1. Knowledge Representation
Peggy Israel Doerschuk
Knowledge Rep

2. Requirements adequately reflect the types of knowledge needed
Knowledge Rep
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 knowledge
promote efficient processing of the information
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3. Common representation schemes
Knowledge Rep
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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4. Common representation schemes cont.
Knowledge Rep
Common representation schemes cont.
relational representation
relational databases
knowledge represented by tuples or records
languages like Structured Query Language (SQL) used to manipulate data
hierarchical databases
allow links between related groups of data
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5. Common schemes cont. structured representation
Knowledge Rep
Common schemes cont.
structured representation
frames, scripts, object databases, object-oriented programming languages
knowledge is inheritable
groups similar objects together
compact representation
allows reasoning at different levels of abstraction
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6. Semantic Networks (Quillian)
Knowledge Rep
Semantic Networks (Quillian)
Models human information storage and retrieval
association of concepts
hierarchical organization - info is stored at its most abstract level
canary 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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7. Semantic Networks cont.
Knowledge Rep
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 circles
arcs are represented as directed arrows
Examples: p. 202 of Luger, p. 65 of Bigus, other examples in Rich
strength: inferencing via links, inheritance, flexibility
weakness: too unconstrained
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8. Conceptual Dependency
Knowledge Rep
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 of Luger
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9. Scripts (Schank and Abelson)
Knowledge Rep
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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10. Knowledge Rep
Components of a script
Entry conditions - must be true for script to be entered
results - true when script is exited
props
roles
scenes
ex: Fig 6.11
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11. Knowledge Rep
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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12. Frames cont. Let complex object be represented by a single frame
Knowledge Rep
Frames cont.
Let complex object be represented by a single frame
good for representing classes, inheritance, default values
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13. Conceptual graphs John Sowa (1984)
Knowledge Rep
Conceptual graphs John Sowa (1984)
two types of nodes in the graph
concepts (concrete or abstract)- boxes
relations - ellipses
arcs connect concepts to relations
each 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
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14. Operations on conceptual graphs
Knowledge Rep
Operations on conceptual graphs
create a new graph by either specializing or generalizing an existing graph
copy
restrict - replace concept node with specialization
generic marker replaced by individual marker
type label replaced by subtype
join
simplify
Fig 6.22
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15. Propositional nodes in conceptual graphs
Knowledge Rep
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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16. Subsumpition Architecture
Knowledge Rep
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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17. Three-layered subsumption architecture
Knowledge Rep
Three-layered subsumption architecture
Each layer has a network of FSMs
FSMs run asynchronously, sending and receiving messages
no central control; each FSM is driven by the messages it receives
Fig 6.26
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18. Limitations of subsumption architecture
Knowledge Rep
Limitations of subsumption architecture
Myopic - each level sees only local info
no model of the complete environment means no ability to determine globally acceptable actions
no learning
can it scale to very large, complex systems?
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19. Agent-Based and Distributed Problem Solving
Knowledge Rep
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
communicate
bid for subtasks
cooperate, coordinate
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20. Multi-agent problem solving
Knowledge Rep
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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21. Applications for agent-based problem solving
Knowledge Rep
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 systems
information management - info filtering, gathering on the internet, etc.
ecommerce - portfolio management, etc.
interactive games
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22. Knowledge Information Interchange (KIF)
Knowledge Rep
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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23. Knowledge Information Interchange cont
Knowledge Rep
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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24. Knowledge Information Interchange cont.
Knowledge Rep
Knowledge Information Interchange cont.
Variables
individual variables begin with ?, sequence variables begin
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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25. Building a Knowledge Base
Knowledge Rep
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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26. Research areas in intelligent agents
Knowledge Rep
Research areas in intelligent agents
How to decompose problem, synthesize results
interagent communication
how to ensure agents act coherently
coordination
resolving conflicts between agents
how to recognize, avoid chaotic behavior
how to allocate and manage resources
what are the best hardware, software platforms
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27. Representing Uncertainty
Knowledge Rep
Representing Uncertainty
Use statistical theory
probability of an event ranges from 0 to 1
unconditional probability P(heads) = 0.5
conditional probability is expressed as:
P(H|E) probability of hypothesis H given evidence E
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28. Representing Uncertainty cont.
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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
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