Handling Non-local Dead-ends in Agent Planning Programs


We propose an approach to reason about agent planning programs with global information. Agent planning programs can be understood as a network of planning problems, accommodating long-term goals, non-terminating behaviors, and interactive execution. We provide a technique that relies on reasoning about “global” dead-ends and that can be incorporated to any planning-based approach to agent planning programs. In doing so, we also introduce the notion of online execution of such planning structures. We provide experimental evidence suggesting the technique yields significant benefits.

International Joint Conference on Artificial Intelligence (IJCAI)