The series of presentations contains the information about "Management Information System" subject of SEIT for University of Pune. Subject Teacher: Tushar B Kute (Sandip Institute of Technology and Research Centre, Nashik) http://www.tusharkute.com

1. Management information system Third Year Information Technology Part 07 Expert Systems Tushar B Kute, Department of Information Technology, Sandip Institute of Technology and Research Centre, Nashik http://www.tusharkute.com

2. Expert system architecture (1) The typical architecture of an e.s. is often described as follows: user interface user inference engine knowledge base

3. Expert system architecture (1) The inference engine and knowledge base are separated because: the reasoning mechanism needs to be as stable as possible; the knowledge base must be able to grow and change, as knowledge is added; this arrangement enables the system to be built from, or converted to, a shell.

4. Expert system architecture (2) It is reasonable to produce a richer, more elaborate, description of the typical expert system. A more elaborate description, which still includes the components that are to be found in almost any real-world system, would look like this:

5. Expert system architecture (2)

6. Expert system architecture (2)

7. The system holds a collection of general principles which can potentially be applied to any problem - these are stored in the knowledge base. The system also holds a collection of specific details that apply to the current problem (including details of how the current reasoning process is progressing) - these are held in working memory. Both these sorts of information are processed by the inference engine. Expert system architecture (2)

8. Expert system architecture (2) Any practical expert system needs an explanatory facility. It is essential that an expert system should be able to explain its reasoning.

9. Expert & Knowledge-Based Systems One of AI’s greatest areas of success was the development of large-scale problem solving systems Originally called expert systems, they would mimic the problem solving processes of domain experts Such as doctors performing diagnosis, or engineers performing design, or wall street analysts selecting stock transactions Expert systems were originally developed by hand And most commonly in some Lisp dialect It was discovered that many problems were being solved by chaining through rules (if-then statements) that would operate on a collection of facts and partial conclusions Called working memory These rule-based systems led to the first AI tools or shells Today, to simplify expert system creation, most people use these AI shells – you just fill in the knowledge, the problem solving processes are already implemented

10. Introduction: Dendral The Dendral system (DENDRiticALgorithm) was the first expert system, developed in the 1960s The idea was, given mass spectrogram data, determine what the chemical composition was The approach: plan-generate-and-test with human feedback This is a constrained search technique Generate a hypothesis: a possible chemical compound Test the hypothesis: use a series of heuristics and subprograms to determine if the chemical compound generated is plausible given the data If so, show it to the user – the user can steer the direction that DENDRAL takes next by suggesting what chemical elements should be added or removed from the generated hypothesis compound DENDRAL repeats until an answer is found acceptable that does not violate the constraints as dictated by the data

11. Pattern Matching Expert Systems consisted of two major components A knowledge base The knowledge that the system would use to come to conclusions In Dendral, this would be chemical elements, compounds, the mass spectrogram data that could arise from any element In Mycin, this would be bacteriological infection knowledge An inference engine How to apply the knowledge For Dendral, this was done by generate-and-test, a very weak form of search Mycin would perform backward chaining through rules More generically though, the inference engine would embody some form of pattern matching One of the first systems that clearly demonstrated pattern matching was Eliza Eliza was a spoof of an expert system but the code will show us how pattern matching works

12. Eliza Created by Joseph Weizenbaum This system would act like a Rogerian psychoanalyst by holding a dialog with a person the dialog would be consist of the doctor (Eliza) asking questions, the human responding, and the doctor using the response to ask another question Weizenbaum wrote the program so that it would generate an English response/question based on a group of patterns If the user sentence matched a pattern, this pattern would be used to generate the next sentence/question Sample dialog: H: Men are all alike E: In what way? H: They’re always bugging us about something or another. E: Can you be more specific? H: He says I’m depressed much of the time. E: I am sorry to hear you are depressed. H: It’s true. I am unhappy. E: Do you think coming here will help you not to be unhappy? H: I need some help, that much seems certain. E: What would it mean to you if you got some help? H: Perhaps I could learn to get along with my mother. E: Tell me more about your family

13. How Eliza Works Repeat Input a sentence Find a rule in the Eliza knowledge-base that matches the pattern Attempt to perform pattern match (see next slide) Attempt to perform segment match (see two slides) If rule found, select one of the responses randomly (each pattern will have at least one response) Fill in any variables Substitute values (you for I, I for you, me for you, am for are, etc) Respond Until user quits Most of the work goes on in the pattern match and segment match functions

14. Artificial intelligence systems It falls into three basic categories: Expert systems. Natural Language Processing Perception Systems. AI is a software technique applied to the non-numeric data expressed in terms of symbols, statements and patterns. It uses methods of symbolic processing, social and scientific reasoning and conceptual modeling for solving the problems.

15. Categories of AI Artificial Intelligence System Natural Language Expert Perception Uses Uses Uses Native Language Knowledge Knowledge Size, Shape, Image, Voice Applies Applies Applies Language Reasoning Human Like Reasoning Sensing Abilities for Reasoning

16. AI Applications Uses Human Information Processing Capability Uses Computer Intelligence for producing Human Like Capacity Uses Human capabilities in speech recognition, Multi Sensory Interfacing AI Applications Robotics Applications Natural Interface Applications Intelligent Agents Fuzzy Logic Learning System Expert System Robot Systems for doing Human Jobs VR Systems

17. Knowledge based expert systems Decision making or problem solving is a unique situation riddled with uncertainty and complexity, dominated by resource constraints and a possibility of several goals. In such cases, flexible systems (open systems) are required to solve the problems. Most of such situations, termed as the unstructured situations, adopt two methods of problem solving, generalized or the knowledge based expert systems.

18. KBES To build a KBES, certain prerequisites are required. The first prerequisite is that a person with the ability to solve the problem with knowledge based reasoning should be available. Second prerequisite is that, such an expert should be able to articulate the knowledge to the specific problem characteristics. Knowledge in KBES is defined as a mix of theory of the subject, knowledge of its application, organized information and the data of problems and its solutions.

20. E. Turban, J. Aronson, T.P. Liang, R. Sharda, “Decision Support and Business Intelligence Systems”, 8th Edition, Pearson Education.