Author Identifier

Martin Masek

ORCID : 0000-0001-8620-6779

Chiou-Peng Lam

ORCID : 0000-0002-4843-9229

Document Type

Journal Article

Publication Title

Annals of Operations Research

Publisher

Springer

School

School of Science

RAS ID

36662

Funders

Edith Cowan University - Open Access Support Scheme 2021

Defence Science and Technology Group, Australia

Comments

Masek, M., Lam, C. P., Kelly, L., & Wong, M. (2021). Discovering optimal strategy in tactical combat scenarios through the evolution of behaviour trees. Annals of Operations Research. Advance online publication. https://doi.org/10.1007/s10479-021-04225-7

Abstract

In this paper we address the problem of automatically discovering optimal tactics in a combat scenario in which two opposing sides control a number of fighting units. Our approach is based on the evolution of behaviour trees, combined with simulation-based evaluation of solutions to drive the evolution. Our behaviour trees use a small set of possible actions that can be assigned to a combat unit, along with standard behaviour tree constructs and a novel approach for selecting which action from the tree is performed. A set of test scenarios was designed for which an optimal strategy is known from the literature. These scenarios were used to explore and evaluate our approach. The results indicate that it is possible, from the small set of possible unit actions, for a complex strategy to emerge through evolution. Combat units with different capabilities were observed exhibiting coordinated team work and exploiting aspects of the environment.

DOI

https://doi.org/10.1007/s10479-021-04225-7

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Research Themes

Securing Digital Futures

Priority Areas

Artificial intelligence and autonomous systems

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