Shall We Play a Game? Language Models for Open-ended Wargames
- URL: http://arxiv.org/abs/2509.17192v2
- Date: Thu, 23 Oct 2025 02:21:49 GMT
- Title: Shall We Play a Game? Language Models for Open-ended Wargames
- Authors: Glenn Matlin, Parv Mahajan, Isaac Song, Yixiong Hao, Ryan Bard, Stu Topp, Evan Montoya, M. Rehan Parwani, Soham Shetty, Mark Riedl,
- Abstract summary: We take the position that Artificial Intelligence systems, such as Language Models (LMs), are rapidly approaching human-expert capability for strategic planning.<n>We argue the ability for AI systems to influence large-scale decisions motivates additional research into the safety, interpretability, and explainability of AI in open-ended wargames.
- Score: 1.8240882160775522
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Wargames are simulations of conflicts in which participants' decisions influence future events. While casual wargaming can be used for entertainment or socialization, serious wargaming is used by experts to explore strategic implications of decision-making and experiential learning. In this paper, we take the position that Artificial Intelligence (AI) systems, such as Language Models (LMs), are rapidly approaching human-expert capability for strategic planning -- and will one day surpass it. Military organizations have begun using LMs to provide insights into the consequences of real-world decisions during _open-ended wargames_ which use natural language to convey actions and outcomes. We argue the ability for AI systems to influence large-scale decisions motivates additional research into the safety, interpretability, and explainability of AI in open-ended wargames. To demonstrate, we conduct a scoping literature review with a curated selection of 100 unclassified studies on AI in wargames, and construct a novel ontology of open-endedness using the creativity afforded to players, adjudicators, and the novelty provided to observers. Drawing from this body of work, we distill a set of practical recommendations and critical safety considerations for deploying AI in open-ended wargames across common domains. We conclude by presenting the community with a set of high-impact open research challenges for future work.
Related papers
- FAIRGAME: a Framework for AI Agents Bias Recognition using Game Theory [51.96049148869987]
We present FAIRGAME, a Framework for AI Agents Bias Recognition using Game Theory.<n>We describe its implementation and usage, and we employ it to uncover biased outcomes in popular games among AI agents.<n>Overall, FAIRGAME allows users to reliably and easily simulate their desired games and scenarios.
arXiv Detail & Related papers (2025-04-19T15:29:04Z) - Conversation Games and a Strategic View of the Turing Test [0.0]
We focus on a subset of the games, called verdict games.<n>In a verdict game, two players alternate to contribute to a conversation, which is evaluated at each stage by a non-strategic judge.<n>We show the practical relevance of the proposed concepts by simulation experiments, and show that a strategic agent outperforms a naive agent by a high margin.
arXiv Detail & Related papers (2025-01-30T16:08:37Z) - Mastering Board Games by External and Internal Planning with Language Models [30.782334791241556]
We show that search-based planning can yield significant improvements in Large Language Models game-playing strength.<n>We introduce, compare and contrast two major approaches: in external search, the model guides Monte Carlo Tree Search rollouts and evaluations without calls to an external game engine, and in internal search, the model is trained to generate in-context a linearized tree of search and a resulting final choice.<n>Our proposed approach, combining search with domain knowledge, is not specific to board games, hinting at more general future applications.
arXiv Detail & Related papers (2024-12-02T18:56:51Z) - Combining AI Control Systems and Human Decision Support via Robustness and Criticality [53.10194953873209]
We extend a methodology for adversarial explanations (AE) to state-of-the-art reinforcement learning frameworks.
We show that the learned AI control system demonstrates robustness against adversarial tampering.
In a training / learning framework, this technology can improve both the AI's decisions and explanations through human interaction.
arXiv Detail & Related papers (2024-07-03T15:38:57Z) - Open-Ended Wargames with Large Language Models [3.2228025627337864]
We introduce "Snow Globe," an LLM-powered multi-agent system for playing qualitative wargames.
We describe its software architecture conceptually and release an open-source implementation alongside this publication.
