Continuous Case-Based Reasoning

Case-based reasoning systems have traditionallybeen used to perform high-level reasoning in problem domains that can be adequately described using discrete, symbolic representations. However, many realworld problem domains, such as autonomous robotic navigation, are better characterized using continuous representations. Such problem domains also require continuous performance, such as continuous sensorimotor interaction with the environment, and continuous adaptation and learning during the performance task. We introduce a new method for continuous casebased reasoning, and discuss how it can be applied to the dynamic selection, modification, and acquisition of robot behaviors in autonomous navigation systems. We conclude with a general discussion of case-based reasoning issues addressed by this work.