knowledge based expert system
DESCRIPTION
Learn the meaning of an expert systemUnderstand the problem domain and knowledge domainLearn the advantages of an expert systemUnderstand the stages in the development of an expert systemExamine the general characteristics of an expert systemExamine earlier expert systems which have given rise to today’s knowledge-based systemsExplore the applications of expert systems in use todayExamine the structure of a rule-based expert systemLearn the difference between procedural and nonprocedural paradigmsWhat are the characteristics of artificial neural systemsTRANSCRIPT
Chapter 1:
Introduction to Expert Systems
KNOWLEDGE BASED EXPERT SYSTEM
104/18/23
2
Objectives
• Learn the meaning of an expert system
• Understand the problem domain and knowledge domain
• Learn the advantages of an expert system
• Understand the stages in the development of an expert system
• Examine the general characteristics of an expert system
04/18/23
3
Objectives
• Examine earlier expert systems which have given rise to today’s knowledge-based systems
• Explore the applications of expert systems in use today
• Examine the structure of a rule-based expert system
• Learn the difference between procedural and nonprocedural paradigms
• What are the characteristics of artificial neural systems
04/18/23
4
Artificial Intelligence
• AI = “Making computers think like people.”
04/18/23
5
Areas of Artificial Intelligence
04/18/23
6
What is an expert system?
“An expert system is a computer system that emulates, or acts in all respects, with the decision-making capabilities of a human expert.”
Professor Edward Feigenbaum
Stanford University
• Expert Systems = knowledge-based systems = knowledge-based expert systems
04/18/23
7
What is an expert system?
• Emulation (mimics cause/process) is stronger than simulation (mimics outward appearance) which is required to act like the real thing in only some aspects.
• The basic idea is that if a human expert can specify the steps of reasoning by which a problem may be solved, so too can an expert system.
• Restricted domain expert systems (extensive use of specialized knowledge at the level of human expert) function well which is not the case of general-purpose problem solver.
04/18/23
8
Expert system technology may include:
• Special expert system languages – CLIPS
• Programs
• Hardware designed to facilitate the implementation of those systems (e.g., in medicine)
04/18/23
9
Expert System Main Components
• Knowledge base – obtainable from books, magazines, knowledgeable persons, etc; or expertise knowledge.
• Inference engine – draws conclusions from the knowledge base.
04/18/23
10
Basic Functions of Expert Systems
04/18/23
11
Problem Domain vs. Knowledge Domain
• In general, the first step in solving any problem is defining the problem area or domain to be solved.
• An expert’s knowledge is specific to one problem domain – medicine, finance, science, engineering, etc.
• The expert’s knowledge about solving specific problems is called the knowledge domain.
• The problem domain is always a superset of the knowledge domain.
• Expert system reasons from knowledge domain.04/18/23
12
Problem Domain vs. Knowledge Domain
• Example: infections diseases diagnostic system does not have (or require) knowledge about other branches such as surgery.
04/18/23
13
Problem and Knowledge Domain Relationship
04/18/23
14
Advantages of Expert Systems
• Increased availability: on suitable computer hardware
• Reduced cost
• Reduced danger: can be used in hazardous environment.
• Permanence: last for ever, unlike human who may die, retire, quit.
• Multiple expertise: several experts’ knowledge leads to• Increased reliability
04/18/23
15
Advantages Continued
• Explanation: explain in detail how arrived at conclusions.
• Fast response: (e.g. emergency situations).
• Steady, unemotional, and complete responses at all times: unlike human who may be inefficient because of stress or fatigue.
• Intelligent tutor: provides direct instructions (student may run sample programs and explaining the system’s reasoning).
• Intelligent database: access a database intelligently (e.g. data mining).
04/18/23
16
Representing the Knowledge
The knowledge of an expert system can be represented in a number of ways, including IF-THEN rules:
IF the light is red THEN stop
04/18/23
17
Representing the Knowledge Car Failure Diagnosis
IF the selection is 2 "Run-Stable State"AND the fuel is not burning wellAND the engine running cycle is okAND there is no blue gasAND the advance is bad
THENThere is a Dirt in the injections/carburetoror The adjustment of ear and gasoline is not good, clear injections/carburetor and adjust the ear.
04/18/23
18
Knowledge Engineering
The process of building an expert system:
1. The knowledge engineer establishes a dialog with the human expert to elicit knowledge.
2. The knowledge engineer codes the knowledge explicitly in the knowledge base.
3. The expert evaluates the expert system and gives a critique to the knowledge engineer.
04/18/23
19
Development of an Expert System
04/18/23
20
The Role of AI
• An algorithm is an ideal solution guaranteed to yield a solution in a finite amount of time.
• When an algorithm is not available or is insufficient, we rely on artificial intelligence (AI).
• Expert system relies on inference – we accept a “reasonable solution.”
04/18/23
21
Limitations of Expert Systems
• Uncertainty = having limited knowledge (more than possible outcomes)
• Both human experts and expert systems must be able to deal with uncertainty.
• Limitation 1: most expert systems deals with shallow knowledge than with deep knowledge.
• Shallow knowledge – based on empirical and heuristic knowledge.
• Deep knowledge – based on basic structure, function, and behavior of objects.
04/18/23
22
Limitations of Expert Systems
• Limitation 2: typical expert systems cannot generalize through analogy to reason about new situations in the way people can.
