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Knowledge
Representation
(Topic 6)
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Course Contents
Again..Selected topics for our course. Covering all of AI is impossible!
Key topics include:
Introduction to Artificial Intelligence (AI)
Knowledge Representation and Search
Introduction to AI Programming
Problem Solving Using Search
Exhaustive Search Algorithm
Heuristic Search
Techniques and Mechanisms of Search Algorithm
Knowledge Representation Issues and Concepts
Strong Method Problem Solving
Soft Computing and Machine Learning
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Knowledge Representation
• Definition
• Categories of AI Representation
• Issues in Knowledge Representation
• Semantic Network
• Frames
• Conceptual Graph
• Agent-based
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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: 45)
• The representation principle:
– Once a problem is described using an appropriate
representation, the problem is almost solved.
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4 Categories of AI Representation
Mylopoulos and Levesque (1984)
• Logical representation schemes
– formal logic (covered)
• Procedural representation schemes
– Production rule system (covered)
• Network representation schemes
– Semantic network
• Structured representation schemes
– frames
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Issues - Knowledge Representation
• You need to represent a problem to solve
it on a computer.
• Implement into intelligent systems thru :
– Representation scheme (like data structures,
explicit structure for knowledge
representation)
– Representation medium (like programming
languages, i.e. PROLOG, LISP,C++,Java)
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STRUCTURED REPRESENTATION
SCHEMES
SEMANTIC NETWORKS
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KR: Semantic Network
• Graph- explicitly representing relations using arcs
and nodes; formalizing knowledge
• Semantic network
– represents knowledge as a graph
– with nodes corresponding to facts or concepts
– and arcs correspond to relations between concepts
– Represented as pairs of object and value linked by
attribute
– Eg. Figure 1(canary-bird) and Figure 2(snow-ice)
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Figure 1: Semantic network developed by Collins and Quillian in their research
on human information storage and response times (Harmon and King 1985).
•Knowledge
organization
•Inheritance
systems allow:-
-storing knowledge
at highest level of
abstraction
-reduce size of
knowledge base
-help prevents
update
inconsistencies
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Figure 2: Network representation of properties of snow and ice
•Semantic network can
be used to:-
-answer questions
about snow,ice and
snowmen(with
appropriate inference
rules)
-References are made
by following links to
concept
-implement inheritance
i.e. frosty inherits all
properties of snowman
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Construct a semantic network with this information, along
with exceptions (where necessary).
Mammals are warm-blooded. Mammals have 4 legs.
Tigers eat meat. Tigers are mammals. Tigers are dangerous.
Hobbs is a tiger. Hobbs is not dangerous.
Raja is a tiger. Raja has three legs.
Using this hierarchy, answer the following.
(a) Which are the dangerous tigers?
(b) How many legs does Raja have?
(c) How many links have to be traversed in the hierarchy to check
if Hobbs is warm-blooded?
(d) Are all mammals dangerous?
(e) What facts can you deduce about Raja from the hierarchy?
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Three planes representing three definitions of the word “plant” (Quillian
1967).
•A program defined
English word
•Each definition leads to
other definition in
unstructured or circular
fashion
•Looking up a word, the
network is traversed until
the word is understood
•Knowledge-based is
organized into planes,
each explains single
word
•i.e. 3 planes capture
definitions of ‘plant’-living
organism,work place or
putting seed in ground
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Intersection path between “cry” and “comfort” (Quillian 1967).
•Knowledge-based is used
to find relationships
between pairs of english
word
•Given two words, it would
search graphs outward
from each word in breadth-
first fashion, searching for
intersection-node
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STRUCTURED REPRESENTATION
SCHEMES
FRAMES
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KR:Frames
• Another representational scheme
• Implicit connection of information
• Static data structure to represent well-
understood stereotyped situations
• Organize our knowledge of the world
• We adjust to new situation by calling up
information structure by past experience
• We then revise details of past experiences to
represent differences in new situation
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Frames : Features
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How frames are organised
• A frame system is a hierarchy of frames
• The idea of frame hierarchies is very similar to the
idea of class hierarchies found in object-orientated
programming.
• Each frame has: – a name.
– slots: these are the properties of the entity that has the name, and they have values. A particular value may be:
• a default value • an inherited value from a higher frame • a procedure, called a daemon, to find a value • a specific value, which might represent an exception.
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Part of a frame description of a hotel room. “Specialization” indicates a
pointer to a superclass.
•Hotel room and its
components are described
by number of individual
frames
•Each frame may be seen
as data structure
•Contains info relevant to
stereotyped entities
•Frame systems support
class inheritance
Eg.
