1. 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

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 Concrete 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: 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

10. 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

11. 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

12. 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

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
(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

15. 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

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

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

18. 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

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

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

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