A simheuristic approach for evolving agent behaviour in the exploration for novel combat tactics. (2019)

• Record Type: Journal Article
• Authors: Lam, Chiou-Peng; Masek, Martin; Kelly, Luke; Papasimeon, Michael; Benke, Lyndon
• Is Part Of: Operations research perspectives. Volume 6(2019)
• Publication Date: 2019
• Subjects: Simheuristics -- Genetic algorithms -- Multiagent simulations -- Stochastic combinatorial optimization -- Finite state machines; Operations research -- Periodicals; Management science -- Periodicals; 658.403405
• DOI: 10.1016/j.orp.2019.100123
• Languages: English
• ISSNs: 2214-7160

Abstract

Highlights: Genetic algorithms (GA) can generate finite state machine based behavioural models. A worked example in air combat behaviour is presented. The success of genetic algorithms depends on tuning a number of parameters. Workable starting points for researchers intending to use GA are provided. Abstract: The automatic generation of behavioural models for intelligent agents in military simulation and experimentation remains a challenge. Genetic Algorithms are a global optimization approach which is suitable for addressing complex problems where locating the global optimum is a difficult task. Unlike traditional optimisation techniques such as hill-climbing or derivatives-based methods, Genetic Algorithms are robust for addressing highly multi-modal and discontinuous search landscapes. In this paper, we outline a simheuristic GA-based approach for automatic generation of finite state machine based behavioural models of intelligent agents, where the aim is the identification of novel combat tactics. Rather than evolving states, the proposed approach evolves a sequence of transitions. We also discuss workable starting points for the use of Genetic Algorithms for such scenarios, shedding some light on the associated design and implementation difficulties.