We discuss potential applications of the approach and how it fits into the broader wargaming ecosystem.
arXiv Detail & Related papers (2024-04-17T14:54:58Z) - Human vs. Machine: Behavioral Differences Between Expert Humans and Language Models in Wargame Simulations [1.6108153271585284]
We show that large language models (LLMs) behave differently compared to humans in high-stakes military decision-making scenarios.
Our results motivate policymakers to be cautious before granting autonomy or following AI-based strategy recommendations.
arXiv Detail & Related papers (2024-03-06T02:23:32Z) - CivRealm: A Learning and Reasoning Odyssey in Civilization for
Decision-Making Agents [63.79739920174535]
We introduce CivRealm, an environment inspired by the Civilization game.
CivRealm stands as a unique learning and reasoning challenge for decision-making agents.
arXiv Detail & Related papers (2024-01-19T09:14:11Z) - War and Peace (WarAgent): Large Language Model-based Multi-Agent
Simulation of World Wars [40.489161847202325]
We propose textbfWarAgent, an LLM-powered multi-agent AI system, to simulate historical international conflicts.
By evaluating the simulation effectiveness, we examine the advancements and limitations of cutting-edge AI systems' abilities.
Our findings offer data-driven and AI-augmented insights that can redefine how we approach conflict resolution and peacekeeping strategies.
arXiv Detail & Related papers (2023-11-28T20:59:49Z) - ALYMPICS: LLM Agents Meet Game Theory -- Exploring Strategic
Decision-Making with AI Agents [77.34720446306419]
Alympics is a systematic simulation framework utilizing Large Language Model (LLM) agents for game theory research.
Alympics creates a versatile platform for studying complex game theory problems.
arXiv Detail & Related papers (2023-11-06T16:03:46Z) - SPRING: Studying the Paper and Reasoning to Play Games [102.5587155284795]
We propose a novel approach, SPRING, to read the game's original academic paper and use the knowledge learned to reason and play the game through a large language model (LLM)
In experiments, we study the quality of in-context "reasoning" induced by different forms of prompts under the setting of the Crafter open-world environment.
Our experiments suggest that LLMs, when prompted with consistent chain-of-thought, have great potential in completing sophisticated high-level trajectories.
arXiv Detail & Related papers (2023-05-24T18:14:35Z) - The Ethics of AI in Games [4.691076280925923]
Video games are one of the richest and most popular forms of human-computer interaction.
As artificial intelligence (AI) tools are gradually adopted by the game industry a series of ethical concerns arise.
This paper calls for an open dialogue and action for the games of today and the virtual spaces of the future.
arXiv Detail & Related papers (2023-05-12T11:41:05Z) - A Survey of Decision Making in Adversarial Games [8.489977267389934]
In many practical applications, such as poker, chess, evader pursuing, drug interdiction, coast guard, cyber-security, and national defense, players often have apparently adversarial stances.
This paper provides a systematic survey on three main game models widely employed in adversarial games.
arXiv Detail & Related papers (2022-07-16T16:04:01Z) - AI in Games: Techniques, Challenges and Opportunities [40.86375378643978]
Various game AI systems (AIs) have been developed such as Libratus, OpenAI Five and AlphaStar, beating professional human players.
In this paper, we survey recent successful game AIs, covering board game AIs, card game AIs, first-person shooting game AIs and real time strategy game AIs.
arXiv Detail & Related papers (2021-11-15T09:35:53Z) - Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society.
A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests.
This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z) - Learning to Play Imperfect-Information Games by Imitating an Oracle
Planner [77.67437357688316]
We consider learning to play multiplayer imperfect-information games with simultaneous moves and large state-action spaces.
Our approach is based on model-based planning.
We show that the planner is able to discover efficient playing strategies in the games of Clash Royale and Pommerman.
arXiv Detail & Related papers (2020-12-22T17:29:57Z) - Exploration Based Language Learning for Text-Based Games [72.30525050367216]
This work presents an exploration and imitation-learning-based agent capable of state-of-the-art performance in playing text-based computer games.
Text-based computer games describe their world to the player through natural language and expect the player to interact with the game using text.
These games are of interest as they can be seen as a testbed for language understanding, problem-solving, and language generation by artificial agents.
arXiv Detail & Related papers (2020-01-24T03:03:51Z)
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.