Improving Multi-Agent Cooperation using Theory of Mind
- URL: http://arxiv.org/abs/2007.15703v1
- Date: Thu, 30 Jul 2020 19:31:31 GMT
- Title: Improving Multi-Agent Cooperation using Theory of Mind
- Authors: Terence X. Lim, Sidney Tio, Desmond C. Ong
- Abstract summary: We investigate how much an explicit representation of others' intentions improves performance in a cooperative game.
We find that teams with ToM agents significantly outperform non-ToM agents when collaborating with all types of partners.
These findings have implications for designing better cooperative agents.
- Score: 4.769747792846005
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advances in Artificial Intelligence have produced agents that can beat
human world champions at games like Go, Starcraft, and Dota2. However, most of
these models do not seem to play in a human-like manner: People infer others'
intentions from their behaviour, and use these inferences in scheming and
strategizing. Here, using a Bayesian Theory of Mind (ToM) approach, we
investigated how much an explicit representation of others' intentions improves
performance in a cooperative game. We compared the performance of humans
playing with optimal-planning agents with and without ToM, in a cooperative
game where players have to flexibly cooperate to achieve joint goals. We find
that teams with ToM agents significantly outperform non-ToM agents when
collaborating with all types of partners: non-ToM, ToM, as well as human
players, and that the benefit of ToM increases the more ToM agents there are.
These findings have implications for designing better cooperative agents.
Related papers
- Mutual Theory of Mind in Human-AI Collaboration: An Empirical Study with LLM-driven AI Agents in a Real-time Shared Workspace Task [56.92961847155029]
Theory of Mind (ToM) significantly impacts human collaboration and communication as a crucial capability to understand others.
Mutual Theory of Mind (MToM) arises when AI agents with ToM capability collaborate with humans.
We find that the agent's ToM capability does not significantly impact team performance but enhances human understanding of the agent.
arXiv Detail & Related papers (2024-09-13T13:19:48Z) - Enhancing Human Experience in Human-Agent Collaboration: A
Human-Centered Modeling Approach Based on Positive Human Gain [18.968232976619912]
We propose a "human-centered" modeling scheme for collaborative AI agents.
We expect that agents should learn to enhance the extent to which humans achieve these goals while maintaining agents' original abilities.
We evaluate the RLHG agent in the popular Multi-player Online Battle Arena (MOBA) game, Honor of Kings.
arXiv Detail & Related papers (2024-01-28T05:05:57Z) - Leading the Pack: N-player Opponent Shaping [52.682734939786464]
We extend Opponent Shaping (OS) methods to environments involving multiple co-players and multiple shaping agents.
We find that when playing with a large number of co-players, OS methods' relative performance reduces, suggesting that in the limit OS methods may not perform well.
arXiv Detail & Related papers (2023-12-19T20:01:42Z) - ProAgent: Building Proactive Cooperative Agents with Large Language
Models [89.53040828210945]
ProAgent is a novel framework that harnesses large language models to create proactive agents.
ProAgent can analyze the present state, and infer the intentions of teammates from observations.
ProAgent exhibits a high degree of modularity and interpretability, making it easily integrated into various coordination scenarios.
arXiv Detail & Related papers (2023-08-22T10:36:56Z) - Towards Effective and Interpretable Human-Agent Collaboration in MOBA
Games: A Communication Perspective [23.600139293202336]
This paper makes the first attempt to investigate human-agent collaboration in MOBA games.
We propose to enable humans and agents to collaborate through explicit communication by designing an efficient Meta-Command Communication-based framework.
We show that MCC agents can collaborate reasonably well with human teammates and even generalize to collaborate with different levels and numbers of human teammates.
arXiv Detail & Related papers (2023-04-23T12:11:04Z) - Mastering the Game of No-Press Diplomacy via Human-Regularized
Reinforcement Learning and Planning [95.78031053296513]
No-press Diplomacy is a complex strategy game involving both cooperation and competition.
We introduce a planning algorithm we call DiL-piKL that regularizes a reward-maximizing policy toward a human imitation-learned policy.
We show that DiL-piKL can be extended into a self-play reinforcement learning algorithm we call RL-DiL-piKL.
arXiv Detail & Related papers (2022-10-11T14:47:35Z) - Incorporating Rivalry in Reinforcement Learning for a Competitive Game [65.2200847818153]
This work proposes a novel reinforcement learning mechanism based on the social impact of rivalry behavior.
Our proposed model aggregates objective and social perception mechanisms to derive a rivalry score that is used to modulate the learning of artificial agents.
arXiv Detail & Related papers (2022-08-22T14:06:06Z) - Emergence of Theory of Mind Collaboration in Multiagent Systems [65.97255691640561]
We propose an adaptive training algorithm to develop effective collaboration between agents with ToM.
We evaluate our algorithms with two games, where our algorithm surpasses all previous decentralized execution algorithms without modeling ToM.
arXiv Detail & Related papers (2021-09-30T23:28:00Z) - Adaptive Agent Architecture for Real-time Human-Agent Teaming [3.284216428330814]
It is critical that agents infer human intent and adapt their polices for smooth coordination.
Most literature in human-agent teaming builds agents referencing a learned human model.
We propose a novel adaptive agent architecture in human-model-free setting on a two-player cooperative game.
arXiv Detail & Related papers (2021-03-07T20:08:09Z) - "Other-Play" for Zero-Shot Coordination [21.607428852157273]
Other-play learning algorithm enhances self-play by looking for more robust strategies.
We study the cooperative card game Hanabi and show that OP agents achieve higher scores when paired with independently trained agents.
arXiv Detail & Related papers (2020-03-06T00:39:37Z) - Real-World Human-Robot Collaborative Reinforcement Learning [6.089774484591287]
We present a real-world setup of a human-robot collaborative maze game, designed to be non-trivial and only solvable through collaboration.
We use deep reinforcement learning for the control of the robotic agent, and achieve results within 30 minutes of real-world play.
We present results on how co-policy learning occurs over time between the human and the robotic agent resulting in each participant's agent serving as a representation of how they would play the game.
arXiv Detail & Related papers (2020-03-02T19:34:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.