Meta-case-Based Reasoning: Using Functional Models to Adapt Case-Based Agents

It is useful for an intelligent software agent to be able to adapt to new demands from an environment. Such adaptation can be viewed as a redesign problem; an agent has some original functionality but the environment demands an agent with a slightly different functionality, so the agent redesigns itself. It is possible to take a case-based approach to this redesign task. Furthermore, one class of agents which can be amenable to redesign of this sort is case-based reasoners. These facts suggest the notion of “meta-case-based reasoning,” i.e., the application of case-based redesign techniques to the problem of adapting a case-based reasoning process. Of course, meta-case-based reasoning is a very broad topic. In this paper we focus on a more specific issue within meta-casebased reasoning: balancing the use of relatively efficient but knowledge intensive symbolic techniques with relatively flexible but computationally costly numerical techniques. In particular, we propose a mechanism whereby qualitative functional models are used to efficiently propose a set of design alternatives to specific elements within a meta-case and then reinforcement learning is used to select among these alternatives. We describe an experiment in which this mechanism is applied to a casebased disassembly agent. The results of this experiment show that the combination of model-based adaptation and reinforcement learning can address meta-case-based reasoning problems which are not effectively addressed by either approach in isolation.