Knowledge
Representation
(Topic 6)
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
Knowledge Representation
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Definition
Categories of AI Representation
Issues in Knowledge Representation
Semantic Network
Frames
Conceptual Graph
Agent-based
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.
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
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)
STRUCTURED REPRESENTATION
SCHEMES
SEMANTIC NETWORKS
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)
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
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
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?
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
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 breadthfirst fashion, searching for
intersection-node
STRUCTURED REPRESENTATION
SCHEMES
FRAMES
KR:Frames
• Another representational scheme
• Implicit connection of information
• Static data structure to represent wellunderstood 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
Frames : Features
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.
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
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
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.
MODERN REPRESENTATION
SCHEMES
CONCEPT GRAPHS and
AGENTS
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)
Conceptual relations of different arities.
•Each conceptual
graph represents a
single proposition
Conceptual
nodes
•A typical knowledgebase contains several
graphs
•Graph must be finite
•i.e. a dog has a color
of brown
Conceptual
relation
•Conceptual graphs are
used to model
semantics of natural
language
Parents is a 3-ary relation
Graph of “Mary gave John the book.”
type
ind
•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 :
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
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
Conceptual graph of the sentence “The dog scratches its
ear with its paw.”
*Generic marker
-Unspecified
individual
•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
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
Generalization and Specialization:
Examples of restrict, join, and simplify operations
•Replace type with
subtypes; animal ->
dog
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
Ж restrict ~ match two concepts
Ж join and restrict ~ allow
implementation of inheritance
Generalization and Specialization: Examples of restrict, join, and
simplify operations
Conceptual graph of the proposition “There are no pink dogs.”
To represent negation or disjunctionvariable 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 specialpurpose inferencing
mechanisms such as join and
restrict, not normally part of
predicate calculus
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
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.

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
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
A network agent workstation named NetAgt, defined on MasterB to manage internetwork dependencies on jobs or job streams defined inNetwork A
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.
Local Network
Master B
Remote network
Master A
Master
A
•
Network
agent
Master
B
A network agent workstation named NetAgt, defined on MasterB to manage
internetwork dependencies on jobs or job streams defined inNetwork A
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
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)
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
–
Intelligent Agent
4 criteria of intelligent agent
Situated, autonomous, flexible and social
Agents : Criteria (1)
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 (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
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
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?
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
Agents vs. Objects
Differences between OBJECTs and
AGENTs
OBJECT
AGENT
 invoke methods on one another
 request action to be
perfomed
 defined as computational systems
with encapsulated state
 designed to have flexible
 have methods associated with
state
 have own thread of controls
 communicate by message passing
 rarely exhibit control over own
behavior
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
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
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