Oops, Not Now: PEARL, a RAG-Based Support Agent for Gameplay and What Players Want from AI Help

• URL: http://arxiv.org/abs/2609.13718v1
• Date: Sat, 12 Sep 2026 05:05:29 GMT
• Title: Oops, Not Now: PEARL, a RAG-Based Support Agent for Gameplay and What Players Want from AI Help
• Abstract summary: AI-powered gameplay support agents hold promise for game-based learning, yet grounding generative models in structured game data remains an open challenge.<n>We present PEARL, a dual-component Retrieval-Augmented Generation system that combines semantic knowledge retrieval with structural board-state matching.<n>PEARL operates on two input streams (natural language queries and board topology), retrieving both conceptual explanations of gameplay moves and peer-generated board states as evidence.
• Score: 16.68356031030621
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: AI-powered gameplay support agents hold promise for game-based learning, yet grounding generative models in structured game data remains an open challenge. We present PEARL (Parallel Education Agent for Reflection and Learning), a dual-component Retrieval-Augmented Generation (RAG) system that combines semantic knowledge retrieval with structural board-state matching to deliver contextualized scaffolding in Parallel, a puzzle game for learning parallel programming. PEARL operates on two input streams (natural language queries and board topology), retrieving both conceptual explanations of gameplay moves and peer-generated board states as evidence: capabilities unavailable to a standard Large Language Model (LLM) with game state access alone. In a qualitative evaluation (N=10) comparing PEARL against an existing community-based Open Player Model (OPM) visualization system, participants preferred the visualization system on perceived usefulness and reported higher frustration with PEARL; five of ten minimized or abandoned the AI tool during play. Proactive delivery, generic responses, and trust deficits drove disengagement, while a subset of four participants found PEARL's grounded explanations complementary to visualization in specific contexts where they initiated the interaction. We position PEARL as a deployed design probe whose failure modes inform a concrete design agenda for AI gameplay support, captured as seven open problems for the community.

Related papers

• MINDGAMES: A Live Arena for Evaluating Social and Strategic Reasoning in Multi-Agent LLMs [54.81359054218573]
  We introduce Mindgames, a multi-game arena and evaluation platform for large language models (LLMs)<n>Mindgames provides a unified interaction interface, TrueSkill-based rating, and full trajectory logging across four game environments.<n>We analyze 29,571 multi-agent games with turn-level observations, actions, and rewards, together with MG-Ref, a deterministic offline tournament protocol.
  arXiv  Detail & Related papers  (2026-05-28T07:33:47Z)
• Collaborative Multi-Agent Scripts Generation for Enhancing Imperfect-Information Reasoning in Murder Mystery Games [57.64247186502836]
  Vision-language models (VLMs) have shown impressive capabilities in perceptual tasks, yet they degrade in complex multi-hop reasoning under multiplayer game settings.<n>In this paper, we study a representative multiplayer task, Murder Mystery Games, which require inferring hidden truths based on partial clues provided by roles with different intentions.<n>We propose a collaborative multi-agent framework for evaluating and synthesizing high-quality, role-driven multiplayer game scripts.
  arXiv  Detail & Related papers  (2026-04-13T17:16:23Z)
• GameWorld: Towards Standardized and Verifiable Evaluation of Multimodal Game Agents [76.60994803070436]
  GameWorld is a benchmark for evaluation of Multimodal Large Language Model (MLLM) game agents in browser environments.<n>Two game agent interfaces are studied: (i) computer-use agents that directly emit keyboard and mouse controls, and (ii) generalist multimodal agents that act in a semantic action space.<n>Results across 18 model-interface pairs suggest that even the best performing agent is far from achieving human capabilities on video games.
  arXiv  Detail & Related papers  (2026-04-08T17:49:03Z)
• The Empty Quadrant: AI Teammates for Embodied Field Learning [0.0]
  Field Atlas is a framework that shifts AIED's guiding metaphor from instruction to sensemak-ing.<n>We demonstrate the framework through a museum scenario and argue that the resulting trajecto-ries constitute process-based evi-dence structurally resistant to AI fabrication.
  arXiv  Detail & Related papers  (2026-03-04T13:13:44Z)
• Who is a Better Player: LLM against LLM [53.46608216197315]
  We propose an adversarial benchmarking framework to assess the comprehensive performance of Large Language Models (LLMs) through board games competition.<n>We introduce Qi Town, a specialized evaluation platform that supports 5 widely played games and involves 20 LLM-driven players.
  arXiv  Detail & Related papers  (2025-08-05T06:41:47Z)
• Learning to Play Like Humans: A Framework for LLM Adaptation in Interactive Fiction Games [8.06073345741722]
  Interactive Fiction games (IF games) are where players interact through natural language commands.<n>This work presents a cognitively inspired framework that guides Large Language Models (LLMs) to learn and play IF games systematically.
  arXiv  Detail & Related papers  (2025-05-18T14:21:56Z)
• Playpen: An Environment for Exploring Learning Through Conversational Interaction [84.0413820245725]
  We investigate whether Dialogue Games can also serve as a source of feedback signals for learning.<n>We introduce Playpen, an environment for off- and online learning through Dialogue Game self-play.<n>We find that imitation learning through SFT improves performance on unseen instances, but negatively impacts other skills.
  arXiv  Detail & Related papers  (2025-04-11T14:49:33Z)
• Playing Non-Embedded Card-Based Games with Reinforcement Learning [18.971623378904503]
  We propose a non-embedded offline reinforcement learning training strategy to achieve real-time autonomous gameplay in the RTS game Clash Royale.<n>We extract features using state-of-the-art object detection and optical character recognition models.<n>Our method enables real-time image acquisition, perception feature fusion, decision-making, and control on mobile devices, successfully defeating built-in AI opponents.
  arXiv  Detail & Related papers  (2025-04-07T07:26:02Z)
• Evaluating and Enhancing LLMs Agent based on Theory of Mind in Guandan: A Multi-Player Cooperative Game under Imperfect Information [36.11862095329315]
  Large language models (LLMs) have shown success in handling simple games with imperfect information. This study investigates the applicability of knowledge acquired by open-source and API-based LLMs to sophisticated text-based games.
  arXiv  Detail & Related papers  (2024-08-05T15:36:46Z)
• An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games [79.23847247132345]
  This work investigates how well an artificial agent can benefit from playing guessing games when later asked to perform on novel NLP downstream tasks such as Visual Question Answering (VQA) We propose two ways to exploit playing guessing games: 1) a supervised learning scenario in which the agent learns to mimic successful guessing games and 2) a novel way for an agent to play by itself, called Self-play via Iterated Experience Learning (SPIEL)
  arXiv  Detail & Related papers  (2021-01-31T10:30:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.