Player-centered game AI from a flow perspective: Towards a better understanding of past trends and future directions

Cruz, Christian Arzate; Uresti, Jorge Adolfo Ramírez

doi:10.1016/j.entcom.2017.02.003

Cited by 36 publications

(18 citation statements)

References 30 publications

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“…The subscript in this naming notation represents the sub-problem it is intended to solve, while the superscript indicates the archetype behavior for the agent. Thus, the subtasks {M st 1 1 M st 2 1 } are designed to tackle the same state-action space, and achieve the same goal, but with different approaches.…”

Section: Hierarchical Decompositionmentioning

confidence: 99%

“…In recent years, the Game AI community has made many efforts to accomplish a better understanding of how Theory of Flow constructs would be essential to improve current approaches in the player-centered subarea [1]. We can argue that the main reasons to study flow in the context of Game AI are the effects of achieving this state of optimal experience; that is, people enjoy the most when achieving this subjective state of consciousness [2].…”

Section: Introductionmentioning

confidence: 99%

“…We can argue that the main reasons to study flow in the context of Game AI are the effects of achieving this state of optimal experience; that is, people enjoy the most when achieving this subjective state of consciousness [2]. In more common terms, we can define flow as a lasting and deep state of immersion [1].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, creating more immersive experiences is key to enhancing players' experience while playing a game. An approach to meet this goal is generating believable behaviors for Non-Player Characters (NPCs) [1]. A believable NPC behaves in a manner that makes it indistinguishable from human players.…”

Section: Introductionmentioning

confidence: 99%

“…A believable NPC behaves in a manner that makes it indistinguishable from human players. Therefore, to approach the design of believable NPCs, we need to identify which traits characterize a human-like behavior [3,4], and how those traits can be achieved through artificial intelligence techniques (AI) [1,[5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

HRLB⌃2: A Reinforcement Learning Based Framework for Believable Bots

Cruz

Uresti

2018

Applied Sciences

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The creation of believable behaviors for Non-Player Characters (NPCs) is key to improve the players’ experience while playing a game. To achieve this objective, we need to design NPCs that appear to be controlled by a human player. In this paper, we propose a hierarchical reinforcement learning framework for believable bots (HRLB⌃2). This novel approach has been designed so it can overcome two main challenges currently faced in the creation of human-like NPCs. The first difficulty is exploring domains with high-dimensional state–action spaces, while satisfying constraints imposed by traits that characterize human-like behavior. The second problem is generating behavior diversity, by also adapting to the opponent’s playing style. We evaluated the effectiveness of our framework in the domain of the 2D fighting game named Street Fighter IV. The results of our tests demonstrate that our bot behaves in a human-like manner.

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Section: Hierarchical Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%