1 / 11

Intelligent Decision Support Systems: A Summary

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. Talk@ cse395. Revised talk .

mora
Download Presentation

Intelligent Decision Support Systems: A Summary

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Intelligent Decision Support Systems: A Summary

  2. 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 Talk@ 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: talk@ 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 • Model+cases • 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 doesn’t 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. 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

  10. 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

More Related