arXiv:2508. 13213v4 Announce Type: replace Abstract: Strategic decision-making requires balancing immediate opportunities against long-term objectives: a tension fundamental to competitive environments.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2606. 26267v1 Announce Type: new Abstract: Rating systems such as Elo serve as the gold standard for matchmaking in competitive chess.
The paper presents a systematic mapping of recent chess research involving humans, engines, neural and reinforcement‑learning systems, large language models (LLMs), and hybrid approaches. It identifies 84 core study families and classifies them by agent type, strategic‑reasoning stages, and evaluation dimensions, highlighting a strong focus on situation assessment, evaluation, and action selection while noting gaps in planning, explanation, metacognition, and human–AI collaboration. The study also distinguishes hybrid systems by integration timing and cautions that improved human performance in evaluations does not automatically imply human–AI synergy.
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.
arXiv:2609.35835v1 Announce Type: cross Abstract: With the sheer constant advancements raining down in the field of Artificial Intelligence, one particular possibility that may cross our mind is whet...
arXiv:2607. 00190v1 Announce Type: cross Abstract: Recent advances in reinforcement learning have produced superhuman agents across a wide range of competitive games.
arXiv:2608. 07490v1 Announce Type: cross Abstract: Large language model agents are increasingly evaluated through games, but most benchmarks emphasize final outcomes rather than how players learn from repeated interaction.