Intelligent Decision Support Systems: A Summary

Case-Based Reasoning Example: 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 of Talks w/ Similar Content 1. Retrieve 5. Retain New Case 4. Review New Slides - 9/12/03: cse395 First draft 2. Reuse 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 & view creation 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:  Compositional  Transformational

E-commerce (Joe Souto)Recommender Systems (Chad Hogg) fixed innovative products Knowledge gap: seller doesn’t know what buyer wants User Requirements  Hard versus soft  Redundant + contradictory Local similarity metrics Information overload Variants:  Content: inter-item similarity  Collaborative: Preferences  Query based  Hybrid 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: requirements  Organizational: training  Managerial: 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

TopicPresenterKnowledgeCertaintyTask OntologiesProf. Heflin Intensive Certain Methodological Rule-Based Systems Catie Welsh IntensiveUncertaintyAnalysis DesignLiam PageIntensiveCertainSynthesis ConfigurationSudhanKanitkarIntensiveCertainSynthesis E-commerceJoe SoutoLow/MediumUncertaintyAnalysis RecommenderChad HoggLow/MediumUncertaintyAnalysis Intelligent Tutor. Systems Nicolas Frantzen IntensiveCertainAnalysis/ Synthesis Help-desk systems Stephen Lee- Urban Low/MediumUncertaintyAnalysis CCBRShruti BhandariLow/MediumUncertaintyAnalysis CBMFabiana Prabhakar LowN.A.Methodological MDPs and RLMegan SmithLow/MediumUncertaintyMethodological Fuzzy LogicMark Strohmaier MediumUncertaintyMethodological

Computational Complexity Techniques for IDSS have a variety of complexities  Searching for m-NN in a sequential case base with n cases:  O(nlog 2 m)  Searching for m-NN in a case base with n cases indexed with a KD-tree :  O(log k n  log 2 m)  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 systems  Classification  Diagnosis  Tutoring  Synthesis Tasks  Int. Tutoring Systems  E-commerce  Help-desk systems AI  Introduction  Overview IDT  Attribute-Value Rep.  Decision Trees  Induction CBR  Introduction  Representation  Similarity  Retrieval  Adaptation Rule-based Inference  Rule-based Systems  Expert Systems The Summary Synthesis Tasks  Constraints  Configuration Uncertainty (MDPs, Fuzzy logic)