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Automated Changes of Problem Representation

This article discusses the concept of representation in problem-solving and explores automated systems for changing problem representations. It highlights research interests and projects in artificial intelligence, machine learning, algorithm theory, and computational geometry. The future research challenges and applications of representation changes in various domains are also discussed.

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Automated Changes of Problem Representation

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  1. Automated Changes ofProblem Representation Eugene FinkLTI Retreat 2007

  2. Outline Research interests and projects • Research interests and projects • Concept of representation • Representation-changing system • Future research challenges

  3. 1992–1998 Ph.D. Student 1999–2003 Assistant Professor 2004–2007 Systems Scientist Research interests and projects

  4. Major Minor Research interests • Artificial intelligence • Machine learning • Algorithm theory • Computational geometry

  5. Representation changesin AI search (Prodigy) Indexing of massive approx-imate data (Argus/Rapid) Optimization and elicitation under uncertainty (Radar) Exchange markets for complex commodities Medical expert systems Indexing of time series Generalized convexity Main projects • Artificial intelligence • Machine learning • Algorithm theory • Computational geometry

  6. Automated improve-ment of representations Efficient reasoningunder uncertainty Collaboration withthe human user Heuristic searchin indexing trees Research themes Representation changesin AI search (Prodigy) Indexing of massive approx-imate data (Argus/Rapid) Optimization and elicitation under uncertainty (Radar) Exchange markets for complex commodities Medical expert systems Indexing of time series Generalized convexity

  7. Representation changesin AI search (Prodigy) Automated improve-ment of representations Indexing of massive approx-imate data (Argus/Rapid) Efficient reasoningunder uncertainty Optimization and elicitation under uncertainty (Radar) Collaboration withthe human user Exchange markets for complex commodities Medical expert systems Heuristic searchin indexing trees Indexing of time series Generalized convexity Research themes Automated improve-ment of representations

  8. Outline • Research interests and projects • Concept of representation • Representation-changing system • Future research challenges

  9. Concept of representation Informally, a representation is a specific approach to solving a problem or class of problems. Psychologists and computer scientists have accumulated evidence on the importance of appropriate representations for both human problem solvers and software systems. • Examples: • Efficient indexing structures vs. linear search • Java vs. assembly language • Well written vs. poorly written papers

  10. Aha! The related AI challenge is to automated the search for the right approach. Aha! Motivation When humans works on a complex problem, they may need to search for the right approach to the problem.

  11. A representation-changing system should select or construct appropriate data structures and algorithms for each given class of problems. data structures Rep-changing system problems algorithms Definition of representation • A representation consists of three parts: • Problem or class of problems • Data structures for representing them • Algorithms operating on these structures

  12. Outline • Research interests and projects • Concept of representation • Representation-changing system • Future research challenges

  13. Representation-changing system Automated representation improvements in the Prodigy problem-solving architecture. Three main parts: • Library of search modules • Library of learning modules • Top-level control mechanism

  14. learned data structures Architecture top-level control search modules learning modules

  15. Architecture top-level control Automatic control Manual control Control center search modules learning modules learned data structures • Given a new problem: • Select appropriate modules • Apply them to solve the problem • Repeat if necessary

  16. Top-level control Solve the problem or learn additional knowledge? learn solve Which learner to apply? Which past results to use? Solve or skip the problem? With which search module? Which learned data to use? skip solve failure Invoke the selected learning module Invoke the selected search module success Wait for the next problem

  17. Performance example Solving a series of 50 problems, and improving the related representation. gain on each problem order of solving problems

  18. Performance example Solving a series of 50 problems, and improving the related representation. accumulated average gain order of solving problems

  19. Performance example Solving a series of 500 problems, and improving the related representation. accumulated average gain order of solving problems

  20. Related publications • Eugene Fink. Changes of problem representation. Springer-Verlag, 2003. • Eugene Fink. Automatic evaluation and selection of problem solving methods. JETAI journal, 16(2), 2004. • Eugene Fink. Evaluation of representations. IEEE SMC conference, under review.

  21. Applications • Exchange markets for complex commodities: Automated selection of indexing structures, depending on the type and number of orders • Elicitation under uncertainty (Radar): Automated selection among elicitation algorithms and related heuristics

  22. Outline • Research interests and projects • Concept of representation • Representation-changing system • Future research challenges

  23. Future research challenges • Evaluation of representations • Automated selections among data structures and algorithms • Automated construction of new data structures • Integrated AI systems • Applications

  24. Evaluation of representations • Standard methods for the evaluation and comparison of alternative representations • Theory of representation efficiency, which should account for the search time, solution quality, and percentage of solved problems • Inherent complexity of AI problems, complexity classes, and AI-completeness

  25. Automated selection amongdata structures and algorithms • Combining exploration with exploitation • Analysis of similarities among problems and among representations • Generation and use of small test problems

  26. Automated constructionof new data structures • Automated selection among alternative data structures • Self-adjusting data structures • Automated construction of complex structures from basic blocks

  27. Integrated AI systems • General architecture for integrating search and learning algorithms • Large library of standard AI algorithms and tools for their synergetic use The grand challenge is to develop an architecturethat integrates thousands of AI engines and problemdomains, in the same way as an operating systemintegrates file-processing programs.

  28. Applications The automated improvement ofrepresentations should be applicableto most areas of computer science.

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