Intelligent moving of groups in real-time strategy games

Danielsiek, Holger; Stüer, Raphael; Thom, Andreas; Beume, Nicola; Naujoks, Boris; Preuß, Mike

doi:10.1109/cig.2008.5035623

Cited by 31 publications

(12 citation statements)

References 5 publications

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“…al. [5] use IMs in conjunction with flocking to determine movement of groups in RTS games, with the goal of cohesively moving groups of units. Our goal differs in that we are attempting to generate autonomous tactics for each entity, which are evolved to coordinate their tactics.…”

Section: Related Workmentioning

confidence: 99%

Coevolving influence maps for spatial team tactics in a RTS game

Avery

Louis

2010

Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation

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Real Time Strategy (RTS) games provide a representation of spatial tactics with group behaviour. Often tactics will involve using groups of entities to attack from different directions and at different times of the game, using coordinated techniques. Our goal in this research is to learn tactics which are challenging for human players. The method we apply to learn these tactics, is a coevolutionary system designed to generate effective team behavior. To do this, we present a unique Influence Map representation, with a coevolutionary technique that evolves the maps together for a group of entities. This allows the creation of autonomous entities that can move in a coordinated manner. We apply this technique to a naval RTS island scenario, and present the successful creation of strategies demonstrating complex tactics.

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Section: Related Workmentioning

confidence: 99%

Coevolving influence maps for spatial team tactics in a RTS game

Avery

Louis

2010

Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation

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“…This could be a heavy load for a system. Preuss and Beume used a flocking based and influence map-based path finding algorithm to enhance team movement in the RTS game "Glest" [9], [10].…”

Section: Related Workmentioning

confidence: 99%

Comparing heuristic search methods for finding effective group behaviors in RTS game

Liu

Louis

Nicolescu

2013

2013 IEEE Congress on Evolutionary Computation

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We compare genetic algorithms against hillclimbers for generating competitive unit micro-management for winning real-time strategy game skirmishes. Good group positioning and movement, which are part of unit micro-management can help win skirmishes against equal numbers and types of opponent units or even when outnumbered. In this paper, we use influence maps to generate group positioning and potential fields to guide unit movement. We tested the behaviors obtained from genetic algorithm and two types of hill-climbing search against the default Starcraft AI using the brood war API. Preliminary results show that while our hill-climbers quickly find influence maps and potential fields that generate quality positioning and movement in our simulations, they only find quality solutions fifty to seventy percent of the time. On the other hand, genetic algorithms evolve high quality solutions a hundred percent of the time, but take significantly longer.

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“…Finally, although pathfinding does not fall under our previous definition of reactive control, we include it in this section, since it is typically performed as a low-level service, not part of either tactical nor strategical reasoning (although there are some exceptions, like the tactical pathfinding of Danielsiek et al [47]). The most common pathfinding algorithm is A*, but its big problem is CPU time and memory consumption, hard to satisfy in a complex, dynamic, real-time environment with large numbers of units.…”

Section: Reactive Controlmentioning

confidence: 99%

“…Avery et al [45] and Smith et al [46] co-evolved influence map trees for spatial reasoning in RTS games. Danielsiek et al [47] used influence maps to achieve intelligent squad movement to flank the opponent in a RTS game. Despite their success, a drawback for potential field-based techniques is the large number of parameters that has to be tuned in order to achieve the desired behavior.…”

Section: Reactive Controlmentioning

confidence: 99%

A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft

Ontañón

Synnaeve

Uriarte

et al. 2013

IEEE Trans. Comput. Intell. AI Games

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329

269

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Abstract-This paper presents an overview of the existing work on AI for real-time strategy (RTS) games. Specifically, we focus on the work around the game StarCraft, which has emerged in the past few years as the unified test-bed for this research. We describe the specific AI challenges posed by RTS games, and overview the solutions that have been explored to address them. Additionally, we also present a summary of the results of the recent StarCraft AI competitions, describing the architectures used by the participants. Finally, we conclude with a discussion emphasizing which problems in the context of RTS game AI have been solved, and which remain open.

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Intelligent moving of groups in real-time strategy games

Cited by 31 publications

References 5 publications

Coevolving influence maps for spatial team tactics in a RTS game

Coevolving influence maps for spatial team tactics in a RTS game

Comparing heuristic search methods for finding effective group behaviors in RTS game

A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft

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