Post on 06-Jan-2016
description
Knowledge Rep 1
Knowledge Representation
Peggy Israel 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