relations - ellipses

arcs connect concepts to relationseach concept box has the name of the

type and the individual, separated by :markers are used to identify individuals

# followed by number generic marker * marks unspecified individual

Ex: Fig 6.15-6.20


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Operations on conceptual graphs

create a new graph by either specializing or generalizing an existing graph copy restrict - replace concept node with

specializationgeneric marker replaced by individual marker type label replaced by subtype

join simplify

Fig 6.22


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Propositional nodes in conceptual graphs

Propositional concepts are indicated as a box that contains another conceptual graph

represent modal logics (various ways propositions are entertained - believed, asserted as true, false, possible, probable, etc.)

ex: Tom believes that Jane lines pizza. Fig 6.24, 6.25


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Subsumpition Architecture

Rodney Brooks (1991) - intelligent behavior emerges from the interactions of architectures of organized simpler behaviors

subsumption architecture used for robot control collection of task-handling behaviors each behavior accomplished by a finite

state machine that maps perceptions to actions


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Three-layered subsumption architecture

Each layer has a network of FSMs FSMs run asynchronously, sending

and receiving messagesno central control; each FSM is

driven by the messages it receivesFig 6.26


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Limitations of subsumption architecture

Myopic - each level sees only local infono model of the complete environment

means no ability to determine globally acceptable actions

no learningcan it scale to very large, complex

systems?


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Agent-Based and Distributed Problem Solving

Characteristics of intelligent agent system: Situated - interacts with its environment autonomous - acts independently flexible - both responsive and proactive (goal

directed) social - interacts with other agents

communicatebid for subtaskscooperate, coordinate


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Multi-agent problem solving

Problems are solved by multiple agents cooperating together, dividing and sharing knowledge of the problem each agent has incomplete info no global controller knowledge is decentralized reasoning processes are often

asynchronous


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Applications for agent-based problem solving

Manufacturing - modeled as hierarchy of work areas

automated control - transportation systems, air traffic control, etc.

telecommunications - network control, transmission and switching, etc.

transportation systemsinformation management - info filtering,

gathering on the internet, etc.ecommerce - portfolio management, etc.interactive games


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Knowledge Information Interchange (KIF)

Results from efforts of Defense Advanced Research Projects Agency Knowledge Sharing Environment workgroup

Designed to provide a common format for exchanging knowledge between agents

based on predicate logic, syntax similar to LISP

supports definition of objects, functions, relations, rules, and metaknowledge ( knowledge about knowledge)


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Knowledge Information Interchange cont

a KIF knowledge base is a collection of forms

A form is either a sentence, a rule, or a definition


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Knowledge Information Interchange cont.

Variables individual variables begin with ?, sequence variables

begin with @ expressions

terms - objects; sentences - facts; definitions - constants; rules - inferencing steps

(=> (EventName “AGENT:STARTING”)(SetIdentifiedIntervalAlarm “NETSCAPE” 20 “minutes”)

If we get an AGENT:STARTING event, start an alarm called NETSCAPE to go off every 20 minutes.

operators term, rule, sentence, definition operators

constants numbers, characters, strings, objects, functions,

relations, logical constants


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Building a Knowledge Base

The symbolic approach: Knowledge engineer gathers knowledge from domain expert(s) and represents it in a form used by the reasoning system expert must represent knowledge explicitly knowledge acquisition bottleneck

the subsymbolic approach: expert networks use neural network to learn to perform classification and prediction tasks knowledge is encoded in weights between neurons


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Research areas in intelligent agents

How to decompose problem, synthesize results

interagent communicationhow to ensure agents act coherentlycoordinationresolving conflicts between agentshow to recognize, avoid chaotic behaviorhow to allocate and manage resourceswhat are the best hardware, software

platforms


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Representing UncertaintyUse statistical theory probability of an event ranges from 0 to 1unconditional probability P(heads) = 0.5conditional probability is expressed as:

P(H|E) probability of hypothesis H given evidence E


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Representing Uncertainty cont.

Bayes’ theorem: P(Y|X) = P(X|Y)P(Y)/P(X)

Bayesian network a directed acyclic graph each node represents a variable and a

conditional probability table defining relationships between parent nodes

uses probability to reason with uncertainty

Knowledge Rep1 Knowledge Representation Peggy Israel Doerschuk.

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