Plan Generation, Plan Management, and the Design of Computational Agents

A significant amount of prior research effort in the field of Artificial Intelligence has gone into the design and analysis of planning algorithms. For the most part, this work has been guided by several strong, simplifying assumptions, most notably, that the plans will be performed in static, deterministic environments. Although these assumptions have made rigorous formal analysis possible, they make sense only for a limited number of applications, in which planning is done more or less in isolation of other reasoning tasks, and also in isolation of plan execution. Once we turn our attention to agents that perform autonomously in dynamic, uncertain environments—including multi-agent environments—the assumptions made by traditional planners are violated, and it becomes necessary to rethink the traditional AI approaches to planning.