Proactive Continual Planning –
In order to behave intelligently, artificial agents must be able to deliberatively plan their future actions. Unfortunately, realistic agent environments are usually highly dynamic and only partially observable, which makes planning computationally hard. For most practical purposes this rules out planning techniques that account for all possible contingencies in the planning process. However, many agent environments permit an alternative approach, namely continual planning, i. e. the interleaving of planning with acting and sensing.
This article presents a principled approach to continual planning that describes why and when an agent should switch between planning and acting. The resulting continual planning algorithm enables agents to deliberately postpone parts of their planning process and instead actively gather missing information that is relevant for the later refinement of the plan. To this end, the algorithm explictly reasons about the knowledge (or lack thereof) of an agent and its sensory capabilities. In order to enable proactive information gathering we introduce the concept of assertions into our planning language, i.e. abstract actions that can substitute yet unformed subplans in early planning phases.
To study our continual planning approach empirically we have developed MAPSIM, a simulation environment that automatically builds multiagent simulations from planning domain descriptions. In MAPSIM, agents can thus not only plan, but also execute their plans, perceive their environment, and interact with each other.While obviously such a simulation does not capture many aspect of a physical robot environment, it can be used for rapid prototyping of planning models for such environments.
KeywordsMultiagent System Planning Domain Plan Execution Symbolic Execution Planning Language
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