
Distributed Policy Synthesis of MultiAgent Systems With Graph Temporal Logic Specifications
We study the distributed synthesis of policies for multiagent systems t...
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Distributed Locally Noninterfering Connectivity via Linear Temporal Logic
In this paper, we consider networks of static sensors with integrated se...
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Compositional planning in Markov decision processes: Temporal abstraction meets generalized logic composition
In hierarchical planning for Markov decision processes (MDPs), temporal ...
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Interpretable Apprenticeship Learning with Temporal Logic Specifications
Recent work has addressed using formulas in linear temporal logic (LTL) ...
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Synthesis of Provably Correct Autonomy Protocols for Shared Control
We synthesize shared control protocols subject to probabilistic temporal...
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Controlling a random population
Bertrand et al. introduced a model of parameterised systems, where each ...
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Verifiable Planning in Expected Reward Multichain MDPs
The planning domain has experienced increased interest in the formal syn...
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Policy Synthesis for Factored MDPs with Graph Temporal Logic Specifications
We study the synthesis of policies for multiagent systems to implement spatialtemporal tasks. We formalize the problem as a factored Markov decision process subject to socalled graph temporal logic specifications. The transition function and the spatialtemporal task of each agent depend on the agent itself and its neighboring agents. The structure in the model and the specifications enable to develop a distributed algorithm that, given a factored Markov decision process and a graph temporal logic formula, decomposes the synthesis problem into a set of smaller synthesis problems, one for each agent. We prove that the algorithm runs in time linear in the total number of agents. The size of the synthesis problem for each agent is exponential only in the number of neighboring agents, which is typically much smaller than the number of agents. We demonstrate the algorithm in case studies on disease control and urban security. The numerical examples show that the algorithm can scale to hundreds of agents.
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