Expert System With Python -1
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The document introduces expert systems, focusing on knowledge representation techniques such as rules, frames, and semantic nets, and outlines the roles of the development team. It discusses the structure of rule-based expert systems and their advantages, such as natural knowledge representation and separation of knowledge from processing, while also addressing disadvantages like opaque relations between rules and inability to learn. Furthermore, it compares forward and backward chaining methods for reasoning within expert systems and emphasizes the importance of these systems in various professional fields.
Expert System With Python -1
- 1. Expert System With Python Ahmad Hussein [email protected] Lecture 1: Introduction
- 2. CONTENT OVERVIEW • What is knowledge? • Presenting Knowledge • The main players in the development team • Uses of Expert Systems • Prominent Expert Systems • Basic structure of a rule-base expert system • Can expert systems make mistakes? • Comparison of expert systems with conventional systems and human experts • Forward chaining and backward chaining • How do we choose between forward and backward chaining? • Advantages and Disadvantages of rule-base expert systems
- 3. What is knowledge? • Knowledge is a theoretical or practical understanding of a subject or domain. Knowledge is also the sum of what is currently known, and apparently knowledge is power. Those who possess knowledge are called experts. • Anyone can be considered a domain expert if he/she has deep knowledge (of both fact and rules) and strong practical experience in a particular domain. The area of the domain may be limited. In general, an expert is a skillful person who can do things other people cannot.
- 4. Presenting Knowledge knowledge representation techniques: • Rules • Frames • Semantic Nets
- 5. Presenting Knowledge Rules: • The term rule in AI, which is the most commonly used type of knowledge representation, can be defined as an IF-THEN structure that release given information or facts in the IF part to some action in the THEN part. A rule provides some description of how to solve a problem. Rules are relatively easy to create and understand. • Any rule consists of two parts: the IF part, called the antecedent (premise or condition) and the THEN part called consequent (conclusion or action).
- 6. Presenting Knowledge Rules: • IF <condition> THEN <action> • A rule can have multiple antecedents joined by the keywords AND (conjunction), OR (disjunction) or a combination of both. IF <condition1> AND <condition2> OR <condition3> THEN <action>
- 7. Presenting Knowledge Frames: • Frames are also useful for representing knowledge. As frames allow nodes to have structures they can be regarded as representations of knowledge. • A frame is similar to a record structure and corresponding to the fields and values are slots and slot fillers.
- 8. Presenting Knowledge Frames: • A frame for a book is given below: Slots Fillers publisher Dana title Expert Systems author Ahmad edition Third year 2018 pages 600
- 9. Presenting Knowledge Semantic Nets: • semantic net (or semantic network) is a knowledge representation technique used for propositional information. So it is also called a propositional net. Semantic nets convey meaning. Mathematically a semantic net can be defined as a labelled directed graph. • Semantic nets consist of: • Nodes • Links (edges) • Link labels
- 10. Presenting Knowledge Semantic Nets: Example: Manar Mammals Female Persons Persons Male Persons Karim Subset of Subset of Member ofMember of Sister of
- 11. The main players in the development team • There are five members of the expert system development team: • The domain expert • The knowledge engineer • The programmer • The project manager • The end user • The success of their expert system entirely depends on how well the members work together.
- 12. The main players in the development team
- 13. Uses of Expert Systems • Very useful to companies with a high-level of experience and expertise that cannot easily be transferred to other members. • Solves problems that would normally be tackled by a medical or other professional. • Currently used in fields such as accounting, medicine, process control, financial service, production, and human resources.
- 14. Prominent Expert Systems • MYCIN – used to diagnose infectious blood diseases and recommend antibiotics. • DENDRAL – embedded a chemist’s knowledge of mass spectrometry rules to use in analysis. • CADUCEUS – used to analyze blood-borne infectious bacteria • CLIPS, JESS, Prolog and Pyknow programming languages are all used in expert systems • The Age of Empire game uses CLIPS to control its AI
- 15. Basic structure of a rule-base expert system
- 16. Basic structure of a rule-base expert system • The knowledge base contains the domain knowledge useful for problem solving. In a rule-based expert system, the knowledge is represented as a set of rules. Each rule specifies a relation, recommendation, directive, strategy or heuristic and has the IF (condition) THEN (action) structure. When the condition part of a rule is said to fire and the action part is executed. • The database includes a set of facts used to match against the IF (condition) part of rules stored in the knowledge base.
