arXiv:2607. 09196v1 Announce Type: new Abstract: Designing balanced adversaries is a central but labor-intensive task in tabletop role-playing game (TTRPG) development.
arXiv:2609.25001v1 Announce Type: new Abstract: Modern video games provide a measurable testbed for AI models, combining abilities of visual understanding, instruction decomposition, goal planning, a...
arXiv:2607. 00190v1 Announce Type: cross Abstract: Recent advances in reinforcement learning have produced superhuman agents across a wide range of competitive games.
arXiv:2609.16679v1 Announce Type: new Abstract: Foundation models, alongside advances in learned game-world models, are reshaping AI across the game lifecycle. Beyond playing games, recent systems mo...
arXiv:2609.38881v1 Announce Type: new Abstract: Real-time strategy (RTS) games require agents to coordinate economic development, production and construction, base defense, unit organization, and att...
The paper introduces the Abstraction Agent, a zero‑shot pipeline that employs a large language model to automatically generate continuous strategic features from a natural‑language game description, score private states, and cluster them into abstraction buckets without any game‑specific evaluators or training data. The pipeline consists of four phases—feature discovery with calibration anchors, batched private‑state scoring, correlation‑based feature selection, and k‑means clustering—and achieves significant reductions in lifted‑strategy exploitability in heads‑up no‑limit Texas hold’em and outperforms scalar rank baselines in ROVER Trials. The method also transfers to other games such as four‑card Pot‑Limit Omaha, HUNL preflop and flop, and Riichi Mahjong, demonstrating that it can uncover strategic concepts that align with recognized game theory insights.
arXiv:2606. 20210v1 Announce Type: new Abstract: Immersion in video games depends not only on graphics, audio, and game mechanics, but also on the quality of in-game characters.
arXiv:2607. 11501v1 Announce Type: new Abstract: Accurately modeling and understanding player experience is crucial for designing engaging puzzle games.
arXiv:2510. 11503v2 Announce Type: replace-cross Abstract: Games have long been a microcosm for studying planning and reasoning in both natural and artificial intelligence (AI), often focusing on expert-level or even super-human play.
Accurately modeling and understanding player experience is crucial for designing engaging puzzle games. To achieve this, a common approach involves collecting diverse user data to train predictive playtesting models that mimic player behavior.
arXiv:2305.17327v4 Announce Type: replace Abstract: Imperfect Information Games (IIGs) are used to model games under uncertainty or lack complete information. Counterfactual Regret Minimization (CFR)...
arXiv:2607. 00527v1 Announce Type: new Abstract: Generative AI now enables games to produce dialogue, quests, characters, images, and worlds at runtime.
The paper introduces JSON-Bag VF, a game-agnostic method for training value functions using JSON-Bag prototypes derived from tokenized game trajectories. It demonstrates that Random Forest-based feature selection and game-stage-specific feature selection enhance performance, and that these selections are more critical than prototype-tokenization. Experiments on six tabletop games show that JSON-Bag OSLA outperforms baseline one-step-look-ahead agents in most cases.