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 template’s 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 Programming

Starts with a specification of some taskEnds with a program that is the solution to this task

Abstraction Levels

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

Different Cases

Abstract Cases Solutions correspond to high-level plans for particular problemsHigh-Level program designs

Concrete CasesSolutions correspond to actual programs

Combining Cases

Abstract Cases

Solution Part: Contains abstract operators that correspond to high-level actionsAbstract operators also act as subproblem specifications

Description Part:Similar to description part of a concrete caseDrawn from the abstract task hierarchy

Complete Set of Abstract Tasks

Concrete Cases

Solution Part: Sequential function chart made up of primitive operations

Description Part:Contains a set of features that relate to the case solutionIn déjà vu these descriptions are task oriented

Complete Set of Concrete Tasks

The HCBR Process Model

Decompose a specification into right set of subproblems

Solved separatelyIndividual solutions can be recombined to produce a suitable overall solution

The Main HCBR Algorithm

Retrieval and AdaptationRetrieval

The 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 target

AdaptationEnsures 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 solution

Solution 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 base

Parts 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 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 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