Keke AI Competition: Solving puzzle levels in a dynamically changing mechanic space

• URL: http://arxiv.org/abs/2209.04911v1
• Date: Sun, 11 Sep 2022 17:50:27 GMT
• Title: Keke AI Competition: Solving puzzle levels in a dynamically changing mechanic space
• Authors: M Charity and Julian Togelius
• Abstract summary: The Keke AI Competition introduces an artificial agent competition for the game Baba is You. The paper describes the framework and evaluation metrics used to rank submitted agents.
• Score: 5.2508303190856624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Keke AI Competition introduces an artificial agent competition for the game Baba is You - a Sokoban-like puzzle game where players can create rules that influence the mechanics of the game. Altering a rule can cause temporary or permanent effects for the rest of the level that could be part of the solution space. The nature of these dynamic rules and the deterministic aspect of the game creates a challenge for AI to adapt to a variety of mechanic combinations in order to solve a level. This paper describes the framework and evaluation metrics used to rank submitted agents and baseline results from sample tree search agents.

Related papers

• Personalized Dynamic Difficulty Adjustment -- Imitation Learning Meets Reinforcement Learning [44.99833362998488]
  In this work, we explore balancing game difficulty using machine learning-based agents to challenge players based on their current behavior. This is achieved by a combination of two agents, in which one learns to imitate the player, while the second is trained to beat the first. In our demo, we investigate the proposed framework for personalized dynamic difficulty adjustment of AI agents in the context of the fighting game AI competition.
  arXiv  Detail & Related papers  (2024-08-13T11:24:12Z)
• Reinforcement Learning for High-Level Strategic Control in Tower Defense Games [47.618236610219554]
  In strategy games, one of the most important aspects of game design is maintaining a sense of challenge for players. We propose an automated approach that combines traditional scripted methods with reinforcement learning. Results show that combining a learned approach, such as reinforcement learning, with a scripted AI produces a higher-performing and more robust agent than using only AI.
  arXiv  Detail & Related papers  (2024-06-12T08:06:31Z)
• Toward Human-AI Alignment in Large-Scale Multi-Player Games [24.784173202415687]
  We analyze extensive human gameplay data from Xbox's Bleeding Edge (100K+ games) We find that while human players exhibit variability in fight-flight and explore-exploit behavior, AI players tend towards uniformity. These stark differences underscore the need for interpretable evaluation, design, and integration of AI in human-aligned applications.
  arXiv  Detail & Related papers  (2024-02-05T22:55:33Z)
• 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)
• TotalBotWar: A New Pseudo Real-time Multi-action Game Challenge and Competition for AI [62.997667081978825]
  TotalBotWar is a new pseudo real-time multi-action challenge for game AI. The game is based on the popular TotalWar games series where players manage an army to defeat the opponent's one.
  arXiv  Detail & Related papers  (2020-09-18T09:13:56Z)
• Moody Learners -- Explaining Competitive Behaviour of Reinforcement Learning Agents [65.2200847818153]
  In a competitive scenario, the agent does not only have a dynamic environment but also is directly affected by the opponents' actions. Observing the Q-values of the agent is usually a way of explaining its behavior, however, do not show the temporal-relation between the selected actions.
  arXiv  Detail & Related papers  (2020-07-30T11:30:42Z)
• Learning from Learners: Adapting Reinforcement Learning Agents to be Competitive in a Card Game [71.24825724518847]
  We present a study on how popular reinforcement learning algorithms can be adapted to learn and to play a real-world implementation of a competitive multiplayer card game. We propose specific training and validation routines for the learning agents, in order to evaluate how the agents learn to be competitive and explain how they adapt to each others' playing style.
  arXiv  Detail & Related papers  (2020-04-08T14:11:05Z)

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.