- Preprint (7) (remove)
- Feature Weighting by Explaining Case-Based Problem Solving Episodes (1996)
- We present a similarity criterion based on feature weighting. Feature weights are recomputed dynamically according to the performance of cases during problem solving episodes. We will also present a novel algorithm to analyze and explain the performance of the retrieved cases and to determine the features whose weights need to be recomputed. We will perform experiments and show that the integration in a feature weighting model of our similarity criterion with our analysis algorithm improves the adaptability of the retrieved cases by converging to best weights for the features over a period of multiple problem solving episodes.
- Planning for Manufacturing Workpieces by Storing, Indexing and Replaying Planning Decisions (1996)
- Planning for manufacturing workpieces is a complex task that requires the interaction of a domain-specific reasoner and a generic planning mechanism. In this paper we present an architecture for organizing the case base that is based on the information provided by a generic problem solver. A retrieval procedure is then presented that uses the information provided by the domain-specific reasoner in order to improve the accuracy of the cases retrieved. However, it is not realistic to suppose that the case retrieved will entirely fit into the new problem. We present a replay procedure to obtain a partial solution that replays not only the valid decisions taken for solving the case, but also justifications of rejected decisions made during the problem solving process. As a result, those completion alternatives of the partial solution are discarded that are already known to be invalid from the case.
- Complete Eager Replay (1996)
- We present an algorithm for completely replaying previous problem solving experiences for plan-space planners. In our approach not only the solution trace is replayed, but also the explanations of failed attempts made by the first-principle planner. In this way, the capability of refitting previous solutions into new problems is improved.
- A Case Study on Mergeability of Cases with a Partial-Order Planner (1997)
- Retrieving multiple cases is supposed to be an adequate retrieval strategy for guiding partial-order planners because of the recognized flexibility of these planners to interleave steps in the plans. Cases are combined by merging them. In this paper, we will examine two different kinds of merging cases in the context of partial-order planning. We will see that merging cases can be very difficult if the cases are merged eagerly. On the other hand, if cases are merged by avoiding redundant steps, the guidance of the additional cases tends to decrease with the number of covered goals and retrieved cases in domains having a certain kind of interactions. Thus, to retrieve a single case covering many of the goals of the problem or to retrieve fewer cases covering many of the goals is at least equally effective as to retrieve several cases covering all goals in these domains.
- Advantages of Types in Partial-Order Planning (1997)
- This paper shows an approach to profit from type information about planning objects in a partial-order planner. The approach turns out to combine representational and computational advantages. On the one hand, type hierarchies allow better structuring of domain specifications. On the other hand, operators contain type constraints which reduce the search space of the planner as they partially achieve the functionality of filter conditions.
- Controlling Nonlinear Hierarchical Planning by Case Replay (1999)
- We describe a hybrid case-based reasoning system supporting process planning for machining workpieces. It integrates specialized domain dependent reasoners, a feature-based CAD system and domain independent planning. The overall architecture is build on top of CAPlan, a partial-order nonlinear planner. To use episodic problem solving knowledge for both optimizing plan execution costs and minimizing search the case-based control component CAPlan/CbC has been realized that allows incremental acquisition and reuse of strategical problem solving experience by storing solved problems as cases and reusing them in similar situations. For effective retrieval of cases CAPlan/CbC combines domain-independent and domain-specific retrieval mechanisms that are based on the hierarchical domain model and problem representation.
- Case-Based Reasoning Applied to Planning (1999)
- Planning means constructing a course of actions to achieve a specified set of goals when starting from an initial situation. For example, determining a sequence of actions (a plan) for transporting goods from an initial location to some destination is a typical planning problem in the transportation domain. Many planning problems are of practical interest.