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Policy Synthesis for Factored MDPs with Graph Temporal Logic Specifications
We study the synthesis of policies for multi-agent systems to implement ...
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Scalable Planning in Multi-Agent MDPs
Multi-agent Markov Decision Processes (MMDPs) arise in a variety of appl...
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Anytime and Efficient Coalition Formation with Spatial and Temporal Constraints
The Coalition Formation with Spatial and Temporal constraints Problem (C...
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Decomposing GR(1) Games with Singleton Liveness Guarantees for Efficient Synthesis
Temporal logic based synthesis approaches are often used to find traject...
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Distributed Synthesis of Surveillance Strategies for Mobile Sensors
We study the problem of synthesizing strategies for a mobile sensor netw...
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Distributed Locally Non-interfering Connectivity via Linear Temporal Logic
In this paper, we consider networks of static sensors with integrated se...
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Distributed Communication-aware Motion Planning for Multi-agent Systems from STL and SpaTeL Specifications
In future intelligent transportation systems, networked vehicles coordin...
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Distributed Policy Synthesis of Multi-Agent Systems With Graph Temporal Logic Specifications
We study the distributed synthesis of policies for multi-agent systems to perform spatial-temporal tasks. We formalize the synthesis problem as a factored Markov decision process subject to graph temporal logic specifications. The transition function and task of each agent is a function of the agent itself and its neighboring agents. By leveraging the structure in the model, and the specifications, we develop a distributed algorithm that decomposes the problem into a set of smaller problems, one for each agent. We show that the running time of the algorithm is linear in the number of agents. The size of the problem for each agent is exponential only in the number of neighboring agents, which is typically much smaller than the number of agents. If the transition function of each agent does not depend on its neighboring agents, we show that we can simplify the algorithm, which improves the runtime by multiple orders of magnitude. We demonstrate the algorithms in case studies on disease control, urban security, and ground robot surveillance. The numerical examples show that the algorithms can scale to hundreds of agents with hundreds of states per agent.
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