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Authors: Barry Smyth, Mark T. Keane, Padraig Cunningham

Hierarchical Case-Based Reasoning Integrating Case-Based and Decompositional Problem-Solving Techniques for Plant-Control Software Design. Authors: Barry Smyth, Mark T. Keane, Padraig Cunningham. What is Case Based Reasoning (CBR).

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Authors: Barry Smyth, Mark T. Keane, Padraig Cunningham

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  1. Hierarchical Case-Based Reasoning Integrating Case-Based and Decompositional Problem-Solving Techniques for Plant-Control Software Design Authors: Barry Smyth, Mark T. Keane, Padraig Cunningham

  2. What is Case Based Reasoning (CBR) • CBR System uses case bases of previously solved problems to solve new problems • The solution is modified to fit the new problem

  3. What is Hierarchical Case Based Reasoning (HCBR) • HCBR is a technique created by the authors for multiple-case (more complex) reuse • Uses multiple levels of solution abstraction • Abstract case is like a decomposition template • Parts of previous solutions, stored as individual cases, can be reused and recombined to solve some subproblem

  4. What is Hierarchical Case Based Reasoning (HCBR) • A decomposition template’s abstract solution is used to break up a complex problem into subproblems • These subproblems are solved from the solution code segments that are produced from the reuse of concrete cases • The integration of these code segments is guided by the solution structure of the abstract cases

  5. What is Automated Programming • Starts with a specification of some task • Ends with a program that is the solution to this task

  6. Abstraction Levels • Abstract plans outline solutions • Abstract plans are refined by replacing abstract operators with collections of more detailed operators • Complete plan will contain only primitive operators

  7. Different Cases • Abstract Cases • Solutions correspond to high-level plans for particular problems • High-Level program designs • Concrete Cases • Solutions correspond to actual programs

  8. Combining Cases

  9. Abstract Cases • Solution Part: • Contains abstract operators that correspond to high-level actions • Abstract operators also act as subproblem specifications • Description Part: • Similar to description part of a concrete case • Drawn from the abstract task hierarchy

  10. Complete Set of Abstract Tasks

  11. Concrete Cases • Solution Part: • Sequential function chart made up of primitive operations • Description Part: • Contains a set of features that relate to the case solution • In déjà vu these descriptions are task oriented

  12. Complete Set of Concrete Tasks

  13. The HCBR Process Model • Decompose a specification into right set of subproblems • Solved separately • Individual solutions can be recombined to produce a suitable overall solution

  14. The Main HCBR Algorithm

  15. Retrieval and Adaptation • Retrieval • The target specification features are matched against the description features of cases in the case base and a measure of similarity is computed • Result is a ranking of cases according to their similarity to the target • Adaptation • Ensures that the retrieved case is the easiest of those available to adapt, not just the case that is most similar

  16. Decomposition

  17. Integration • Each HCBR cycle generates one new solution component • Integration adds this component to the evolving solution • Solution is an abstraction hierarchy and each new solution component is added as a leaf node

  18. Integration

  19. Learning • New Solved Problems Learned By: • Packaging their specifications and solutions together as new cases • Adding these new cases to the case base • Parts of the problem can be learned as separate cases at various levels of abstraction • Condition tests whether or not the new case is novel enough to warrant addition

  20. Results

  21. Benefits of HCBR • Repeated solution segments are not redundantly stored within multiple large cases • Instead they’re stored as single case instances that can be easily reused • Avoids the problem of decomposing a problem for which there are no suitable cases in the case base • Cases and the decompositional knowledge are fully integrated • The collection of abstract cases in the case base is the decomposition knowledge of the system (both use the same sources of knowledge for all problem solving) • Improved problem solving coverage • Represents complex problems as collections of independently reusable abstract and concrete cases

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