- 17. Basic structure of a rule-base expert system • The inference engine carries out the reasoning whereby the expert system reaches a solution. It links the rules given in the knowledge base with the facts provided in the database. • The explanation facilities enable the user to ask the expert system how a particular conclusion is reached and why a specific fact is needed. An expert system must be able to explain its reasoning and justify its advice, analysis or conclusion. • The user interface is the means of communication between a user seeking a solution to problem and an expert system.
- 18. Can expert systems make mistakes? Even a brilliant expert is only a human and thus can make mistakes. This suggests that an expert system built to perform at a human expert level also should be allow to make mistakes. But we still trust experts, even we recognize that their judgements are sometimes wrong. Likewise, at least in most cases, we can rely on solutions provided by expert systems, but mistakes are possible and we should be aware of this.
- 19. Comparison of expert systems with conventional systems and human experts
- 20. Comparison of expert systems with conventional systems and human experts
- 21. Forward chaining and backward chaining • In rule-based expert system, the domain knowledge is represented by a set of IF-THEN production rules and data is represented by a set of facts about the current situation. The inference engine compares each rule stored in the knowledge base with facts contained in the database. When the IF (condition) part of the rule matches a fact, the rule is fired and its THEN (action) part is executed. • The matching of the rule IF parts to the facts produces inference chains. The inference chain indicates how an expert system applies the rules to reach conclusion.
- 22. Inference engine cycle via a match-fire produce
- 23. Forward chaining • Forward chaining is the data-driven reasoning. The reasoning starts from the known data and proceeds forward with that data. Each time only the topmost rule is executed. When fired, the rule adds a new fact in the database. Any rule can be executed only once. The match- fire cycle be executed only once. The match-fire cycle stops when no further rules can be fired. • forward chaining is a technique for gathering information and the inferring for it whatever can be inferred.
- 24. Forward chaining • However, in forward chaining, many rules may be executed that have nothing to do with the established goal. • therefore, if our goal is to infer only one particular fact, the forward chaining inference technique would not be efficient.
- 25. Backward chaining • Backward chaining is the goal-driven reasoning. In backward chaining, an expert system has the goal (a hypothetical solution) and the inference engine attempts to find the evidence to prove it. First, the knowledge base is searched to find rules that might have the desired solution. Such rules must have the goal in their THEN (action) parts. If such a rule is found and its IF (condition) part matches data in the database, then the rule is fired and the goal is proved. However, this is rarely the case.
- 26. Backward chaining • Thus the inference engine puts side rule it is working with (the rule is said stack) and sets up a new goal, a subgoal, to prove the IF part of this rule. Then the knowledge base is searched again for rules that can prove the subgoal. The inference engine repeats the process of stacking the rules until no rules are found in the knowledge base to prove the current subgoal.
- 27. How do we choose between forward and backward chaining? • If an expert first needs to gather some information and then tries to infer from it whatever can be inferred, choose the forward chaining inference engine. • However, if your expert begins with a hypothetical solution and then attempts to find facts to prove it, choose the backward chaining inference engine.
- 28. Advantages of rule-base expert systems • Natural knowledge representation. An expert usually explains the problem-solving procedure with such expressions as this: “In such- and-such situation, I do so-and-so”. These expressions can be represented quite naturally as IF-THEN production rules. • Uniform structure. Production rules have the uniform IF-THEN structure. Each rule is an independent piece of knowledge. The very syntax of production rules enables them to be self-documented.
- 29. Advantages of rule-base expert systems • Separation of knowledge from its processing. The structure of rule- base expert system provides an effective separation of the knowledge base from the inference engine. This makes it possible to develop different applications using the same expert system shell. • Dealing with incomplete and uncertain knowledge. Most rule-based expert systems are capable of representing and reasoning with incomplete and uncertain knowledge.
- 30. Disadvantages of rule-based expert systems • Opaque relations between rules. Although the individual production rules are relatively simple and self-documented, their logical interactions within the large set of rules may be opaque. Rule-base systems make it difficult to observe how individual rules serve the overall strategy. • Ineffective search strategy. The inference engine applies an exhaustive search through all the production rules during each cycle. Expert systems with a large set of rules (over 100 rules) can be slow, and thus large rules-based systems can be unsuitable for real-time applications.
- 31. Disadvantages of rule-based expert systems • Inability to learn. In general, rule-based expert systems do not have an ability to learn from the experience. Unlike a human expert, who knows when to “break the rules”, an expert system can’t automatically modify its knowledge base, or adjust existing rules or add new ones. The knowledge engineer is still responsible for revising and maintaining the system.