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In distributed multiplayer games, it can be difficult to communicate strategic information for planning game moves and player interactions. Often, players spend extra time communicating, reducing their engagement in the game. Visual annotations in game maps and in the gameworld can address this problem and result in more efficient player communication. We studied the impact of real-time feedback on planning annotations, specifically two different annotation types, in a custom-built, third-person, multiplayer game and analyzed their effects on player performance, experience, workload, and annotation use. We found that annotations helped engage players in collaborative planning, which reduced frustration, and shortened goal completion times. Based on these findings, we discuss how annotating in virtual game spaces enables collaborative planning and improves team performance.
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Human-Agent Cooperation in Games under Incomplete Information through Natural Language Communication
Developing autonomous agents that can strategize and cooperate with humans under information asymmetry is challenging without effective communication in natural language. We introduce a shared-control game, where two players collectively control a token in alternating turns to achieve a common objective under incomplete information. We formulate a policy synthesis problem for an autonomous agent in this game with a human as the other player. To solve this problem, we propose a communication-based approach comprising a language module and a planning module. The language module translates natural language messages into and from a finite set of flags, a compact representation defined to capture player intents. The planning module leverages these flags to compute a policy using an asymmetric information-set Monte Carlo tree search with flag exchange algorithm we present. We evaluate the effectiveness of this approach in a testbed based on Gnomes at Night, a search-and-find maze board game. Results of human subject experiments show that communication narrows the information gap between players and enhances human-agent cooperation efficiency with fewer turns.
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- Award ID(s):
- 2211432
- PAR ID:
- 10585023
- Publisher / Repository:
- International Joint Conferences on Artificial Intelligence Organization
- Date Published:
- ISBN:
- 978-1-956792-04-1
- Page Range / eLocation ID:
- 7833 to 7841
- Format(s):
- Medium: X
- Location:
- Jeju, South Korea
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.24963/ijcai.2024/867
