Abstract
We present agents that perform well against humans in imperfect information games with partially observable actions. We introduce the Semi-Determinized-MCTS (SDMCTS), a variant of the Information Set MCTS algorithm (ISMCTS). SDMCTS generates a predictive model of the unobservable portion of the opponent's actions from historical behavioral data. Next, SDMCTS performs simulations on an instance of the game where the unobservable portion of the opponent's actions are determined. Thereby, it facilitates the use of the predictive model in order to decrease uncertainty. We present an implementation of the SDMCTS applied to the Cheat Game. Results from experiments with 120 subjects playing a head-to-head Cheat Game against our SDMCTS agents suggest that SDMCTS performs well against humans, and its performance improves as the predictive model's accuracy increases.
| Original language | English |
|---|---|
| Title of host publication | 17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018 |
| Publisher | International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) |
| Pages | 1874-1876 |
| Number of pages | 3 |
| ISBN (Print) | 9781510868083 |
| State | Published - 2018 |
| Event | 17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018 - Stockholm, Sweden Duration: 10 Jul 2018 → 15 Jul 2018 |
Publication series
| Name | Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS |
|---|---|
| Volume | 3 |
| ISSN (Print) | 1548-8403 |
| ISSN (Electronic) | 1558-2914 |
Conference
| Conference | 17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018 |
|---|---|
| Country/Territory | Sweden |
| City | Stockholm |
| Period | 10/07/18 → 15/07/18 |
Bibliographical note
Publisher Copyright:© 2018 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
Keywords
- Agents competing against humans