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Social Laws for Multi-agent Coordination in Stochastic Environments

Paper recorded by Signals 4 on 2026-09-16 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-16 on arXiv · recorded by Signals 4 on 2026-09-17

Category: cs.AI · 人工智能 · first seen 2026-09-17

Abstract

In multi-agent environments, coordinating agents to prevent interference and ensure robust individual performance is a critical challenge. Previous research on social laws for multi-agent systems has primarily focused on deterministic, goal-based settings. This paper extends the concept of social laws to stochastic, reward-based environments, proposing a formalism for defining and verifying their

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#35 most recent of 300 cs.AI papers we have recorded · ↑ newer: RLLBC-Lib: An Educational Code Library for Reinforcement Learning and · ↓ older: Higher-order pruning of experts in mixture-of-experts language models
Cite this page: Social Laws for Multi-agent Coordination in Stochastic Environments: the #35 most recent of 300 cs.AI papers we have recorded (as of 2026-09-16). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/social-laws-for-multi-agent-coordination-in-stochastic-environments.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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