Abdul Rahim Ahmad MITM 613 Intelligent System Chapter 1: Introduction To Intelligent Systems

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 Abdul Rahim Ahmad 2

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. Abdul Rahim Ahmad 3

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. Abdul Rahim Ahmad 4

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 knowledge- based Models)  numerical approaches such as neural networks, genetic algorithms, and fuzzy logic (implicit numerical or computational Models).  Can also be a hybrid of different approaches. Abdul Rahim Ahmad 5

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. Abdul Rahim Ahmad 6

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)  IR may also be a knowledge-based system containing metaknowledge (how to apply the domain knowledge). Abdul Rahim Ahmad 7

Knowledge-based Systems Abdul Rahim Ahmad 8

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 Abdul Rahim Ahmad 9

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. Abdul Rahim Ahmad 10

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 Abdul Rahim Ahmad 11

Example - a payroll system  Consider the facts :  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. Abdul Rahim Ahmad 12 /* Fact 1.1 */ Joe Bloggs works for ACME /* Rule 1.1 */ IF ?x works for ACME THEN ?x earns a large salary

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)  Default (to be used in the absence of transient fact) Abdul Rahim Ahmad 13

Examples  Facts about my car  Fact can be  attribute (properties of objects or classes)  relationship Abdul Rahim Ahmad 14

Attributes and relationships  Can be represented as a network (associative or semantic network)  Here, attributes = relationships. Abdul Rahim Ahmad 15

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.  The derived fact may satisfy, or partially satisfy, another rule, such as: Abdul Rahim Ahmad 16 /* 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 /* Derived Rule 1.2 */ IF ?x earns a large salary OR ?x has job satisfaction THEN ?x is professionally content

Inference Network  The derived fact may satisfy, or partially satisfy, another rule, such as:  Rules 1.1 and 1.2 are interdependent, since the conclusion of one can satisfy the condition of the other.  The interdependencies amongst the rules define the inference network Abdul Rahim Ahmad 17 /* 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

Inference Network  The interdependencies amongst the rules define the inference network Abdul Rahim Ahmad 18 /* 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

Cause and Effect  Inference network are used to link cause and effect.  Using the inference network we can make:  Deduction.  Abduction.  Induction Abdul Rahim Ahmad 19 IF THEN 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

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. Abdul Rahim Ahmad 20

Inference Engines  Two types of inference engines  forward-chaining ( data-driven )  backward-chaining ( goal-driven)  Abdul Rahim Ahmad 21 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. 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.

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. Abdul Rahim Ahmad 22

Declarative Programming  Examples of declarative programming:  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. Abdul Rahim Ahmad 23 /* 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 */

Procedural Programming  C is a procedural language - contains explicit step-by- step instructions telling the computer to perform actions: Abdul Rahim Ahmad 24 /* A program in C to read 10 integers from a file and */ /* print them out */ #include 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); }

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. Abdul Rahim Ahmad 25

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. Abdul Rahim Ahmad 26

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. Abdul Rahim Ahmad 27

Search Tree Abdul Rahim Ahmad 28 Search Tree Depth-first Search Breadth-first Search

Heuristic Search  Search can be improved by pruning – using heuristic search.  Ensure that the most likely alternatives are tested before less likely ones. Abdul Rahim Ahmad 29

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). Abdul Rahim Ahmad 30

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. Abdul Rahim Ahmad 31

Categories of Intelligent Systems Abdul Rahim Ahmad 32

Computational Intelligence Techniques Abdul Rahim Ahmad 33

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