Design and Modelling of Hanabi-Playing Agents using Artificial Intelligence

Authors

  • Shweta Pramodrao Sontakke  P. G. Student, Department of Computer Engineering, Bapurao Deshmukh College of Engineering, Sevagram Wardha, India
  • Dr. A. N. Thakare  Assistant Professor, Department of Computer Engineering, Department of Computer Engineering Bapurao Deshmukh College of Engineering, Sevagram, Wardha, India

Keywords:

Set-Monte Carlo Tree Search, Hanabi-Playing Agents, Artificial Intelligence

Abstract

In order to affect your own activities, agent modelling entails thinking about how other agents will behave. In this work, we look at how agent modelling is used in the collaborative card game Hanabi, which is based on concealed information. In addition to an Information Set-Monte Carlo Tree Search (IS-MCTS) agent, we implement a variety of rule-based agents from the literature as well as our own inventions. We see that IS-MCTS produces poor results, so we create a new predictor version that incorporates a model of the agents with whom it is matched. This agent outperforms IS-MCTS in terms of game-playing strength, owing to its consideration of what the other agents in the game would do. We also develop a faulty rule-based agent to demonstrate the predictor's capability with such an agent.

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International Journal of Scientific Research in Science and Technology

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Published

2021-04-10

Issue

Volume 9, Issue 1, March-April-2021

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Shweta Pramodrao Sontakke, Dr. A. N. Thakare, " Design and Modelling of Hanabi-Playing Agents using Artificial Intelligence, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 9, Issue 1, pp.1364-1373, March-April-2021.