Authors:
Yi Zhang
;
Shuilin Li
;
Chuan Ai
;
Yong Peng
and
Kai Xu
Affiliation:
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Keyword(s):
Simulation and Application, Evolution Behavior Tree, Symbolic Plan Recognition, Decision-Reasoning.
Abstract:
Intent recognition refers to obtaining the observations of an agent and then using the observations to reason its current state and to predict its future actions. Behavior modeling, describing the behavior or performance of an agent, is an important research area in intent recognition. However, few studies have combined behavior modeling with intent recognition to investigate its real-world applications. In this paper, we study behavior modeling for intent recognition for cognitive intelligence, aiming to enhance the situational awareness capability of AI and expand its applications in multiple fields. Taking the combat environment and tanks as the research object, based on the behavior tree and SBR recognition algorithm, this paper designs the framework and experiments for behavior modeling and intent recognition. Firstly, uses the evolution behavior tree algorithm to autonomously generate the behavior model adapted to the environment. Secondly uses the SBR algorithm to effectively
recognize actions and plan paths of enemy tank to guide self-tank actions in the TankSimV1.20 simulation platform. The results show that the tank survival rate increases by 80% under the guidance of the intent recognition results, and the method in this paper can provide effective guidance for the intent recognition behavior modeling, which has a broad application prospect.
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