基于限定的规划识别问题求解

该文把McCarthy的限定理论同规划识别结合起来,在限定中研究规划识别问题,证明了在一定的限制下,由观察到的现象求出的最小规划集与对这些现象作限定获得的解集是一样的,以此为基础,文中提出了一种用限定求解规划识别问题的方式,这种方法把Kautz提出的规划识别表示形式做了某些改变,求解的过程中把二阶限定的表示形式转化为一阶形式,这种一阶形式的限定结果可以用逐点限定的方法直接求得,因为利用了逐点限定的这一特点,该文的方法对限定的计算过程中可以用机器自动完成。

Solution to Plan Recognition Problem Based on Circumscription

Plan recognition is a new technique for planning field of artificial intelligence. Given a fragmented, impoverished description of the actions performed by one or more agents, we need the technique to infer a rich, highly interrelated description. A generalized formal plan recognition model presented by Kautz is the best famous method in plan recognition field at present. Kautz's model is under the assumptions that the agent has complete knowled1ge and does not make mistakes. Under these assumptions, plan recognition is similar to McCarthy's circumscription theory. We combine circumscription with plan recognition, and investigate plan recognition problem with circumscription. At first we present a definition of plan recognition and a formal formalization of plan, the formalization includes the relations among plans. Then we prove that the plan recognition's minimum plan set that is based on the observations is the same as the circumscription of the observations. At last we give an algorithm of computing the plan recognition's minimum plan set by circumscription. As circumscription is a second-order non-monotonic formulism, its mechanical computation is a very difficult problem. There is a method to turn a second-order circumscription into a set of first-order formulas. This kind of first-order formula can be computed directly with pointwise circumscription. This profits from two main works: a method prompted by Lifschitz, that under some restriction, second-order formula could be turned into first-order formula. Another kind of circumscription, pointwise circumscription not only is logically equivalent to second-order circumscription, but also could be defined in any first-order theories under some conditions, that is to say that it always behaves as a first-order approximation of second-order circumscription without exceptions. Because of pointwise circumscription's attractive feature, the method we given here can compute circumscription mechanically. The method resolves some problems in Kautz's method. The semantic of the method is direct and distinct due to the circumscription's good semantic property. The scope of the inferred plans can be adjusted through changing the values of the variables in the circumscription, and the computation is more simple and more flexible. The priorities in observations, which are offered by the priority pointwise circumscription, make the circumscription procedure pay more attention on those events that are more important or more special. It can reduce the inferring, and obtain the plans efficiently when there are more than one candidate. This method can enhance the ability of fault tolerance when agent makes mistakes. In other words, if there is not completed satisfied plan in system, the most similar one will be found.