Introduction to Expert Systems PDF

Introduction to Expert Systems  What is an Expert System An Expert System is a computer program that simulates human intelligence and behavior in specific and limited domains It is composed of three major modules: – A Knowledge Base – An Inference Engine – A User Interface E...

Introduction to Expert Systems  What is an Expert System An Expert System is a computer program that simulates human intelligence and behavior in specific and limited domains It is composed of three major modules: – A Knowledge Base – An Inference Engine – A User Interface Expert System Major Components  Expert Systems are Good For Limited domains where expert knowledge is available Providing expert opinion in remote sites Enhance the performance of tasks by applying heuristic expert knowledge Planning, Troubleshooting, Robotic manipulation, Exploration, Process Control  Expert Systems Are Not Good For Representing temporal knowledge Representing spatial knowledge Performing commonsense reasoning Recognizing the limits of their ability Handling inconsistent knowledge  Conventional vs. Symbolic Programming Representations and use of data vs. Representations and use of knowledge Algorithmic processing vs. Heuristic processing Repetitive vs. Inferential process Effective manipulation of large data bases vs. Effective manipulation of large knowledge bases  Overall Architecture Working Memory I n t Knowledge e Base r f a Inference c Engine e  Artificial Intelligence (Simple Definition) Behavior by a machine that, if performed by a human being, would be called intelligent 8  AI Objectives Make machines smarter (primary goal) Understand what intelligence is (Nobel Laureate purpose) Make machines more useful (entrepreneurial purpose) 9 AI Represents Knowledge as Sets of Symbols A symbol is a string of characters that stands for some real-world concept Examples Product Defendant 0.8 Chocolate 10  How AI Works AI Programs Manipulate Symbols to Solve Problems Symbols and Symbol Structures Form Knowledge Representation Artificial Intelligence Dealings Primarily with Symbolic, Nonalgorithmic Problem Solving Methods 11  Some Major AI Areas Expert Systems Natural Language Processing Speech Understanding (Smart) Robotics and Sensory Systems Neural Computing Fuzzy Logic Genetic Algorithms Intelligent Software Agents 12 Expert Systems/Knowledge- Based Systems Attempt to Imitate Expert Reasoning Processes and Knowledge in Solving Specific Problems Most Popular Applied AI Technology – Enhance Productivity – Augment Work Forces Narrow Problem-Solving Domain or Tasks Qualitative Problem-Solving Aspects 13  Terminology Knowledge Engineering: The discipline of acquiring, encoding and using human domain knowledge to develop a computer application Expert System: A computer program that uses domain knowledge to perform a specific task usually human experts perform Knowledge Base: A set of rules and facts describing the domain of an application Inference Engine: A program that imposes a general control strategy on how the system is working Working Memory: A set of facts describing a particular consultation Interface: A program that links the user with the Expert System  Knowledge Base Models domain knowledge usually in the form of rules, frames, or semantic nets Probabilistic models and fuzzy models can be used to model uncertainty A typical Expert System has several hundred rules A Knowledge base can become very complex and its has to be consistent at all times Knowledge acquisition tools can be used to build and maintain a Knowledge Base  Rules IF the infection is pimary-bacteremia AND the site of the culture is one of the sterile sites AND the suspected portal of entry is the gastrointestinal tract THEN there is suggestive evidence (0.7) that infection is bacteroid.  Semantic Networks isa has-part Ship isa has-part Ocean Oil Engine Hull Liner Tanker has-part isa isa has-part Swimming Queen Liverpool Boiler Pool Mary  Frames Progress Report Author: John Allen (default) Topic: Due Date: Length: 2 pages (default) If-added If-removed isa Author: Mary Smith Topic: Biological Classification Project If-needed Due Date: Sept. 30, 2000 Length:40 pages  Inference Engine Considering that the Knowledge Base encodes domain knowledge and expertise in terms of rules and facts there are three variations for the inference engine: – Forward Chaining or Data Driven (essentially Modus Ponens) – Backward Chaining or Hypothesis Driven – Mixed (i.e. Forward and Backward Chaining combined) Most Expert Systems assume that the Inference Engine strategy is monotonic Several Expert Systems allow for reasoning under uncertainty – Probabilistic (MYCIN, Bayes, Demster-Shafer) – Fuzzy – Non-monotonic (Truth Maintenance Systems)  Inference and Logic Modus Ponens: A1, A2, A1 & A2 => B B Modus Tolens: not A2, A1 => A2 not A1  Issues on Building Expert Systems Lack of Resources – Personnel – Expert System tools Inherent limitations of Expert System tools – Performing knowledge acquisition – Refining Knowledge Bases – Handling mixed representation schemes Expert Systems take long time to build  Pitfalls in Choosing Problems Difficult problem chosen and inadequate resources available The problem the Expert System is about to solve does not warrant the development effort The problem the Expert System addresses is very general or complex  Choosing a Problem The need for a solution must justify the costs involved in development. There must be a realistic assessment of the costs and benefits involved. Human expertise is not available in all situations where it is needed. If the ``expert'' knowledge is widely available it is unlikely that it will be worth developing an expert system. The problem may be solved using symbolic reasoning techniques. It shouldn't require manual dexterity or physical skill. The problem is well structured and does not require (much) common sense knowledge. Common sense knowledge is notoriously hard to capture and represent. It turns out that highly technical fields are easier to deal with, and tend to involve relatively small amounts of well formalised knowledge. The problem cannot be easily solved using more traditional computing methods. If there's a good algorithmic solution to a problem, you don't want to use an expert system. Cooperative and articulate experts exist. For an expert system project to be successful it is essential that the experts are willing to help, and don't feel that their job is threatened! You also need any management and potential users to be involved and have positive attitudes to the whole thing..The problem is of proper size and scope. Typically you need problems that require highly specialized expertise, but would only take a human expert a short time to solve (say an hour, max). Neural Network Math.