Integrating Planning, Execution and Monitoring in the presence of Open World Novelties: Case Study of an Open World Monopoly Solver

• URL: http://arxiv.org/abs/2107.04303v1
• Date: Fri, 9 Jul 2021 08:26:28 GMT
• Title: Integrating Planning, Execution and Monitoring in the presence of Open World Novelties: Case Study of an Open World Monopoly Solver
• Abstract summary: Monopoly was one of the test beds chosen for the DARPA-SAILON program. We developed an agent that eschews complete plans, and adapts it's policy online as the game evolves.
• Score: 21.170242411005955
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The game of monopoly is an adversarial multi-agent domain where there is no fixed goal other than to be the last player solvent, There are useful subgoals like monopolizing sets of properties, and developing them. There is also a lot of randomness from dice rolls, card-draws, and adversaries' strategies. This unpredictability is made worse when unknown novelties are added during gameplay. Given these challenges, Monopoly was one of the test beds chosen for the DARPA-SAILON program which aims to create agents that can detect and accommodate novelties. To handle the game complexities, we developed an agent that eschews complete plans, and adapts it's policy online as the game evolves. In the most recent independent evaluation in the SAILON program, our agent was the best performing agent on most measures. We herein present our approach and results.

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)
• 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)
• Minibal: Balanced Game-Playing Without Opponent Modeling [2.5788559173418357]
  Minibal is a variant of Minimax specifically designed for balanced play.<n>Experiments conducted across seven board games demonstrate that one variant consistently achieves the most balanced play.
  arXiv  Detail & Related papers  (2026-03-24T10:56:52Z)
• Monopoly Deal: A Benchmark Environment for Bounded One-Sided Response Games [0.0]
  Card games are widely used to study sequential decision-making under uncertainty.<n>We term games featuring bounded one-sided response games (BORGs)<n>We introduce a modified version of Monopoly Deal as a benchmark environment that isolates this dynamic.
  arXiv  Detail & Related papers  (2025-10-29T01:38:19Z)
• Multi-agent Multi-armed Bandits with Stochastic Sharable Arm Capacities [69.34646544774161]
  We formulate a new variant of multi-player multi-armed bandit (MAB) model, which captures arrival of requests to each arm and the policy of allocating requests to players. The challenge is how to design a distributed learning algorithm such that players select arms according to the optimal arm pulling profile. We design an iterative distributed algorithm, which guarantees that players can arrive at a consensus on the optimal arm pulling profile in only M rounds.
  arXiv  Detail & Related papers  (2024-08-20T13:57:00Z)
• Leading the Pack: N-player Opponent Shaping [52.682734939786464]
  We extend Opponent Shaping (OS) methods to environments involving multiple co-players and multiple shaping agents. We find that when playing with a large number of co-players, OS methods' relative performance reduces, suggesting that in the limit OS methods may not perform well.
  arXiv  Detail & Related papers  (2023-12-19T20:01:42Z)
• Preference-conditioned Pixel-based AI Agent For Game Testing [1.5059676044537105]
  Game-testing AI agents that learn by interaction with the environment have the potential to mitigate these challenges. This paper proposes an agent design that mainly depends on pixel-based state observations while exploring the environment conditioned on a user's preference. Our agent significantly outperforms state-of-the-art pixel-based game testing agents over exploration coverage and test execution quality when evaluated on a complex open-world environment resembling many aspects of real AAA games.
  arXiv  Detail & Related papers  (2023-08-18T04:19:36Z)
• TiZero: Mastering Multi-Agent Football with Curriculum Learning and Self-Play [19.98100026335148]
  TiZero is a self-evolving, multi-agent system that learns from scratch. It outperforms previous systems by a large margin on the Google Research Football environment.
  arXiv  Detail & Related papers  (2023-02-15T08:19:18Z)
• Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions [145.54544979467872]
  We propose and analyze new fictitious play policy optimization algorithms for zero-sum Markov games with structured but unknown transitions. We prove tight $widetildemathcalO(sqrtK)$ regret bounds after $K$ episodes in a two-agent competitive game scenario. Our algorithms feature a combination of Upper Confidence Bound (UCB)-type optimism and fictitious play under the scope of simultaneous policy optimization.
  arXiv  Detail & Related papers  (2022-07-25T18:29:16Z)
• Generating Diverse and Competitive Play-Styles for Strategy Games [58.896302717975445]
  We propose Portfolio Monte Carlo Tree Search with Progressive Unpruning for playing a turn-based strategy game (Tribes) We show how it can be parameterized so a quality-diversity algorithm (MAP-Elites) is used to achieve different play-styles while keeping a competitive level of play. Our results show that this algorithm is capable of achieving these goals even for an extensive collection of game levels beyond those used for training.
  arXiv  Detail & Related papers  (2021-04-17T20:33:24Z)
• Playing Against the Board: Rolling Horizon Evolutionary Algorithms Against Pandemic [3.223284371460913]
  This paper contends that collaborative board games pose a different challenge to artificial intelligence as it must balance short-term risk mitigation with long-term winning strategies. This paper focuses on the exemplary collaborative board game Pandemic and presents a rolling evolutionary algorithm for this game.
  arXiv  Detail & Related papers  (2021-03-28T09:22:10Z)
• Collaborative Agent Gameplay in the Pandemic Board Game [3.223284371460913]
  Pandemic is an exemplar collaborative board game where all players coordinate to overcome challenges posed by events occurring during the game's progression. This paper proposes an artificial agent which controls all players' actions and balances chances of winning versus risk of losing in this highly Evolutionary environment. Results show that the proposed algorithm can find winning strategies more consistently in different games of varying difficulty.
  arXiv  Detail & Related papers  (2021-03-21T13:18:20Z)
• Learning Monopoly Gameplay: A Hybrid Model-Free Deep Reinforcement Learning and Imitation Learning Approach [31.066718635447746]
  Reinforcement Learning (RL) relies on an agent interacting with an environment to maximize the cumulative sum of rewards received by it. In multi-player Monopoly game, players have to make several decisions every turn which involves complex actions, such as making trades. This paper introduces a Hybrid Model-Free Deep RL (DRL) approach that is capable of playing and learning winning strategies of Monopoly.
  arXiv  Detail & Related papers  (2021-03-01T01:40:02Z)
• 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)
• The Design Of "Stratega": A General Strategy Games Framework [62.997667081978825]
  Stratega is a framework for creating turn-based and real-time strategy games. The framework has been built with a focus on statistical forward planning (SFP) agents. We hope that the development of this framework and its respective agents helps to better understand the complex decision-making process in strategy games.
  arXiv  Detail & Related papers  (2020-09-11T20:02:00Z)

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.