2,217 research outputs found
Differential Privacy in Cooperative Multiagent Planning
Privacy-aware multiagent systems must protect agents' sensitive data while
simultaneously ensuring that agents accomplish their shared objectives. Towards
this goal, we propose a framework to privatize inter-agent communications in
cooperative multiagent decision-making problems. We study sequential
decision-making problems formulated as cooperative Markov games with
reach-avoid objectives. We apply a differential privacy mechanism to privatize
agents' communicated symbolic state trajectories, and then we analyze tradeoffs
between the strength of privacy and the team's performance. For a given level
of privacy, this tradeoff is shown to depend critically upon the total
correlation among agents' state-action processes. We synthesize policies that
are robust to privacy by reducing the value of the total correlation. Numerical
experiments demonstrate that the team's performance under these policies
decreases by only 3 percent when comparing private versus non-private
implementations of communication. By contrast, the team's performance decreases
by roughly 86 percent when using baseline policies that ignore total
correlation and only optimize team performance
Safe Policy Synthesis in Multi-Agent POMDPs via Discrete-Time Barrier Functions
A multi-agent partially observable Markov decision process (MPOMDP) is a
modeling paradigm used for high-level planning of heterogeneous autonomous
agents subject to uncertainty and partial observation. Despite their modeling
efficiency, MPOMDPs have not received significant attention in safety-critical
settings. In this paper, we use barrier functions to design policies for
MPOMDPs that ensure safety. Notably, our method does not rely on discretization
of the belief space, or finite memory. To this end, we formulate sufficient and
necessary conditions for the safety of a given set based on discrete-time
barrier functions (DTBFs) and we demonstrate that our formulation also allows
for Boolean compositions of DTBFs for representing more complicated safe sets.
We show that the proposed method can be implemented online by a sequence of
one-step greedy algorithms as a standalone safe controller or as a
safety-filter given a nominal planning policy. We illustrate the efficiency of
the proposed methodology based on DTBFs using a high-fidelity simulation of
heterogeneous robots.Comment: 8 pages and 4 figure
08461 Abstracts Collection -- Planning in Multiagent Systems
From the 9th of November to the 14th of November 2008 the Dagstuhl Seminar
08461 \u27`Planning in Multiagent Systems\u27\u27 was held
in Schloss Dagstuhl~--~Leibniz Center for Informatics.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
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