• Solution 1 for limitation 2: repeating the cycle of interviewing the expert.
• Limitation raised form Solution 1: A knowledge acquisition bottleneck results from the time-consuming and labor intensive task of building an expert system.
04/18/23
23
Early Expert Systems
• DENDRAL – used in chemical mass spectroscopy to identify chemical constituents
• MYCIN – medical diagnosis of illness
• DIPMETER – geological data analysis for oil
• PROSPECTOR – geological data analysis for minerals
• XCON/R1 – configuring computer systems
04/18/23
24
Broad Classes of Expert Systems
04/18/23
25
Problems with Algorithmic Solutions
• Conventional computer programs generally solve problems having algorithmic solutions.
• Algorithmic languages include C, Java, and C#.
• Classic AI languages include LISP and PROLOG.
04/18/23
26
Considerations for Building Expert Systems
• Can the problem be solved effectively by conventional programming? (expert systems are suited for ill-structured problems- problems with no efficient algorithmic solution)
• Is there a need and a desire for an expert system?
• Is there at least one human expert who is willing to cooperate?
• Can the expert explain the knowledge to the knowledge engineer in a way that can understand it.
• Is the problem-solving knowledge mainly heuristic and uncertain?
04/18/23
27
Languages, Shells, and Tools
• Expert system languages are post-third generation.
• Expert system languages (e.g. CLIPS) focus on ways to represent knowledge.
• Tool = language + utility program (code generator, graphics editor, debuggers, etc.).
• Shell: is a special purpose tool designed for certain types of applications in which the user must supply the knowledge base. Example, EMYCIN (empty MYCIN)
04/18/23
28
Elements of an Expert System
• User interface – mechanism by which user and system communicate.
• Exploration facility – explains reasoning of expert system to user.
• Working memory – global database of facts used by rules.
• Inference engine – makes inferences deciding which rules are satisfied and prioritizing.
04/18/23
29
Elements Continued
• Agenda – a prioritized list of rules created by the inference engine, whose patterns are satisfied by facts or objects in working memory.
• Knowledge acquisition facility – automatic way for the user to enter knowledge in the system bypassing the explicit coding by knowledge engineer.
04/18/23
30
Structure of a Rule-Based Expert System
04/18/23
31
Production Rules
• Knowledge base is also called production memory.
• Production rules can be expressed in IF-THEN pseudocode format.
• In rule-based systems, the inference engine determines which rule antecedents are satisfied by the facts.
04/18/23
32
Inference engine operates on recognize-act cycle
While not done
conflict resolution:
act:
match:
check for halt:
End-while
04/18/23
33
Inference engine operates on recognize-act cycle
- conflict resolution: if there are activations then select the one with the highest priority. Else done.
- act: sequentially perform the actions. Update the working memory. Remove the fired activations.
- match: Update the agenda by checking if there are activation or remove activations if there LHS is no longer satisfied.
- check for halt: if an halt action is performed or break command given, then done.
04/18/23
34
General Methods of Inferencing
• Forward chaining – reasoning from facts to the conclusions resulting from those facts – best for prognosis, monitoring, and control.
• Backward chaining – reasoning in reverse from a hypothesis, a potential conclusion to be proved to the facts that support the hypothesis – best for diagnosis problems.
04/18/23
35
Production Systems
• Rule-based expert systems – most popular type today.
• Knowledge is represented as multiple rules that specify what should/not be concluded from different situations.
• Forward chaining – start w/facts and use rules do draw conclusions/take actions.
• Backward chaining – start w/hypothesis and look for rules that allow hypothesis to be proven true.
04/18/23
36
Post Production System
• Basic idea – any mathematical / logical system is simply a set of rules specifying how to change one string of symbols into another string of symbols.
• Basic limitation – lack of control mechanism to guide the application of the rules.
04/18/23
37
Markov Algorithm
• An ordered group of productions applied in order or priority to an input string.
• If the highest priority rule is not applicable, we apply the next, and so on.
• An inefficient algorithm for systems with many rules.
04/18/23
38
Rete Algorithm
• Functions like a net – holding a lot of information.
• Much faster response times and rule firings can occur compared to a large group of IF-THEN rules which would have to be checked one-by-one in conventional program.
• Takes advantage of temporal redundancy and structural similarity.
• Drawback is high memory space requirements.
04/18/23
39
Procedural Paradigms
• Algorithm – method of solving a problem in a finite number of steps.
• Procedural programs are also called sequential programs.
• The programmer specifies exactly how a problem solution must be coded.
04/18/23
40
Procedural Languages
04/18/23
41
Imperative Programming
• Focuses on the concept of modifiable store – variables and assignments.
• During execution, program makes transition from the initial state to the final state by passing through series of intermediate states.
• Provide for top-down-design.
• Not efficient for directly implementing expert systems.
04/18/23
42
Nonprocedural Paradigms
• Do not depend on the programmer giving exact details how the program is to be solved.
• Declarative programming – goal is separated from the method to achieve it.
• Object-oriented programming – partly imperative and partly declarative – uses objects and methods that act on those objects.
• Inheritance – (OOP) subclasses derived from parent classes.
04/18/23
43
Nonprocedural Languages
04/18/23
44
What are Expert Systems?
Can be considered declarative languages:
• Programmer does not specify how to achieve a goal at the algorithm level.
• Induction-based programming – the program learns by generalizing from a sample.
04/18/23