An extended event graph with probability for causal tracing and event prediction

Qiwang Huang; Yang Zhang; Tao Wang; XiaoGuang Liu

doi:10.1117/12.2673047

1 March 2023 An extended event graph with probability for causal tracing and event prediction

Qiwang Huang, Yang Zhang, Tao Wang, XiaoGuang Liu

Proceedings Volume 12596, International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022); 1259610 (2023) https://doi.org/10.1117/12.2673047
Event: International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022), 2022, Changsha, China

Abstract

One of the most important aspects of Computer General Force (CGF) is to understand what the opponent is doing and predict their possible future actions. In this paper, we propose an extended event graph named Probability Event Graph (PEG) to predict the opponent’s future events. Compared with the basic event graph model, the element of event node, logical node, causal edge and time window is redefines in PEG. Through these novel elements, PEG can describe the event and causal relationship about the system comprehensively. The PEG model is the fundamental of forecast analysis. Firstly, the behaviour characteristics of opponents are analysed and the corresponding PEG model is established according to domain knowledge. Then, the parameters are acquired by training data generated by simulation. Finally, the reasoning algorithm based on PEG model is proposed, and the possibility and principal analysis are carried out.

Citation Download Citation

Qiwang Huang, Yang Zhang, Tao Wang, and XiaoGuang Liu "An extended event graph with probability for causal tracing and event prediction", Proc. SPIE 12596, International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022), 1259610 (1 March 2023); https://doi.org/10.1117/12.2673047

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