1 research outputs found
Scalable Decision-Theoretic Planning in Open and Typed Multiagent Systems
In open agent systems, the set of agents that are cooperating or competing
changes over time and in ways that are nontrivial to predict. For example, if
collaborative robots were tasked with fighting wildfires, they may run out of
suppressants and be temporarily unavailable to assist their peers. We consider
the problem of planning in these contexts with the additional challenges that
the agents are unable to communicate with each other and that there are many of
them. Because an agent's optimal action depends on the actions of others, each
agent must not only predict the actions of its peers, but, before that, reason
whether they are even present to perform an action. Addressing openness thus
requires agents to model each other's presence, which becomes computationally
intractable with high numbers of agents. We present a novel, principled, and
scalable method in this context that enables an agent to reason about others'
presence in its shared environment and their actions. Our method extrapolates
models of a few peers to the overall behavior of the many-agent system, and
combines it with a generalization of Monte Carlo tree search to perform
individual agent reasoning in many-agent open environments. Theoretical
analyses establish the number of agents to model in order to achieve acceptable
worst case bounds on extrapolation error, as well as regret bounds on the
agent's utility from modeling only some neighbors. Simulations of multiagent
wildfire suppression problems demonstrate our approach's efficacy compared with
alternative baselines.Comment: Pre-print with appendices for AAAI 202