Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall Chapter Chapter 7: Expert Systems and Artificial Intelligence Decision Support Systems in the 21 st Century, 2 nd Edition by George M. Marakas

Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-HallChapter : The Concept of Expertise Expertise: extensive knowledge in a narrow field Expert systems: a computer application that employs a set of rules based on human knowledge to solve problems that require human expertise Artificial Intelligence: practical mechanisms that enable computers to simulate the reasoning process

Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-HallChapter : The Intelligence of Artificial Intelligence How do people reason? Categorization Specific Rules Heuristics Past Experience Expectations

Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-HallChapter How Do Computers Reason? Rule-based reasoning: IF-THEN statements represent knowledge encoded as rules Frames: representations of stereotyped situations that are typical of some category Case-based reasoning: adapting previous solutions to a current problem Pattern recognition: detecting sounds, shapes or long sequences

Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-HallChapter Other Forms of AI Machine learning – neural networks and genetic algorithms Automatic programming – mechanisms that generate a program to do a specific task (allows non-programmers to “program”) Artificial life – attempts to recreate biological phenomena within computer-based systems

Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-HallChapter : The Concept and Structure of Expert Systems Basic structure of an ES follows the generic structure of a DSS The knowledge base is specific to a particular problem domain associated with the ES The main difference between an ES and DSS is that the ES contains knowledge acquired from experts in the application domain

Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-HallChapter Common Expert System Architecture User Knowledge Engineer User Interface Inference Engine Knowledge Base User Environment KE Tool Kit KE Interface Development Environment Organization Systems Interface

Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-HallChapter The User Interface in an ES Design of the UI focuses on human concerns such as ease of use, reliability and reduction of fatigue Design should allow for a variety of methods of interaction (input, control and query) Mechanisms include touch screen, keypad, light pens, voice command, hot keys

Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-HallChapter The Knowledge Base Contains the domain-specific knowledge acquired from the domain experts Can consist of object descriptions, problem- solving behaviors, constraints, heuristics and uncertainties The success of an ES relies on the completeness and accuracy of its knowledge base

Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-HallChapter The Inference Engine Here, the knowledge is put to use to produce solutions The engine is capable of performing deduction or inference based on rules or facts Also capable of using inexact or fuzzy reasoning based on probability or pattern matching

Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-HallChapter The Inference Control Cycle Three steps characterize a cycle: 1. Match rules with given facts 2. Select the rule that is to be executed 3. Execute the rule by adding the deduced fact to the working memory

Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-HallChapter Chaining Simple methods used by most inference engines to produce a line of reasoning Forward chaining: the engine begins with the initial content of the workspace and proceeds toward a final conclusion Backward chaining: the engine starts with a goal and finds knowledge to support that goal

Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-HallChapter Forward Chaining Example Suppose we have three rules: R1: If A and B then D R2: If B then C R3: If C and D then E If facts A and B are present, we infer D from R1 and infer C from R2. With D and C inferred, we now infer E from R3.

Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-HallChapter Backward Chaining Example The same three rules: R1: If A and B then D R2: If B then C R3: If C and D then E If E is known, then R3 implies C and D are true. R2 thus implies B is true (from C) and R1 implies A and B are true (from D).

Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-HallChapter : Designing and Building Expert Systems Expert System Shells: generic systems that contain reasoning mechanisms but not the problem-specific knowledge Early shells were cumbersome but still allowed the user to avoid having to completely program the system from scratch Modern shells contain two primary modules: a rule set builder and an inference engine

Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-HallChapter Building an Expert System An early step is to identify the type of tasks (interpretation, prediction, monitoring, etc.) the system will perform Another important step is choosing the experts who will contribute knowledge: It is common for one or more of these experts to be part of the development team Unlike more general information systems design projects, the software tools and hardware platform are selected very early

Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-HallChapter : Evaluating the Benefits of Expert Systems Some major benefits: 1. Increased timeliness in decision making 2. Increased productivity of experts 3. Improved consistency in decisions 4. Improved understanding 5. Improved management of uncertainty 6. Formalization of knowledge

Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-HallChapter Limitations Associated With ES One important limitation is that expertise is difficult to extract and encode. Another is that human experts adapt naturally but an ES must be recoded. Further, human experts better recognize when a problem is outside the knowledge domain, but an ES may just keep working