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Errare humanum est, perseverare autem diabolicum: A Follow-Up Study on the Human-Likeness of an AI Othello Player

Published: 22 December 2023 Publication History

Abstract

Othello, also known as Reversi, is a popular 2-players board game. Olivaw is an intelligent agent playing Othello. Compared to the most famous ones (such as Saio), it exploits limited resources by autonomously learning how to improve its gameplay by playing against itself. In previous occasions, Othello players reported the impression of a sort of human-likeness in how Olivaw plays. We designed and ran an experimental study to better investigate these impressions in a controlled setting. Participants were asked to watch the moves of pre-recorded Othello games played by a human expert player against either another agent (i.e., Olivaw, Saio) or another human. The identity of the opponent, the outcome of the game (i.e., whether the human expert or the opponent player won), and the color of the players (i.e., black or white, black always playing first) were manipulated. We then asked participants to evaluate the human-likeness of the opponent player. Results confirm that the outcome of the match affects the perception of human-likeness of the players.

References

[1]
Enrico Lauletta, Beatrice Biancardi, Antonio Norelli, Maurizio Mancini, and Alessandro Panconesi. 2022. Errare humanum est? a pilot study to evaluate the human-likeness of a AI othello playing agent. In Proceedings of the 22nd ACM International Conference on Intelligent Virtual Agents. 1--3.
[2]
Antonio Norelli and Alessandro Panconesi. 2022. OLIVAW: Mastering Othello without Human Knowledge, nor a Penny. IEEE Transactions on Games (2022).
[3]
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al. 2017. Mastering the game of go without human knowledge. nature 550, 7676 (2017), 354--359.
[4]
Iskander Umarov and Maxim Mozgovoy. 2014. Creating believable and effective AI agents for games and simulations: Reviews and case study. In Contemporary Advancements in Information Technology Development in Dynamic Environments. IGI Global, 33--57.
[5]
Guojia Wan, Shirui Pan, Chen Gong, Chuan Zhou, and Gholamreza Haffari. 2021. Reasoning like human: Hierarchical reinforcement learning for knowledge graph reasoning. In International Joint Conference on Artificial Intelligence. International Joint Conference on Artificial Intelligence.

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      cover image ACM Conferences
      IVA '23: Proceedings of the 23rd ACM International Conference on Intelligent Virtual Agents
      September 2023
      376 pages
      ISBN:9781450399944
      DOI:10.1145/3570945
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      New York, NY, United States

      Publication History

      Published: 22 December 2023

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      Author Tags

      1. AI agent
      2. Othello
      3. board game
      4. human-likeness

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