Decision Support System CHapter one.pptx
- 2. Expert Systems • In artificial intelligence, an expert system is a computer system emulating the decision-making ability of a human expert. • Expert Systems are computer programs that exhibit intelligent behavior. • They are concerned with the concepts and methods of symbolic inference, or reasoning, by a computer, and how the knowledge used to make those inferences will be represented. • Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural code.
- 3. Area of Artificial Intelligence
- 4. Expert system technology • Special expert system languages . • Programs • Hardware designed to facilitate the implementation of those systems
- 5. Main Components of Expert System • Knowledge base – obtainable from books, magazines, knowledgeable persons, etc. • contains essential information about the problem domain • often represented as facts and rules • Inference engine – draws conclusions from the knowledge base • mechanism to derive new knowledge from the knowledge base and the information provided by the user • often based on the use of rules • User interface • interaction with end users • development and maintenance of the knowledge base
- 6. Components of Knowledge Base • The knowledge base store both, factual and heuristic knowledge. • Factual Knowledge − It is the information widely accepted by the Knowledge Engineers and scholars in the task domain. • Heuristic Knowledge − It is about practice, accurate judgement, one’s ability of evaluation, and guessing.
- 7. Problem Domain vs. Knowledge Domain • 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.
- 8. Characteristics of Expert Systems Knowledge acquisition • transfer of knowledge from humans to computers • sometimes knowledge can be acquired directly from the environment • machine learning, neural networks Knowledge representation • suitable for storing and processing knowledge in computers • The knowledge of an expert system can be represented in a number of ways, including IF-THEN rules: IF you are hungry THEN eat Inference • mechanism that allows the generation of new conclusions from existing knowledge in a computer Explanation • illustrates to the user how and why a particular solution was generated
- 9. Knowledge Engineering • Knowledge engineering refers to all technical, scientific and social aspects involved in building, maintaining and using knowledge-based systems. • 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 analysis to the knowledge engineer
- 10. Development of an Expert System
- 11. 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. • 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.
- 12. Artificial Neural Systems In the 1980s, a new development in programming paradigms appeared called artificial neural systems (ANS). • Based on the way the brain processes information. • Models solutions by training simulated neurons connected in a network. • ANS are found in face recognition, medical diagnosis, games, and speech recognition
- 13. ANS Characteristics • A complex pattern recognition problem –computing the shortest route through a given list of cities. • ANS is similar to an analog computer using simple processing elements connected in a highly parallel manner. • Processing elements perform Boolean / arithmetic functions in the inputs • Key feature is associating weights with each element.
- 15. Participants in the development of Expert System • There are three primary participants in the building of Expert System: • Expert: The success of an ES much depends on the knowledge provided by human experts. These experts are those persons who are specialized in that specific domain. • Knowledge Engineer: Knowledge engineer is the person who gathers the knowledge from the domain experts and then codifies that knowledge to the system according to the formalism. • End-User: This is a particular person or a group of people who may not be experts, and working on the expert system needs the solution or advice for his queries, which are complex.
- 16. Advantage Of Expert System • Providing consistent solutions • Provides reasonable explanations • Overcome human limitations • Easy to adapt to new conditions • Reduce the Decision-Making Time • It can tackle a very complex problem that is difficult for a human expert to solve.
- 17. Disadvantages Of Expert System • Lacks common sense: • It lacks common sense needed in some decision making since all the decisions made base on the inference rules set in the system. • High implementation and maintenance cost • Difficulty in creating inference rules • May provide wrong solutions: • It is not error-free. There may error occur in the processing due to some logical mistakes made in the knowledge base, which will then provide the wrong solutions.