A model of reaction for planning in dynamic environments

One shortcoming with most AI planning systems has been an inability to deal with execution-time discrepancies between actual and expected situations. Often, these exception situations jeopardize the immediate integrity and safety of the planning agent or its surroundings, with the only recourse being more time-consuming plan generation. In order to avoid such situations, potential exceptions must be predicted during plan execution. Since many application domains (particularly for autonomous systems) are inherently dynamic — in the sense that information is at best incomplete, perhaps erroneous, and changes over time independent of a planning agent's actions — managing action in the world becomes a difficult problem. Action and events in dynamic worlds must be monitored in order to coordinate an agent's actions with its surroundings. This allows the agent to predict and plan for potential future exception situations while acting in the present.This paper introduces an approach to autonomous reaction in dynamic environments. We have avoided the traditional distinction between generating and then executing plans through the use of a dynamic reaction system, which handles potential exception situations gracefully as it carries out assigned tasks. The reaction system manages constraints imposed by ongoing activity in the world, as well as those derived from long-term planning, to control observable behaviour. This approach provides the necessary stimulus/response behaviour required in dynamic situations, while using goal-directed constraints as heuristics for improved reactions.We present an overview of the salient features of dynamic worlds and their impact on traditional planning, introduce our model of dynamic reactivity, describe an implementation of the model and its performance in a dynamic simulation environment, and present an architecture incorporating long-term planning with short-term reactance suitable for autonomous systems applications.