Procedural
attachment
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Spatial frame for viewing a cube (Minsky 1975).
•This frame system
represents four of faces
of cube
•Broken line indicates
face out of view from
that perspective
•Links between frames
indicate relations
between views
represented by frames
•Each slot in one frame
could be a pointer to
another entire frame
•Since given information
can fill many different
slot (face E), No
redundancy in
information stored
•Frames allow complex objects to be represented as
a single frame rather than large network structure
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QUIZ:
Represent the following as a set of frames.
The aorta is a particular kind of artery which
has a diameter of 2.5cm. An artery is a kind of
blood vessel. An artery always has a muscular
wall, and generally has a diameter of 0.4cm. A
vein is a kind of blood vessel, but has a fibrous
wall. Blood vessels all have tubular form and
contain blood.
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MODERN REPRESENTATION
SCHEMES
CONCEPT GRAPHS and
AGENTS
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Conceptual Graphs
• Example of a network representation language
• A finite, connected graph
• Nodes are either: – concepts or
– conceptual relations
– No labeled arcs
– Conceptual relation nodes=relations between concepts
– i.e. (dog and brown ~ concept nodes)
– i.e. (color ~ conceptual relation)
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Conceptual relations of different arities.
Conceptual
nodes
Conceptual
relation
Parents is a 3-ary relation
•Each conceptual
graph represents a
single proposition
•A typical knowledge-
base contains several
graphs
•Graph must be finite
•i.e. a dog has a color
of brown
•Conceptual graphs are
used to model
semantics of natural
language
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Graph of “Mary gave John the book.”
•The graph uses conceptual relations to represent cases of the verb ‘to give’
•Conceptual graphs used to model semantics of natural language
•Every concept is a unique individual of a particular type, separated by :
type ind
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Conceptual graph indicating that the dog named emma is brown.
Conceptual graph indicating that a particular (but unnamed) dog is brown.
Conceptual graph indicating that a dog named emma is brown.
Graph
1= 3
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Conceptual graph of a person with three names.
#
marker
# Marker
• is unique and different from names
•Individual has many names but one marker
•different individuals may have same name, but not same marker
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Conceptual graph of the sentence “The dog scratches its
ear with its paw.”
•To summarize: each concept node indicates individual of specified type
•This individual is the referent of the concept
•Individual concept ~referent uses individual marker
•Generic concept ~ referent uses generic marker
*Generic marker
-Unspecified
individual
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Generalization and Specialization:
Examples of restrict, join, and simplify operations
Conceptual graph
includes operations
creating new graphs
from existing ones:
-specializing or
generalizing existing
graph to represent
semantics of natural
language:-
1)Copy-exact copy of
graph
2)Restrict-replace
concept nodes with
specialized note:
• generic marker
replace individual
marker
•Replace type with
subtypes; animal ->
dog
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3)Join- combine 2 graphs into single one
• If concept node c1 and c2 identical, delete c2; c1 replace c2
• Specialization- produce less general graph
4)Simplify-if graph has duplicate relations, delete one together with its arcs
• Occur after join operation
Generalization and Specialization: Examples of restrict, join, and
simplify operations
Ж restrict ~ match two concepts
Ж join and restrict ~ allow
implementation of inheritance
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Conceptual graph of the proposition “There are no pink dogs.”
To represent negation or disjunction-
variable quantification
(universal quantifier (for all) and
existential quantifier (there exist)
neg – takes argument as proposition
concept and assert that concept as false
In conceptual graph, generic concepts are
assumed to be existentially quantified
Eg. Translations
X Y (dog(X) color(X,Y) brown(Y)
==> existential quantifier
X Y ( (dog(X) color(X,Y) pink(Y)))
==> universal quantifier
Eg. Translation
X1 (dog(emma) color(emma, X1)
brown(X1))
There is straight mapping
from conceptual graph into
predicate calculus notation
(Sowa,1984)
Advantage of conceptual
graph – support some special-
purpose inferencing
mechanisms such as join and
restrict, not normally part of
predicate calculus
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On a piece of paper , answer the questions below and submit
•Translate the two conceptual graphs into English
• Translate the two conceptual graphs into predicate calculus
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Agent-based Representation
• Definition:
Agent ~ agent-based-system ~ multi-agent
system
multi-agent :- a comp program with problem
solvers situated in interactive environments,
capable of flexible, autonomous and socially
organized actions.
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Definition of agent
• An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators
• Human agent: eyes, ears, and other organs for sensors;
Hands,legs, mouth, and other body parts for actuators
• Robotic agent: cameras and infrared range finders for sensors;
various motors for actuators
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Definition of agent in CS and AI
• Generally, an agent is one who acts for, or in the place of, another,
by authority from him; one entrusted with the business of another.
