Harmonizing Case Retrieval and Adaptation with Alternating Optimization.

Case-based reasoning (CBR) research has developed numerous methods for learning to improve case retrieval and adaptation knowledge. Learning for each type of knowledge is usually pursued independently. However, it is well known that the knowledge containers of CBR are tightly coupled, in that changes in one can affect requirements for another, which suggests potential benefit for coupling learning across knowledge containers. This paper proposes applying alternative optimization to learn retrieval and adaptation knowledge together, in order to harmonize their behaviors. For a testbed system using neural network based similarity and adaptation, this study compares alternative optimization, independent learning, and learning by prioritizing adaptation for adaptation-guided retrieval. Results support that alternative optimization can help to balance both components and achieve good performance.