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

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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). - PowerPoint PPT Presentation

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  • 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)CBR System uses case bases of previously solved problems to solve new problemsThe solution is modified to fit the new problem

  • What is Hierarchical Case Based Reasoning (HCBR)HCBR is a technique created by the authors for multiple-case (more complex) reuseUses multiple levels of solution abstractionAbstract case is like a decomposition templateParts of previous solutions, stored as individual cases, can be reused and recombined to solve some subproblem

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

  • What is Automated ProgrammingStarts with a specification of some taskEnds with a program that is the solution to this task

  • Abstraction LevelsAbstract plans outline solutionsAbstract plans are refined by replacing abstract operators with collections of more detailed operatorsComplete plan will contain only primitive operators

  • Different CasesAbstract Cases Solutions correspond to high-level plans for particular problemsHigh-Level program designsConcrete CasesSolutions correspond to actual programs

  • Combining Cases

  • Abstract CasesSolution Part: Contains abstract operators that correspond to high-level actionsAbstract operators also act as subproblem specificationsDescription Part:Similar to description part of a concrete caseDrawn from the abstract task hierarchy

  • Complete Set of Abstract Tasks

  • Concrete CasesSolution Part: Sequential function chart made up of primitive operationsDescription Part:Contains a set of features that relate to the case solutionIn dj vu these descriptions are task oriented

  • Complete Set of Concrete Tasks

  • The HCBR Process ModelDecompose a specification into right set of subproblemsSolved separatelyIndividual solutions can be recombined to produce a suitable overall solution

  • The Main HCBR Algorithm

  • Retrieval and AdaptationRetrievalThe target specification features are matched against the description features of cases in the case base and a measure of similarity is computedResult is a ranking of cases according to their similarity to the targetAdaptationEnsures that the retrieved case is the easiest of those available to adapt, not just the case that is most similar

  • Decomposition

  • IntegrationEach HCBR cycle generates one new solution componentIntegration adds this component to the evolving solutionSolution is an abstraction hierarchy and each new solution component is added as a leaf node

  • Integration

  • LearningNew Solved Problems Learned By:Packaging their specifications and solutions together as new casesAdding these new cases to the case baseParts of the problem can be learned as separate cases at various levels of abstractionCondition tests whether or not the new case is novel enough to warrant addition

  • Results

  • Benefits of HCBRRepeated solution segments are not redundantly stored within multiple large casesInstead theyre stored as single case instances that can be easily reusedAvoids the problem of decomposing a problem for which there are no suitable cases in the case baseCases and the decompositional knowledge are fully integratedThe 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 coverageRepresents complex problems as collections of independently reusable abstract and concrete cases

    This is a Task Hierarchy

    Each task is classified under a specific task category

    Each category introduces a number of features related to that taskThis is a Task Hierarchy

    Each task is classified under a specific task category

    Each category introduces a number of features related to that task(STEPS 1) In the beginning the specification queue contains just one specification

    (STEPS 4, 5) Begins with the current target specification an involves the retrieval and adaptation of a single case to generate one new solution component a section of abstract code

    The retrieved case may be abstract or concrete

    (STEPS 6 to 8)If it is abstract then its news adapted abstract operators will be extracted and queued as future subproblem specifications (THIS IS DECOMPOSITION)

    (STEP 9)During each cycle the new solution component is integrated into the evolving overall solution

    (STEP 10)Under certain conditions a new solution component may be learned by the system by adding it ot the case baseThe abstract solution operators are copied from the solution into the specification queue

    The abstract solution operators are copied from the solution into the specification queue

    Parent Link -- Preserve the hierarchical relationship between solution components at different levels of abstraction -- Connect a new solution compenent, its parent (the solution component containing the abstract operator) that led to the production of the current component

    Sibling Links Preserve execution ordering between solution components

    The abstract solution operators are copied from the solution into the specification queue

    Reasons for Speed Up: Flexible reuse strategy allows target problems to be solved by reusing multiple cases at various levels of abstraction