The Research On Several Issues Of Intelligent Planning And Plan Recognition

The research on intelligent planning and plan recognition has always been thecore of artificial intelligence technology and the most challenging one of researchdirections, and the application value and research prospect are obvious to all.Intelligent planning is about action reasoning, which process is through theexpectation effect of expected action, selected and organized a series of actions, as faras possible to achieve a given goal in advance. In real word, when we want tocomplete a complicated task, or execute action with some constraints, it needs to planthe movements how to carry out，this is the simple sense of planning. Intelligentplanning covers knowledge representation, human-computer interaction, situationcalculus, knowledge reasoning, non monotonic logic and cognitive science and otherfields with its overlapping subject. There are great of corresponding algorithms. Theresearchers try to use a variety of methods for solving the planning problem, in orderto get the optimal planning solution and widely application in the real world.Intelligent planning has a strong applicability, one of which is in the workshopscheduling. In the limited resources, according to the known processing orderrequirements to make arrangement for the production, the makespan is the shortest,and every machine of waiting time shortest.Plan recognition is a process, according to the observed an agent of fragmentsactions, deduces a series of related actions and ultimate goal. After30years ofdevelopment, its approach has become increasingly mature, for now, plan recognitionhas become one of the hot researchs of artificial intelligence. This paper mainly aimsto intelligent planning and plan recognition, and discuss this two related researchdirections. The innovation achievement as follows:First, we introduced the graphplan and its expansion, such as consistentgraphplan, Sensory graphplan, flexible graphplan, probabilitc graphplan, and so on.We mainly discusse the plan with the dynamic variable object set. This methodrelaxes the hypothesis that the classic graphplan can’t generate objects and destroyobject, make its more flexible in practical application.Second, counterplanning, planning recognition, and adversial plan are closelylinked, they are an organic whole. Therefore, we research and analysis the problems detailedly, and we also analyze the difference and characteristics between adversialplanning and hostile planning. The solving method of counterplanning and itsapplication has been discussed.Third, we propose a modle and its algorithm of conterplanning with the variableobject set. It can execute the effective counterplanning in dynamic environment. Inthe adversial field, the HTN (hierarchical task network) planning is applied torecognize fastly and search the effective strategies to deal with. In order to themilitary problems as an example, we use the algorithm to analyse and solve it.Fourth, we propose a network attack recognition algorithm on the basis of goalgraph, which can recognize the attack planning in the complex network environment,and predict the actions of the next step. At the same time, we also join timingconstraints in the causal network to analysis each step action clearly, and can identifyinvalid and tentative network attacks.Fifth, we have reviewed the solving method of PFSP problem in recent years, allthe current algorithm can be divided into three types roughly, the Makespan and Totalflowtime and Tardiness as the goal. Then three types of solving methods arecompared and analysis, and the advantages and disadvantages of each algorithm aresummarized.Sixth, we propose an effective scheduling algorithm based on the differentialevolution algorithm, namely hybrid differential evolution algorithm, referred to asL-HDE. The algorithm combines the IIS local search and greed bureau search toimprove the diversity of population, and the method can jump out the local minimum.At the same time, we also compare our algrithm with a number of well-knownalgorithms. The experimental results show that L-HDE has superior performance androbustness. L-HDE can realize good compromise in the global search and local search,and is very suitable for processing PFSP problem, which has a good performance andefficiency.In conclusion, the result of this paper has certain theoretical significance andapplication value. It provides a good method and means of promoting the intelligentplanning and plan recognition.