Planning Study On The Possibility Of Planning And Implementation In The Framework

Classical approaches of intelligent planning are based on strong assumptions such as: the agent has total knowledge of planning domains, or the effects of the agent's action are deterministic. So they can't be applied to most practical systems.The aim of the paper is to advance a possibility theory based planning method. Using possibility distribution to describe the uncertainty of the agent's effects, the function of our planning algorithm is more powerful and more adaptable. Though traditional methods in non-deterministic planning are based on probability theory, we believe our method is better because possibility theory is more powerful in handling ignorance. So even when we can't offer a precise scale on the uncertainty of the agent's effects, our method can also work. In this sense, our method is qualitative rather than quantitative. After introducing the concepts of possibilistic actions, possibilistic plans, we introduce a graphplan based method. Using this method, we can find an optimal plan in a given time window. What's more, we also prove that classical plan is a special case of possibilistic plan. We have implemented our method in c++, and the experiment has shown that our method is efficient and robust.