Intelligent Decision Support Systems: A Summary

Case-Based Reasoning

Example: Slide CreationExample: Slide Creation

Repository of Presentations:- 5/9/00: ONR review- 8/20/00: EWCBR talk- 4/25/01: DARPA review

Specification

Revised talk

3. Revise

Slides ofTalks w/SimilarContent

1. Retrieve

5. Retain

New Case

4. Review

New Slides

- 9/12/03: talk@ cse395

First draft2. Reuse

Talk@cse395

Talk@cse395

•E-commerce (Joe Souto)•Recommender (Chad Hogg)• Conversational CBR (Shruti Bhandari)• MDPs and Reinforcement Learning (Megan Smith)•Fuzzy Logic (Mark Strohmaier)• 6 lectures + programming project

•E-commerce (Joe Souto)•Recommender (Chad Hogg)• Conversational CBR (Shruti Bhandari)• MDPs and Reinforcement Learning (Megan Smith)•Fuzzy Logic (Mark Strohmaier)• 6 lectures + programming project

•Design (Liam Page) •Rule-based Systems (Catie Welsh)•Configuration (Sudhan Kanitkar) • Intelligent Tutoring Systems (Nicolas Frantzen)•2 lectures

•Design (Liam Page) •Rule-based Systems (Catie Welsh)•Configuration (Sudhan Kanitkar) • Intelligent Tutoring Systems (Nicolas Frantzen)•2 lectures

• Case Base Maintenance (Fabiana Prabhakar)• Help-desk systems (Stephen Lee-Urban)•2 lectures (indexing)

• Case Base Maintenance (Fabiana Prabhakar)• Help-desk systems (Stephen Lee-Urban)•2 lectures (indexing)

Knowledge Representation(Prof. Jeff Heflin)

Inferred Hierarchy

DL Reasoner

Ontology

table & viewcreation

Database operation

Rule-Based Systems(Catie Welsh)

•Rule inference as search trees•Advantages: volume of information, prevent mistakes•Disadvantages: lack of flexibility to changes in environment•Real world domain: IDSS for cancer test

Design(Liam Page)

•Constrains not fully specified (ranking by preference) •Graph representation of data•Flexible similarity metrics: local•Model+cases•Fish and Shrink retrieval

Configuration Systems (Sudhan Kanitkar)

•Concept Hierarchies

•Structure-Based Approach

•Forms of adaptation:CompositionalTransformational

E-commerce (Joe Souto) Recommender Systems (Chad Hogg)

fixed innovativeproducts

•Knowledge gap: seller doesn’t know what buyer wants•User Requirements

Hard versus softRedundant + contradictory

• Local similarity metrics

•Information overload•Variants:

Content: inter-item similarityCollaborative: PreferencesQuery basedHybrid

•Compromise-driven retrieval

Intelligent Tutoring Systems (Nicolas Frantzen)

Description/performance history of student behavior

Information the tutor is teaching

Reflects the differing needs of each student

Help-desk systems (Stephen Lee-Urban)

•Experience Management CBR•Approved versus Open cases•Client-Server architecture

But all share domain model•Help-desk deployment processes:

Technical: requirementsOrganizational: trainingManagerial: quality assurance

Conversational Case-Based Reasoning (Shruti Bhandari)

Case Base Maintenance (Fabiana Prabhakar)

•Contrast with rule-based systems•Initial input in plain text•Only relevant cases/questions shown to user

•Coverage(CB): all problems that can be solved with CB•Reachability(P): all cases that can solve P

MDPs and Reinforcement Learning (Megan Smith)

Fuzzy Logic (Mark Strohmaier)

• Policy : state action

• MDPs: probabilities are given• RL: learn the probabilities (adaptive)

• Drops concept of an element either belongs to a set or not• Rather there is a degree of membership• As a result well capable of dealing with noise• Applications: autonomous vehicles

Topic Presenter Knowledge Certainty Task

Ontologies Prof. Heflin Intensive Certain Methodological

Rule-Based Systems

Catie Welsh Intensive Uncertainty Analysis

Design Liam Page Intensive Certain Synthesis

Configuration SudhanKanitkar Intensive Certain Synthesis

E-commerce Joe Souto Low/Medium Uncertainty Analysis

Recommender Chad Hogg Low/Medium Uncertainty Analysis

Intelligent Tutor. Systems

Nicolas Frantzen

Intensive Certain Analysis/

Synthesis

Help-desk systems

Stephen Lee-Urban

Low/Medium Uncertainty Analysis

CCBR Shruti Bhandari Low/Medium Uncertainty Analysis

CBM Fabiana Prabhakar

Low N.A. Methodological

MDPs and RL Megan Smith Low/Medium Uncertainty Methodological

Fuzzy Logic Mark Strohmaier

Medium Uncertainty Methodological

Computational Complexity

• Techniques for IDSS have a variety of complexities

Searching for m-NN in a sequential case base with n cases: O(nlog2m)

Searching for m-NN in a case base with n cases indexed with a KD-tree :

O(logkn log2m)

Constructing optimal decision tree, graph-subraph isomorphism, configuration, planning, constraint satisfaction

NP-complete

Quantified Boolean formulas, hierarchical planning, winning strategies in games

PSPACE-complete

Computational Complexity Programming project

•Applications to IDSS:Analysis Tasks

Help-desk systemsClassificationDiagnosisTutoring

Synthesis TasksInt. Tutoring Systems

E-commerceHelp-desk systems

•AI Introduction Overview

•IDTAttribute-Value Rep.Decision TreesInduction

•CBRIntroductionRepresentationSimilarityRetrievalAdaptation

•Rule-based InferenceRule-based SystemsExpert Systems

The Summary•Synthesis Tasks

Constraints Configuration

•Uncertainty (MDPs, Fuzzy logic)