Installing Resilience in Distributed Constraint Optimization Operated by Physical Multi-Agent Systems

Abstract : We study the notion of k-resilient distribution of graph-structured computations supporting agent decisions, over dynamic and physical multi-agent systems. We devise a replication-based self-organizing distributed repair method, namely DRPM[MGM-2], to repair the distribution as to ensure the system still performs collective decisions and remains resilient to upcoming changes. We focus on a particular type of distributed reasoning process to repair, where computations are decision variables and constraints distributed over a set of agents. We experimentally evaluate the performances of our repair method on different topologies of multi-agent systems (uniform or problem-dependent) operating stateless DCOP algorithms (Max-Sum and A-DSA) to solve classical DCOP benchmarks (random graph, graph coloring, Ising model) while agents are disappearing.
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-02013923
Contributor : Gauthier Picard <>
Submitted on : Monday, February 11, 2019 - 12:07:23 PM
Last modification on : Wednesday, May 29, 2019 - 10:52:46 AM

Identifiers

  • HAL Id : hal-02013923, version 1

Citation

Pierre Rust, Gauthier Picard, Fano Ramparany. Installing Resilience in Distributed Constraint Optimization Operated by Physical Multi-Agent Systems. Autonomous Agents and Multiagent Systems (AAMAS) 2019, May 2019, Montréal, Canada. pp.2177-2179. ⟨hal-02013923⟩

Share

Metrics

Record views

45