AI Models Tested on Dungeons & Dragons to Assess Long-Term Decision-Making
Artificial intelligence is rapidly evolving beyond simple tasks like answering questions or writing texts. Now, AI researchers are exploring innovative ways to assess complex cognitive skills, such as long-term planning, strategic decision-making, and adaptive reasoning. One of the most novel testing grounds emerging in this field is the popular tabletop role-playing game Dungeons & Dragons — offering a rich, dynamic environment where AI models must think ahead, navigate rules, and collaborate with players in extended adventures.
The rise of AI applications in real-world autonomous systems — from robotics to digital assistants — necessitates benchmarks that push models beyond short-term tasks. Traditional benchmarks often test performance on isolated queries or immediate reasoning steps. However, tasks requiring sustained strategy, resource tracking, and coordinated team interaction remain challenging for many AI systems. To address this gap, researchers have turned to Dungeons & Dragons as an imaginative and robust framework for evaluating AI models’ long-term decision-making capabilities.
Why Dungeons & Dragons Is a Unique AI Testing Ground
Dungeons & Dragons (D&D) isn’t just a game — it’s a complex narrative experience where players interact with an evolving world driven by rules, choices, risks, and rewards. In a typical campaign, adventurers make decisions that influence future scenarios, manage limited resources, and react to unpredictable outcomes — elements that closely mirror real-world strategic challenges.
Researchers at the University of California San Diego found that this complexity makes D&D an ideal testing ground for evaluating AI models designed to operate autonomously over long periods. The extended nature of D&D gameplay forces agents to plan multiple steps ahead, maintain internal state over time, and work with or against other agents — tasks that standard benchmarks often fail to test.
For instance, a D&D session can involve tracking character abilities, health, items, and evolving storylines across many turns or sessions. This requires not only understanding immediate game mechanics but also anticipating future outcomes and adjusting strategies accordingly — making it a perfect environment to test long-term reasoning and AI models’ capacities.
How Researchers Designed the AI D&D Experiments
To test AI models in this setting, the research team created a simulation where multiple language models played D&D scenarios — both cooperatively and competitively. These simulations required the models not just to generate text, but to interact with an external game engine that enforced the rules of Dungeons & Dragons. This approach minimized hallucinations and ensured that actions taken by the AI were grounded in game logic.
In these experiments, each AI model had to interpret the state of the game, make decisions, and carry out actions, whether taking on the role of a player character or controlling monsters. Researchers set up battle scenarios drawn from well-known D&D encounters like Goblin Ambush and Cragmaw Hideout, where strategic planning and tactical decisions were essential for success.
Importantly, the models were also evaluated on how well they could stay “in character,” interpret the narrative context, and communicate actions and intentions in ways that fit the role-playing scenario. This dynamic interplay between narrative understanding and strategic execution is a critical aspect of long-term decision-making, which the researchers aimed to capture.
Model Performance: Who Excelled and Who Struggled?
When comparing different AI models, researchers found that performance varied significantly. Among the models tested, Claude 3.5 Haiku emerged as the most reliable in terms of consistent decision-making and adherence to game rules. GPT-4 followed closely behind, demonstrating strong narrative comprehension and adaptability. A model named DeepSeek-V3, however, performed less effectively, highlighting the challenges many AI systems still face with sustained, rule-based planning.
These differences underscore the importance of testing AI models across diverse benchmarks that push beyond immediate reasoning. A model that performs well in short tasks doesn’t necessarily excel when the demand shifts to long-term strategy or multi-step planning. The D&D simulations revealed how some models could manage complex game states, anticipate consequences, and maintain coherence over many actions — while others struggled with resource tracking and strategic foresight.
Unexpected Behaviors and Strategic Quirks
One of the most fascinating outcomes of the research was the emergence of unconventional behaviors from AI models during gameplay. For example, characters controlled by the models sometimes started displaying quirky personality traits: goblins taunted opponents with whimsical lines mid-combat, while paladins stepped into danger delivering dramatic speeches. These behaviors weren’t preprogrammed; instead, they emerged naturally as the models tried to generate contextually rich text tied to game situations.
While such quirks might seem amusing, they hint at deeper aspects of AI cognition and language generation. When faced with complex environments like D&D, AI models don’t just calculate optimal actions — they also attempt to interpret narrative context, character identities, and situational nuance. This blend of strategy and storytelling offers new insights into how models balance functional goals (winning the encounter) with expressive behavior (in-character responses).
Expanding the Benchmark: Beyond Combat to Full Campaigns
The current phase of research primarily focused on combat scenarios — high-stakes moments within a larger D&D campaign. However, the ultimate goal for researchers is to expand these tests to full campaigns that include exploration, dialogue, and resource management over extended sessions. Such tests would further challenge AI models to synthesize long-term narratives, manage complex game economies, and collaborate with human players meaningfully.
Broadening the benchmark could also help address some of the key limitations in current AI systems. For example, keeping track of long-term character goals, adapting to evolving story arcs, and negotiating with other players are all elements that closely resemble real-world decision-making tasks, such as strategic business planning or multi-party negotiations.
Implications for Future AI Research and Applications
Using Dungeons & Dragons as a benchmark for AI models marks a major shift in how researchers think about evaluating long-term intelligence. Instead of relying solely on static tests or short-term tasks, this approach encourages the development of systems that can reason over extended interactions, anticipate future consequences, and maintain coherent behavior across multiple decision points.
These capabilities are becoming increasingly relevant as AI systems are deployed in more autonomous roles — whether guiding decisions in business strategy, planning logistics in dynamic environments, or assisting with complex creative tasks requiring sustained engagement over time. Testing AI in narrative-rich, decision-heavy environments like D&D provides a sandbox where many of these real-world skills can be evaluated and refined. Looking ahead, researchers plan to integrate even more complex game frameworks, introduce multi-player negotiation elements, and explore scenarios where AI models must collaborate and compete with humans in unpredictable environments. As benchmarks grow in complexity, so too will our understanding of what it means for an AI to think and plan like a human over long periods — a key goal in shaping the next generation of intelligent systems.
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