Intelligent Decision Support Systems: A Summary

1
Intelligent Decision Support Systems A Summary
2
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 talk_at_ cse395

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

3
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
4
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
• Modelcases
• Fish and Shrink retrieval

5
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 doesnt know what buyer
  wants
• User Requirements
• Hard versus soft
• Redundant contradictory
• Local similarity metrics

6
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
7
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

8
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)

9
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
10
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

11
The Summary

• 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