ArticlePatterns (New York, N.Y.)2023
A brain-inspired theory of mind spiking neural network improves multi-agent cooperation and competition.
Article in Patterns (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 14 citations in OpenAlex.
- Multi-agent game theory for precision psychiatry.Frontiers in psychiatry · 2026Article
- BrainCog: A spiking neural network based, brain-inspired cognitive intelligence engine for brain-inspired AI and brain simulation.Patterns (New York, N.Y.) · 2023Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
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Abstract
During dynamic social interaction, inferring and predicting others' behaviors through theory of mind (ToM) is crucial for obtaining benefits in cooperative and competitive tasks. Current multi-agent reinforcement learning (MARL) methods primarily rely on agent observations to select behaviors, but they lack inspiration from ToM, which limits performance. In this article, we propose a multi-agent ToM decision-making (MAToM-DM) model, which consists of a MAToM spiking neural network (MAToM-SNN) module and a decision-making module. We design two brain-inspired ToM modules (Self-MAToM and Other-MAToM) to predict others' behaviors based on self-experience and observations of others, respectively. Each agent can adjust its behavior according to the predicted actions of others. The effectiveness of the proposed model has been demonstrated through experiments conducted in cooperative and competitive tasks. The results indicate that integrating the ToM mechanism can enhance cooperation and competition efficiency and lead to higher rewards compared with traditional MARL models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.