Reactive Policies with Planning for Action Languages
This work addresses the challenge of designing and verifying reactive behaviors in AI agents, but it appears incremental as it builds on existing high-level transition systems and planning methods.
The paper tackles the problem of representing and analyzing reactive policies for agents by combining a target decision component with online planning, enabling the verification of expected behavior and flexible behavior design.
We describe a representation in a high-level transition system for policies that express a reactive behavior for the agent. We consider a target decision component that figures out what to do next and an (online) planning capability to compute the plans needed to reach these targets. Our representation allows one to analyze the flow of executing the given reactive policy, and to determine whether it works as expected. Additionally, the flexibility of the representation opens a range of possibilities for designing behaviors.