中文摘要：

利用可能性理论提出了一种基于PDDL（planning domain definition language）的可能性规划表示方法——Poss-PDDL,给出了基于可能性理论框架下的定性决策理论和图规划算法的可能性规划求解方法——可能性图规划,设计并开发了可能性规划问题求解器Poss-Graphplan.PDDL是国际规划器大赛的标准域定义语言,这使得Poss-PDDL更具通用性和标准性.由于用可能性理论表示动作效果和状态的不确定性更具优势,因此Poss-Graphplan更适用于解决那些概率模型无法解决或者很难获得概率信息的不确定规划问题.可以证明,应用可能性图规划方法求解可能性规划问题得到的规划解是最优的.实验结果表明,可能性图规划方法在问题求解能力和速度上的表现都较为突出.

英文摘要：

Planning researches have been centralizing in classical planning problems based on the assumptions that actions are deterministic; the initial state is known and the goal is defined by a set of final states for earlier years. However, most practical problems do not satisfy these conditions of complete and deterministic information. Therefore, many researchers have been engaged in the Study of uncertainty planning. Most researches on it are centralised in probabilistic planning based on MDP（Markov decision processes） models and dynamic programming or state-space search methods. But transition probabilities for the representation of the effects of actions cannot be obtained easily, especially in artifical intelligence applications where uncertainty is often ordinal and qualitative. Several researchers have advanced the qualitative view of decision making and qualitative versions of decision theory.And yet some researchers think that the uncertainty on states and effects of actions represented by possibility distributions is more adequate to cases in which problems can not be resolved by probability model or the probabilities are not available, not reliable, or hard to obtain. We introduce a possibilistic planning presentation approach based on PDDL （planning domain definition language）named Poss-PDDL, and provide an algorithm to resolve possibilistic planning problems represented by Poss-PDDL which is based on Graphplan and qualitative utility theory in the framework of possibility theory, where both preferences and uncertainty are qualitative. We also design and develop possibilistic planning problem solver--Poss-Graphplan. Poss-PDDL is more universal and normal for PDDL has been the criteria plan domain definition language. As the uncertainty on states and effects of actions represented by possibility distributions is well-suitable for cases in which the probabilities are not available, not reliable, or hard to obtain, our approach is more suitful in solving uncertain planning problems. The experiments sh