Download 1. Introduction to Intelligent Systems

MITM 613
Intelligent System
Chapter 1: Introduction To
Intelligent Systems
Abdul Rahim Ahmad
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Contents
 Intelligent systems
 Knowledge-based systems
 The knowledge base
 Deduction, abduction, and induction
 The inference engine
 Declarative and procedural programming
 Expert systems
 Knowledge acquisition
 Search
 Computational intelligence
 Integration with other software
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Intelligent System
 Intelligence – A system’s comparative level of
performance in reaching its objectives i.e: having
experiences where the system learned which actions best
let it reach its objectives. (Likewise: a person is not
intelligent in all areas of knowledge, only in areas where
they had experiences).
 System - Part of the universe, with a limited extension in
space and time. Outside the system, is the environment.
 Intelligent System - A system that learns how to act so
that it can reach its objectives.
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Definition of Intelligent System
 A system that learns during its existence. (In
other words, it senses its environment and
learns, for each situation, which action permits it
to reach its objectives.) and it continually acts,
mentally and externally, and by acting reaches
its objectives more often than pure chance
indicates (normally much oftener). It consumes
energy and uses it for its internal processes, and
in order to act.
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Intelligent System
 A broad term, covering a range of computing
techniques within artificial intelligence.
 Includes
 symbolic approaches in which knowledge is explicitly
expressed in words and symbols (explicit knowledgebased Models)
 numerical approaches such as neural networks,
genetic algorithms, and fuzzy logic (implicit numerical
or computational Models).
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 Can also be a hybrid of different approaches.
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Focus of this Course
 Discuss issues encountered in the development
of applied systems.
 Describe a wide range of intelligent systems
techniques with realistic problems in engineering
and science.
 Will look at:
 Techniques of intelligent systems.
 A few categories of applications and the design and
implementation issues.
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Knowledge-based Systems
 A system can be built in a conventional manner
 Where domain knowledge is intimately intertwined
with software for controlling the application of that
knowledge.
 But, in a knowledge-based system, the
knowledge module and the the control module
are explicitly separated.
 The knowledge module is called the knowledge base
 The control module is called the inference engine (IR)
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 IR may also be a knowledge-based system containing
metaknowledge (how to apply the domain knowledge).
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Knowledge-based Systems
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Conventional vs Knowledge-based
 Separating knowledge from control allows easier
addition of new knowledge (during program
development or from experience)
 To change a program behavior;
 In conventional approach, program control structures
needs to be changed resulting in changing some other
aspect of the program’s behavior.
 In knowledge-based approach, knowledge is
represented explicitly in the knowledge base, not
implicitly within the structure of a program
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Knowledge-based Systems
 Knowledge can be altered with ease.
 The inference engine uses the knowledge base
to solve a problem similar to using a
conventional program a data file.
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The knowledge base
 Contains rules and facts.
 Facts may include
 Sequences
 Structured entities
 Attributes of entities
 Relationships between entities
 Representation of rules and facts vary from
system to system
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Example - a payroll system
 Consider the facts :
/* Fact 1.1 */ Joe Bloggs works for ACME
/* Rule 1.1 */ IF ?x works for ACME THEN ?x earns a large salary
 In conventional program
 The fact and the rule are “hard-wired,” so that they
become an intrinsic part of the program.
 In knowledge-based system
 The rule and the fact are represented explicitly and
can be changed at will.
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Rules and Facts
 Rules can be uncertain.
 Uncertainty can arise from three distinct sources
 uncertain evidence
 uncertain link between evidence and conclusion
 vague rule
 Facts can be
 Static (facts that change sufficiently infrequently)
 Transient (apply at a specific instance only while the
system is running)
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 Default (to be used in the absence of transient fact)
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Examples
 Facts about my car
 Fact can be
 attribute (properties of objects or classes)
 relationship
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Attributes and relationships
 Can be represented as a network
(associative or semantic network)
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 Here, attributes = relationships.
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Facts
 Facts are made available to the knowledge-based system
statically or in transient. Both are given facts.
 Derived fact is generated fact:
 One or more given facts may satisfy the condition of a rule
generating derived fact.
/* Fact 1.1 */ Joe Bloggs works for ACME
/* Rule 1.1 */ IF ?x works for ACME THEN ?x earns a large salary
Applying Rule 1.1 to Fact 1.1, we can derive:
/* (Derived) Fact 1.2 */ Joe Bloggs earns a large salary
 The derived fact may satisfy, or partially satisfy, another
rule, such as:
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/* Derived Rule 1.2 */
IF ?x earns a large salary OR ?x has job satisfaction THEN ?x is
professionally content
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Inference Network
 The derived fact may satisfy, or partially satisfy,
another rule , such as:
/* Rule 1.1 */ IF ?x works for ACME THEN ?x earns a large salary
/* Derived Rule 1.2 */
IF ?x earns a large salary OR ?x has job satisfaction THEN ?x is
professionally content
 Rules 1.1 and 1.2 are interdependent, since the
conclusion of one can satisfy the condition of the
other.
