Document Type
Journal Article
Publisher
Elsevier
School
School of Science
RAS ID
29986
Funders
Defence Science and TechnologyGroup, Australia under the Modelling Complex Warfighting StrategicResearch Investment (Grant no. 7840/G1003574).
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.
DOI
10.1016/j.orp.2019.100123
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Comments
Lam, C. P., Masek, M., Kelly, L., Papasimeon, M., & Benke, L. (2019). A simheuristic approach for evolving agent behaviour in the exploration for novel combat tactics. Operations Research Perspectives, 6, Article 100123. Available here