PROBABILISTIC PLAN RECOGNITION BASED ON ALGORITHM OF EG-PRUNING
Some new concepts are introduced, including soft ordering constraint, hard ordering constraint and goal hypotheses sub-tree. All of these concepts together with the concept of supporting degree are incorporated into simple hierarchical (task decomposition) plan, thus results in extended hierarchy (task decomposition) plan. Using this extended hierarchical (task decomposition) plan as plan representation, we present a novel probabilistic algorithm of plan recognition. The core of our algorithm is EG-Pruning.The new algorithm infers the unobserved actions using the two kinds of ordering constraints defined above to extend EG and prunes the current EG by soft ordering constraints checking to make the set of goal hypotheses restricted. Then the probabilities of the goal hypotheses are computed to grade them. Finally, it extends the goal hypotheses sub-trees selected according to their probabilities to attain the whole plan hypotheses. Meanwhile, we have introduced the concept of supporting degree to make the recognition change reasonable with more evidence collected. Benefiting from these steps, our new algorithm clarifies a number of issues that were obscured by previous approaches. In particular, our approach can handle partial observation of domains, partially ordered plans and multiple, interleaved plans. Further, it is able to eliminate the Agents misleading actions. The implementation of this algorithm will have a very considerable prospect in computer network security.
Plan recognition EG-Pruning hard ordering constraint soft ordering constraint
XIU-LI SUN YONG-LI LI SHU-HUA WANG MING-HAO YIN
School of Computer, Northeast Normal University, Changchun130117, Jilin, China
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
2237-2241
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）