An evolutionary online adaptation method for modern computer games based on imitation

Priesterjahn, Steffen; Weimer, Alexander

doi:10.1145/1276958.1277031

Cited by 5 publications

(2 citation statements)

References 2 publications

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“…This simplifies optimisation, as agent performance does not need to be improved individually. Only Priesterjahn and Weimer [17] work on adapting individual agent behaviour. As such, only their approach can be considered an attempt at solving the Unreal Tournament Problem described previously.…”

Section: Previous Researchmentioning

confidence: 99%

Online evolution in Unreal Tournament 2004

Patrick

2010

Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games

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Traditional approaches to game AI often feature behaviour that is scripted and predictable. Previous attempts at adaptive AI have struggled to get agents to learn quickly enough. This paper aims to show adaptation using online evolution is feasible and that it can can be incorporated with minimal change to the existing AI. A new approach is presented for evolving game agents online using an Evolution Strategy.

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Section: Previous Researchmentioning

confidence: 99%

Online evolution in Unreal Tournament 2004

Patrick

2010

Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games

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“…Thurau with colleagues applied Bayesian imitation [4], [5], [6] and other machine learning approaches [7], [8] to develop an NPC player for Quake II. In [9], [10], a rule-based evolutionary approach was applied to evolve an effective NPC player for Quake III.…”

Section: Related Workmentioning

confidence: 99%

Learning a context-aware weapon selection policy for Unreal Tournament III

Galli

Loiacono

Lanzi

2009

2009 IEEE Symposium on Computational Intelligence and Games

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Modern computer games are becoming increasingly complex and only experienced players can fully master the game controls. Accordingly, many commercial games now provide aids to simplify the player interaction. These aids are based on simple heuristics rules and cannot adapt neither to the current game situation nor to the player game style.In this paper, we suggest that supervised methods can be applied effectively to improve the quality of such game aids. In particular, we focus on the problem of developing an automatic weapon selection aid for Unreal Tournament III, a recent and very popular first person shooter (FPS). We propose a framework to (i) collect a dataset from game sessions, (ii) learn a policy to automatically select the weapon, and (iii) deploy the learned models in the game to replace the default weaponswitching aid provided in the game distribution. Our approach allows the development of weapon-switching policies that are aware of the current game context and can also imitate a particular game style.

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Imitation Learning in Uncertain Environments

Priesterjahn

Eberling

2008

Parallel Problem Solving From Nature – PPSN X

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An evolutionary online adaptation method for modern computer games based on imitation

Cited by 5 publications

References 2 publications

Online evolution in Unreal Tournament 2004

Online evolution in Unreal Tournament 2004

Learning a context-aware weapon selection policy for Unreal Tournament III

Imitation Learning in Uncertain Environments

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