GameTable Working Group 1 meeting report on search, planning, learning, and explainability

Author:

Soemers Dennis J.N.J.1,Kowalski Jakub2,Piette Éric3,Morenville Achille3,Crist Walter4

Affiliation:

1. Department of Advanced Computing Sciences, Maastricht University, the Netherlands

2. Faculty of Mathematics and Computer Science, University of Wrocław, Poland

3. ICTEAM, UCLouvain, Belgium

4. Centre for the Arts in Society, Leiden University, the Netherlands

Abstract

The inaugural in-person meeting for the “GameTable” COST Action’s Working Group 1 (WG1) on Search, Planning, Learning, and Explainability took place at the Leiden Institute of Advanced Computer Science (LIACS) on January 31st, 2024. The primary aims of this meeting were to facilitate talks and discussions on, and connect researchers interested in, three core research goals: (1) human-like game-playing AI, (2) imperfect-information games within a general game playing context, and (3) explainable search and reinforcement learning in games. This report provides a summary of the discussions and talks that took place during the meeting.

Publisher

IOS Press

Reference40 articles.

1. From attribution maps to humand-understandable explanations through concept relevance propagation;Achtibat;Nature Machine Intelligence,2023

2. Björnsson, Y. & Schiffel, S. (2016). General game playing. In Handbook of Digital Games and Entertainment Technologies (pp. 1–23). Singapore: Springer Singapore.

3. Brown, N., Bakhtin, A., Lerer, A. & Gong, Q. (2020). Combining deep reinforcement learning and search for imperfect-information games. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan and H. Lin (Eds.), Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (Vol. 33, pp. 17057–17069). Curran Associates, Inc.

4. Superhuman AI for heads-up no-limit poker: Libratus beats top professionals;Brown;Science,2017

5. Superhuman AI for multiplayer poker;Brown;Science,2019

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