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) CBR System uses case bases of previously solved problems to solve new problems The 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) 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

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

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

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

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

Combining Cases

Abstract Cases Solution Part: Description 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

Complete Set of Abstract Tasks This is a Task Hierarchy Each task is classified under a specific task category Each category introduces a number of features related to that task

Concrete Cases Solution Part: Description 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

Complete Set of Concrete Tasks This is a Task Hierarchy Each task is classified under a specific task category Each category introduces a number of features related to that task

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

The Main HCBR Algorithm (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 base

Retrieval and Adaptation 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

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

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

Integration 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

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

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

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