Monte Carlo tree search based algorithms for dynamic difficulty adjustment

Demediuk, S; Tamassia, M; Raffe, WL; Zambetta, F; Li, X; Mueller, F

Monte Carlo tree search based algorithms for dynamic difficulty adjustment

Demediuk, S Tamassia, M Raffe, WL

Zambetta, F Li, X Mueller, F

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Publication Type:: Conference Proceeding
Citation:: 2017 IEEE Conference on Computational Intelligence and Games, CIG 2017, 2017, pp. 53 - 59
Issue Date:: 2017-10-23

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Field	Value	Language
dc.contributor.author	Demediuk, S	en_US
dc.contributor.author	Tamassia, M	en_US
dc.contributor.author	Raffe, WL https://orcid.org/0000-0001-5310-0943	en_US
dc.contributor.author	Zambetta, F	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Mueller, F	en_US
dc.date.issued	2017-10-23	en_US
dc.identifier.citation	2017 IEEE Conference on Computational Intelligence and Games, CIG 2017, 2017, pp. 53 - 59	en_US
dc.identifier.isbn	9781538632338	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127485
dc.description.abstract	© 2017 IEEE. Maintaining player immersion is a crucial step in making an enjoyable video game. One aspect of player immersion is the level of challenge the game presents to the player. To avoid a mismatch between a player's skill and the challenge of a game, which can result from traditional manual difficulty selection mechanisms (e.g. easy, medium, hard), Dynamic Difficulty Adjustment (DDA) has previously been proposed as a means of automatically detecting a player's skill and adjusting the level of challenge the game presents accordingly. This work contributes to the field of DDA by proposing a novel approach to artificially intelligent agents for opponent control. Specifically, we propose four new DDA Artificially Intelligent (AI) agents: Reactive Outcome Sensitive Action Selection (Reactive OSAS), Proactive OSAS, and their 'True' variants. These agents provide the player with an level of difficulty tailored to their skill in real-time by altering the action selection policy and the heuristic playout evaluation of Monte Carlo Tree Search. The DDA AI agents are tested within the FightingICE engine, which has been used in the past as an environment for AI agent competitions. The results of the experiments against other AI agents and human players show that these novel DDA AI agents can adjust the level of difficulty in real-time, by targeting a zero health difference as the outcome of the fighting game. This work also demonstrates the trade-off existing between targeting the outcome exactly (Reactive OSAS) and introducing proactive behaviour (i.e., the DDA AI agent fights even if the health difference is zero) to increase the agents believability (Proactive OSAS).	en_US
dc.relation.ispartof	2017 IEEE Conference on Computational Intelligence and Games, CIG 2017	en_US
dc.relation.isbasedon	10.1109/CIG.2017.8080415	en_US
dc.title	Monte Carlo tree search based algorithms for dynamic difficulty adjustment	en_US
dc.type	Conference Proceeding
utslib.for	0803 Computer Software	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - PERSWADE - Centre on Persuasive Systems for Wise Adaptive Living
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2017 IEEE. Maintaining player immersion is a crucial step in making an enjoyable video game. One aspect of player immersion is the level of challenge the game presents to the player. To avoid a mismatch between a player's skill and the challenge of a game, which can result from traditional manual difficulty selection mechanisms (e.g. easy, medium, hard), Dynamic Difficulty Adjustment (DDA) has previously been proposed as a means of automatically detecting a player's skill and adjusting the level of challenge the game presents accordingly. This work contributes to the field of DDA by proposing a novel approach to artificially intelligent agents for opponent control. Specifically, we propose four new DDA Artificially Intelligent (AI) agents: Reactive Outcome Sensitive Action Selection (Reactive OSAS), Proactive OSAS, and their 'True' variants. These agents provide the player with an level of difficulty tailored to their skill in real-time by altering the action selection policy and the heuristic playout evaluation of Monte Carlo Tree Search. The DDA AI agents are tested within the FightingICE engine, which has been used in the past as an environment for AI agent competitions. The results of the experiments against other AI agents and human players show that these novel DDA AI agents can adjust the level of difficulty in real-time, by targeting a zero health difference as the outcome of the fighting game. This work also demonstrates the trade-off existing between targeting the outcome exactly (Reactive OSAS) and introducing proactive behaviour (i.e., the DDA AI agent fights even if the health difference is zero) to increase the agents believability (Proactive OSAS).

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/127485