• Agent architecture, blueprint for software agents and intelligent
control systems, depicting the arrangement of components
• Agent-based model, computational model for simulating the actions
and interactions of autonomous individuals with a view to assessing
their effects on the system as a whole. It consists of agents that
interact within an environment.
• Intelligent agent, autonomous entity which observes and acts upon
an environment and directs its activity towards achieving goals
• Software agent, piece of software that acts for a user or other
program in a relationship of agency
– Forté Agent, email and Usenet news client used on the Windows
operating system
– User agent, the client application used with a particular network
protocol
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Example of network agent
• The following example shows how to define a network agent workstation for
a remote network, Network A, that allows local network,Network B, to use
jobs and job streams in the remote network as internetwork dependencies.
• A network agent workstation named NetAgt, defined on MasterB to manage
internetwork dependencies on jobs or job streams defined inNetwork A
Master
A
Master
B Network
agent
A network agent workstation named NetAgt, defined on MasterB to manage internetwork dependencies on jobs or job streams defined inNetwork A
Remote network
Master A
Local Network
Master B
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Agents and environments
• The agent function maps from percept histories to actions:
[f: P* A]
• The agent program runs on the physical architecture to produce f
• agent = architecture + program
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Rational agents
• Rationality is distinct from omniscience (all-knowing with infinite knowledge)
• Agents can perform actions in order to modify future percepts so as to obtain useful information (information gathering, exploration)
• An agent is autonomous if its behavior is determined by its own experience (with ability to learn and adapt)
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PEAS
• Must first specify the setting for intelligent agent design
•
• Consider, e.g., the task of designing an automated taxi driver:
•
– Performance measure: Safe, fast, legal, comfortable trip, maximize profits
–
– Environment: Roads, other traffic, pedestrians, customers
–
– Actuators: Steering wheel, accelerator, brake, signal, horn
–
– Sensors: Cameras, sonar, speedometer, GPS, odometer, engine sensors, keyboard
–
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Intelligent Agent
4 criteria of intelligent agent
Situated, autonomous, flexible and social
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Criteria 1: Situatedness
Means agent receives input from environment
in which it is active and can also effect
changes within that environment
i.e. situations like internet, game playing,
robotic situation
i.e. ROBOCUP competition – agent interact
with ball and opponent
Agents : Criteria (1)
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Agents : Criteria (2)
Criteria 2 – autonomous
Can interact with its environment without
direct intervention of other agents
Control over its own action
Can also learn from experience to improve
performance
i.e. ROBOCUP – agent pass the ball to a
teammate or kick on goal depending on its
situation
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Agents : Criteria (3)
Criteria 3 – flexible
Intelligently responsive – receive stimuli from its environment and responds to them in an appropriate and timely fashion
proactive – not simply responsive but able to be opportunistic, goal directed and have appropriate alternatives for various situations
i.e. soccer agent –change its dribble depending on the challenge pattern of opponent
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Agents : Criteria (4)
Criteria 4: social
Interact with other software or human-agent
towards the goal
social dimension address difficult situation
i.e. ROBOCUP – to score a goal, how one
agent support another agent’s goal?
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Multi-Agents
4 characteristics of multi-agent problem
solving (Jenning et. Al 1998)
1st – each agent has incomplete information
and capabilities to solve entire problem
2nd – no global system controller for entire
problem solving
3rd – knowledge and input data for the
problem is decentralized
4th – the reasoning process are asynchronous
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Agents vs. Objects
Differences between OBJECTs and AGENTs
OBJECT
invoke methods on one another
defined as computational systems
with encapsulated state
have methods associated with
state
communicate by message passing
rarely exhibit control over own
behavior
AGENT
request action to be
perfomed
designed to have flexible
have own thread of controls
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Agents : Applications (1)
manufacturing – manage orders, inventory, production sequence, manufacturing operations
automated control – controlling transportation system, spacecraft control and air traffic control
telecommunication – require real-time monitoring and management, i.e. network control and management, transmission and switching
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Agents : Applications (2)
Transportation systems – distributed, situated and autonomous, i.e. coordinate carpooling and transport scheduling
Information management – i.e. internet , information filtering and info. Gathering like mail filtering
E-commerce – make buy and sell decisions i.e. shopping assistance, interactive catalogue
Interactive Games and Theater – i.e. war games, finance management scenarios or sport
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KR : Conclusion
• Intelligent software design skills are necessary to support agent prob solving technology in creating agent architecture, i.e.
– Representational requirements
– Search issues
– Planning
– Stochastic agents reasoning
– Learning..natural language understanding