CSCI-8050 Knowledge-Based Systems
Prerequisites: (CSCI-6540 and CSCI-6550) or POD.
Description: (Themes: Knowledge & Expertise, AI/DB Integration, Decision Support)
Theory and practice of knowledge based system construction with particular emphasis on rule-based expert systems.† Topics include KBS fundamentals, knowledge representation, knowledge base construction, knowledge integration in databases, inference engines, reasoning from incomplete or uncertain information, intelligent decision support, and user tools & interfaces.
Instructor: Walter D. Potter
Office: GSRC-113 (enter through 111)
Phone: 542-0361 (with rollover and voice mail)
Hours: By Appointment, Drop In, or __(hours to be determined)__
Notes: If you stop-by or call, and Iím NOT available then be sure to leave a note.† Iíll get back to you as soon as possible.† E-mail is best.
1) Introduction to Expert Systems, Third Edition by Peter Jackson
2) Microsoft's Age of Mythology (tentative)
References (in Library):
Intelligent Database Systems by Bertino,
Rule-Based Expert Systems by Buchanan, B.G. and E.H. Shortliffe, eds.,
Programming in Depth by
Reserve Books, and Current Literature
Systems, reports, & presentations (variable due dates)
Talks, summaries & other HW (due weekly)
around Tuesday December 14th, noon
*No late coursework accepted.† Due dates are scheduled in advance and are firm.
*Class attendance is required and class participation is graded (under assignments).
Policies: Note that each student is expected to do his/her own work.† Any evidence of academic dishonesty will not be tolerated and will be subject to disciplinary action.† Be sure you are familiar with the Universityís academic (dis)honesty policy as well as any departmental policies (see attached).† No make-up exams are given.
NOTE: The course syllabus provides a general guide for the course; deviations may be necessary.
CSCI-8050: Knowledge Based Systems
Scope: The road map we plan to follow this semester includes a focus on three distinct areas of knowledge based systems: expert systems, intelligent database systems, and intelligent information systems.
Expert Systems are knowledge based systems that attempt to rival the performance of a human expert.† Typically, a knowledge engineering task is undertaken to acquire expert domain knowledge from one or more human experts.† This knowledge is coded using some useful representation scheme and possibly some expert system shell IDE.† We will investigate the development of such a system (to the extent allowed within our time constraints).
Intelligent Database Systems integrate concepts from AI with those from the DB arena to form database systems with more capabilities than merely serving up facts to user queries.† Active Databases may be considered a part of the Intelligent Database Systems domain since they use active triggers (i.e., rules) to initiate some internal database processing.† Other types of rules may be incorporated into a database to derive values to "virtual" attributes during query processing.† On another front, rules may be used to massage a user query in order to provide summary results instead of some large amount of tabular data.
Intelligent Information Systems bring together several types of systems to help with the decision making process.† A typical IIS has several components including one or more databases, one or more expert systems, a structured interface, an intermediate working area (sometimes called a blackboard), one or more models that can be used for decision making or query response (i.e., using a forest regeneration simulation model to predict timber density at some point in the future), and its own processing routines.† These components work together in a transparent fashion to aid user decision making.† The infrastructure to support this seamless interaction among components is the real heart of an IIS.
(Each major topic item is covered at the approximate rate indicated.† However, due to the dynamic nature of the in-class activities, there may be substantial variation from this schedule.)
Week 1††††††††††† Expert Systems - Introduction
††††††††††††††††††††††† ††††††††††† Definition
††††††††††††††††††††††† ††††††††††† Characteristics
††††††††††††††††††††††† ††††††††††† Typical Applications
††††††††††††††††††††††† ††††††††††† Example Systems
Week 4††††††††††† Components of Expert Systems (Architecture)
††††††††††††††††††††††† ††††††††††† Knowledge Base
††††††††††††††††††††††† ††††††††††††††††††††††† Knowledge Representation
††††††††††††††††††††††† ††††††††††††††††††††††† Meta-Knowledge
††††††††††††††††††††††† ††††††††††† Inference Engine
††††††††††††††††††††††† ††††††††††††††††††††††† Search Techniques
††††††††††††††††††††††† ††††††††††††††††††††††† Reasoning With Uncertainty
††††††††††††††††††††††† ††††††††††† User Interface
††††††††††††††††††††††† ††††††††††††††††††††††† User Dialog
††††††††††††††††††††††† ††††††††††††††††††††††† Explanation
††††††††††††††††††††††† ††††††††††††††††††††††† Tutoring
Week 9††††††††††† Tools and Environments for Expert System Development
Week 10††††††††† Building an Expert System
††††††††††††††††††††††† ††††††††††† Problem Selection
††††††††††††††††††††††† ††††††††††† Development Methodology
††††††††††††††††††††††† ††††††††††† Knowledge Acquisition
††††††††††††††††††††††† ††††††††††† Pitfalls
Week 12††††††††† Evaluation of Expert Systems
††††††††††††††††††††††† ††††††††††† Test Cases
††††††††††††††††††††††† ††††††††††† Refinement
††††††††††††††††††††††† ††††††††††† Performance
Week 14††††††††† Intelligent Database Systems
††††††††††††††††††††††† ††††††††††† Data Models
††††††††††††††††††††††††††††††††††† Active Database Systems
††††††††††††††††††††††††††††††††††† Derivable Attribute Values
Week 17††††††††† Intelligent Information Systems
††††††††††††††††††††††††††††††††††† Blackboard Architecture
††††††††††††††††††††††††††††††††††† Wrapper Architecture
††††††††††††††††††††††††††††††††††† Dependent Agent Architecture