Intelligent decision support systems a summary
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Intelligent Decision Support Systems: A Summary. Specification. 1. Retrieve. 5. Retain. New Slides. Repository of Presentations: 5/9/00: ONR review 8/20/00: EWCBR talk 4/25/01: DARPA review. Slides of Talks w/ Similar Content. 4. Review. New Case. [email protected] cse395. Revised talk .

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Case based reasoning l.jpg

Specification

1. Retrieve

5. Retain

New Slides

  • Repository of Presentations:

  • 5/9/00: ONR review

  • 8/20/00: EWCBR talk

  • 4/25/01: DARPA review

Slides of

Talks w/

Similar

Content

4. Review

New Case

[email protected]

cse395

Revised

talk

3. Revise

First draft

2. Reuse

Case-Based Reasoning

  • 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

  • Case Base Maintenance (Fabiana Prabhakar)

  • Help-desk systems (Stephen Lee-Urban)

  • 2 lectures (indexing)

Example: Slide Creation

- 9/12/03: [email protected] cse395

  • Design (Liam Page)

  • Rule-based Systems (Catie Welsh)

  • Configuration (Sudhan Kanitkar)

  • Intelligent Tutoring Systems (Nicolas Frantzen)

  • 2 lectures


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Rule-Based Systems

(Catie Welsh)

Knowledge Representation

(Prof. Jeff Heflin)

Ontology

DL Reasoner

Inferred Hierarchy

  • 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

table & view

creation

Database operation


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Configuration Systems (Sudhan Kanitkar)

Design

(Liam Page)

  • Concept Hierarchies

  • Structure-Based Approach

  • Forms of adaptation:

    • Compositional

    • Transformational

  • Constrains not fully specified (ranking by preference)

  • Graph representation of data

  • Flexible similarity metrics: local

  • Model+cases

  • Fish and Shrink retrieval


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E-commerce (Joe Souto)

Recommender Systems (Chad Hogg)

products

fixed

innovative

  • Information overload

  • Variants:

    • Content: inter-item similarity

    • Collaborative: Preferences

    • Query based

    • Hybrid

  • Compromise-driven retrieval

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

  • User Requirements

    • Hard versus soft

    • Redundant + contradictory

  • Local similarity metrics


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Help-desk systems (Stephen Lee-Urban)

Intelligent Tutoring Systems (Nicolas Frantzen)

Description/performance history of student behavior

  • Experience Management  CBR

  • Approved versus Open cases

  • Client-Server architecture

    • But all share domain model

  • Help-desk deployment processes:

    • Technical: requirements

    • Organizational: training

    • Managerial: quality assurance

Information the tutor is teaching

Reflects the differing needs of each student


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Conversational Case-Based Reasoning (Shruti Bhandari)

Case Base Maintenance (Fabiana Prabhakar)

  • Coverage(CB): all problems that can be solved with CB

  • Reachability(P): all cases that can solve P

  • Contrast with rule-based systems

  • Initial input in plain text

  • Only relevant cases/questions shown to user


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MDPs and Reinforcement Learning (Megan Smith)

Fuzzy Logic (Mark Strohmaier)

  • 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

  • Policy : state action

  • MDPs: probabilities are given

  • RL: learn the probabilities (adaptive)


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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


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The Summary

Computational Complexity

Programming project

  • AI

    • Introduction

    • Overview

  • IDT

    • Attribute-Value Rep.

    • Decision Trees

    • Induction

  • CBR

    • Introduction

    • Representation

    • Similarity

    • Retrieval

    • Adaptation

  • Rule-based Inference

    • Rule-based Systems

    • Expert Systems

  • Synthesis Tasks

    • Constraints

    • Configuration

  • Uncertainty (MDPs,

  • Fuzzy logic)

  • Applications to IDSS:

    • Analysis Tasks

      • Help-desk systems

      • Classification

      • Diagnosis

      • Tutoring

    • Synthesis Tasks

      • Int. Tutoring Systems

    • E-commerce

    • Help-desk systems


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