Abstract
Artificial Intelligence research has traditionally treated planning, execution and learning as independent, sequential subproblems decomposing the larger problem of intelligent action. Recently, several lines of research have challenged the separation of planning and acting. This paper suggests that integration with planning and acting is also important for learning. We present an integrated system SCAVENGER combining an adaptive planner with an ad-hoc learner. Situated plans are retrieved from memory; adaptation during execution extends these plans to cope with contingencies that arise and to tease out descriptions of situations to which these plans pertain. These changes are then integrated into the plan and incorporated into memory. Every situation of action is an opportunity for learning. Adaptive planning makes learning fail-safe by compensating for imperfections and omissions in learning and variability across situations. We discuss a learning example in the domain of mechanical devices.