Building behavior trees from observations in real-time strategy games

Robertson, Glen; Watson, Ian

doi:10.1109/inista.2015.7276774

Cited by 39 publications

(24 citation statements)

References 16 publications

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“…However, the optimal behavior may be inappropriate or unnatural. Thus, there are a few works of learning BTs from observation emerged but still an open problem [7,25]. We believe GP is a promising method and the frequent sub-tree mining can be a potential tool to facilitate behavior block building and accelerate learning.…”

Section: Discussionmentioning

confidence: 99%

“…To remedy the disadvantages, in both behavior learning perspectives, there are some attempts to generate behavior models represented as BTs from observation [7,25] or experience [11,12,14,26,27] automatically. In this paper, we are focusing on generate BTs through experiential learning, especially evolving BTs.…”

Section: Agent Behavior Modeling and Evolving Behavior Treesmentioning

confidence: 99%

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Learning Behavior Trees for Autonomous Agents with Hybrid Constraints Evolution

et al. 2018

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Section: Discussionmentioning

confidence: 99%

Section: Agent Behavior Modeling and Evolving Behavior Treesmentioning

confidence: 99%

Learning Behavior Trees for Autonomous Agents with Hybrid Constraints Evolution

et al. 2018

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“…For example, the Norwegian army is using behaviour trees that have been manually derived to model behaviour for combat units (Evensen et al, 2018). In other work, behaviour trees have also been constructed automatically by analysing examples of RTS gameplay (Robertson & Watson, 2015).…”

Section: Introductionmentioning

confidence: 99%

Discovering optimal strategy in tactical combat scenarios through the evolution of behaviour trees

et al. 2021

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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.

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“…For example, the Norwegian army used manually crafted behaviour trees to model behaviour, such as battle drills, for computer generated forces (Evensen et al 2018). Work has also been done to automatically construct behaviour trees from observations of RTS gameplay (Robertson & Watson, 2015).…”

Section: Introductionmentioning

confidence: 99%

Evolving behaviour trees for automated discovery of novel combat strategy in real-time strategy wargames

Mašek¹,

Lam²,

Kelly³

et al. 2019

El Sawah, S. (Ed.) MODSIM2019, 23rd International Congress on Modelling and Simulation.

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A wargame is defined as: "A simulation, by whatever means, of a military operation involving two or more opposing forces using rules, data, and procedures designed to depict an actual or assumed real life situation" (Gortney 2016, p503). Wargaming is used for several purposes, such as teaching strategic planning, practising the tasks associated with war, and analytic purposes (Burns et al., 2015). While wargaming is predominantly a human centric analytical activity, it is an area where artificial intelligence (AI) may play a useful role because of a computer's ability to play through a wider range of possible strategies. However, creating simulated AI participants for wargame scenarios is challenging because of the complexity and uncertainty in the environments in which they exist. The recent rise of automated behaviour discovery for agents in a variety of games traditionally dominated by humans offers a possible solution. Commercial real-time strategy (RTS) games provide an abstract simulation of a world where players aim to dominate and defeat other players by acquiring, using and managing resources, often including a mix of military, political, scientific and economic factors. As there is considerable overlap between the objectives found in RTS games to those that exist in military style wargames, RTS games are ideal platforms to conduct research and development in support of our AI-enabled wargaming research objectives.

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Building behavior trees from observations in real-time strategy games

Cited by 39 publications

References 16 publications

Learning Behavior Trees for Autonomous Agents with Hybrid Constraints Evolution

Learning Behavior Trees for Autonomous Agents with Hybrid Constraints Evolution

Discovering optimal strategy in tactical combat scenarios through the evolution of behaviour trees

Evolving behaviour trees for automated discovery of novel combat strategy in real-time strategy wargames

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