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 The interdependencies amongst the rules define
the inference network
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Inference Network
 The interdependencies amongst the rules define
the inference network
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/* Rule 1.1 */ IF ?x works for ACME THEN ?x earns a large salary
/* Derived Rule 1.2 */
IF ?x earns a large salary OR ?x has job satisfaction THEN ?x is professionally
content
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Cause and Effect
 Inference network are used to link cause and
effect.
IF <cause> THEN <effect>
 Using the inference network we can make:
 Deduction.
 Abduction.
 Induction
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if Joe Bloggs works for ACME and is in a stable relationship
(the causes) then he is happy (the effect).
Reasoning in the reverse direction, i.e., we wish to ascertain a
cause, given an effect.
If Joe Bloggs is happy, we can infer by abduction that Joe
Bloggs enjoys domestic bliss and professional contentment.
Inferring a rule from a set of example cases of cause and
effect
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Inference Network
 The inference network represents a closed world
 Each node represents a possible state of some aspect
of the world
 A model of the current overall state of the world
is maintained.
 Can determine the extent of the relationships between
the nodes.
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Inference Engines
 Two types of inference engines
 forward-chaining (data-driven )
A knowledge based system working in data-driven
mode takes the available information (the “given”
facts) and generates as many derived facts as it can.
 backward-chaining (goal-driven)

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For tightly focused solution. It is also a lazy kind of inference.
It does no work until absolutely necessary, in distinction to
forward chaining, where the system eagerly awaits new facts
and tries applying conditions as soon as they arrive.
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Declarative Programming
 In knowledge-based system
 knowledge is separated from reasoning.
 programmer expresses information about the problem
to be solved.
 Often this information is declarative, i.e., the programmer
states some facts, rules, or relationships without having to be
concerned with the detail of how and when that information
is applied.
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Declarative Programming
 Examples of declarative programming:
/* Rule 1.3 */
IF pressure is above threshold THEN close valve
/* Fact 1.3 */
valve A is shut /* a simple fact */
/* Fact 1.4 */
valve B is connected to tank 3 /* a relation */
 Each is a part of a knowledge base.
 Inference engine is procedural — obeying a set
of sequential commands (extract and use
information from the knowledge base).
 The how, when, and if the knowledge should be
used are implicit in the inference engine.
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Procedural Programming
 C is a procedural language - contains explicit step-bystep instructions telling the computer to perform actions:
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/* A program in C to read 10 integers from a file and */
/* print them out */
#include <stdio.h>
FILE *openfile;
main()
{ int j, mynumber;
openfile = fopen("myfile.dat", "r");
if (openfile == NULL)
printf("error opening file");
else
{
for (j=1; j<=10; j=j+1)
{
fscanf(openfile,"%d",&mynumber);
printf("Number %d is %d\n", j, mynumber);
}
fclose(openfile);
}
}
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Expert System
 A knowledge-based system
 Mirror a human consultant - offers advice,
suggestions, or recommendations.
 Capable of justifying its line of inquiry and
explaining its reasoning in a conclusion.
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Knowledge acquisition
 3 approaches to acquire knowledge for a
particular domain:
 Teased out of a domain expert by someone else.
 Build by a domain expert him/her self.
 Knowledge learned automatically from examples.
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Search
 Most AI applications involve searching through
the possible solutions (search space) to find one
or more that are optimal or satisfactory.
 In knowledge-based system, inference engine
search the rules and facts to apply.
 Search can be : exhaustive search or systematic
search (depth first and breadth-first) using
search tree.
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Search Tree
Search Tree
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Depth-first Search
Breadth-first Search
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Heuristic Search
 Search can be improved by pruning – using
heuristic search.
 Ensure that the most likely alternatives are
tested before less likely ones.
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Computational intelligence
 Knowledge-based system used symbols to
explicitly build knowledge that form rules, facts,
relations, or other forms of knowledge
representation.
 Computational intelligence (CI) or soft
computing method represents knowledge by
numbers which are adjusted as the system
improves its accuracy (knowledge is not explicitly
stated).
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Examples of Computational
intelligence
 Neural networks.
 Genetic algorithms or, more generally,
evolutionary algorithms.
 Probabilistic methods such as Bayesian updating
and certainty factors.
 Fuzzy logic.
 Combinations of these techniques with each
other and with KBSs.
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Categories of Intelligent Systems
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Computational Intelligence Techniques
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END
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