On-Line Case-Based Policy Learning for Automated Planning in Probabilistic Environments

Many robotic control architectures perform a continuous cycle of sensing, reasoning and acting, where that reasoning can be carried out in a reactive or deliberative form. Reactive methods are fast and provide the robot with high interaction and response capabilities. Deliberative reasoning is particularly suitable in robotic systems because it employs some form of forward projection (reasoning in depth about goals, pre-conditions, resources and timing constraints) and provides the robot reasonable responses in situations unforeseen by the designer. However, this reasoning, typically conducted using Artificial Intelligence techniques like Automated Planning (AP), is not effective for controlling autonomous agents which operate in complex and dynamic environments. Deliberative planning, although feasible in stable situations, takes too long in unexpected or changing situations which require re-planning. Therefore, planning cannot be done on-line in many complex robotic problems, where quick responses are frequently required. In this paper, we propose an alternative approach based on case-based policy learning which integrates deliberative reasoning through AP and reactive response time through reactive planning policies. The method is based on learning planning knowledge from actual experiences to obtain a case-based policy. The contribution of this paper is two fold. First, it is shown that the learned case-based policy produces reasonable and timely responses in complex environments. Second, it is also shown how one case-based policy that solves a particular problem can be reused to solve a similar but more complex problem in a transfer learning scope.