20,435 research outputs found
The Emergence of Norms via Contextual Agreements in Open Societies
This paper explores the emergence of norms in agents' societies when agents
play multiple -even incompatible- roles in their social contexts
simultaneously, and have limited interaction ranges. Specifically, this article
proposes two reinforcement learning methods for agents to compute agreements on
strategies for using common resources to perform joint tasks. The computation
of norms by considering agents' playing multiple roles in their social contexts
has not been studied before. To make the problem even more realistic for open
societies, we do not assume that agents share knowledge on their common
resources. So, they have to compute semantic agreements towards performing
their joint actions. %The paper reports on an empirical study of whether and
how efficiently societies of agents converge to norms, exploring the proposed
social learning processes w.r.t. different society sizes, and the ways agents
are connected. The results reported are very encouraging, regarding the speed
of the learning process as well as the convergence rate, even in quite complex
settings
Coordinated Multi-Agent Imitation Learning
We study the problem of imitation learning from demonstrations of multiple
coordinating agents. One key challenge in this setting is that learning a good
model of coordination can be difficult, since coordination is often implicit in
the demonstrations and must be inferred as a latent variable. We propose a
joint approach that simultaneously learns a latent coordination model along
with the individual policies. In particular, our method integrates unsupervised
structure learning with conventional imitation learning. We illustrate the
power of our approach on a difficult problem of learning multiple policies for
fine-grained behavior modeling in team sports, where different players occupy
different roles in the coordinated team strategy. We show that having a
coordination model to infer the roles of players yields substantially improved
imitation loss compared to conventional baselines.Comment: International Conference on Machine Learning 201
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