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An event-driven plan recognition algorithm based on intuitionistic fuzzy theory

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Abstract

Plan recognition is a process of the observers inferring agents’ goal and planning by observing the agents’ behavior sequences. It is applied to image processing, network security and military domain. But the most prior work about plan recognition could not deal with fragmentary and small quantity of event information effectively and accurately. In this paper, we put forward an event-driven plan recognition method based on intuitionistic fuzzy theory. The algorithm based on recognizing the fuzzy event sequences is presented to forecast the future action about object. First, by analyzing the process and feature of plan recognition, we bring the master plan into sub-tasks which are a sequence of events, and then we create an algorithm about predicting the goal of the plan by recognizing the series of events. At the same time, thinking about the uncertainty of event, intuitionistic fuzzy theory is put into this model to eliminate the ambiguity of data. Finally, an experiment in military domain is testified for our approach.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, 710048, China

    Xiaofan Wang, Lei Wang & Shengji Li

  2. School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China

    Jin Wang

Authors
  1. Xiaofan Wang
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  2. Lei Wang
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  3. Shengji Li
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  4. Jin Wang
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Correspondence to Xiaofan Wang.

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Wang, X., Wang, L., Li, S. et al. An event-driven plan recognition algorithm based on intuitionistic fuzzy theory. J Supercomput 74, 6923–6938 (2018). https://doi.org/10.1007/s11227-018-2650-9

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