Transcript Summary2

Intelligent Decision Support
Systems: A Summary
•AI
Summary
 Introduction
 Overview
•IDT
Attribute-Value Rep.
Decision Trees
Induction
•CBR
Introduction
Representation
Similarity
Retrieval
Adaptation
•Rule-based Inference
Rule-based Systems
Expert Systems
Programming project
•Synthesis Tasks
Planning
Configuration
•Uncertainty (MDP, Utility,
Fuzzy logic)
•Applications to IDSS:
Analysis Tasks
Help-desk systems
Classification
Diagnosis
Tutoring
Synthesis Tasks
KBPP
E-commerce
Knowledge Management
Uncertainty
Degree of beliefs
Decision theory = Probability + Utility
One decision
Sequence of decision
MDPs
Degree of truth
Fuzzy Logic
Design Projects
Jeff
Reuse of design patterns in BlueJ
Osafo Reuse of templates in MS Visual C++. Detailed functionality
Tim
Reuse of web design derivational traces in Dreamweaver
Kiran
Intelligent query re-writing for improving query answering. Scope
Ted
Emacs environment to correct programming errors. Abstract cases
Reddy Going beyond statistics to assist (CBR) planning of a cricket game.
Ke
Extending MS Outlook to schedule a plan of the activities. Example
Shreer Personalized navigation of web browsers.
am
Chris
Personalized web searches (Googleplex)