English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We study the synthesis of policies for multi-agent systems to implementspatial-temporal tasks. We formalize the problem as a factored Markov decisionprocess subject to so-called graph temporal logic specifications. Thetransition function and the spatial-temporal task of each agent depend on theagent itself and its neighboring agents. The structure in the model and thespecifications enable to develop a distributed algorithm that, given a factoredMarkov decision process and a graph temporal logic formula, decomposes thesynthesis 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 thenumber of neighboring agents, which is typically much smaller than the numberof agents. We demonstrate the algorithm in case studies on disease control andurban security. The numerical examples show that the algorithm can scale tohundreds of agents.

Policy Synthesis for Factored MDPs with Graph Temporal Logic Specifications