Researchers have developed a new algorithm called the Distributed Team Orchestrating Algorithm (DTOA) to address challenges in zero-sum potential team games where agents rely on potentially inaccurate belief information from supervisors. The DTOA combines team fictitious play with distributed belief learning, proving convergence to a near team-Nash equilibrium. In scenarios involving Byzantine teams that misreport actions, a resilient version of DTOA is proposed, offering probabilistic guarantees for identifying honest teams and bounding the honest team-Nash gap. AI
IMPACT This research could lead to more robust coordination in multi-agent systems, particularly in scenarios with unreliable communication or adversarial participants.
RANK_REASON The cluster contains a single academic paper detailing a new algorithm and theoretical findings in multiagent systems. [lever_c_demoted from research: ic=1 ai=1.0]
Read on arXiv cs.MA (Multiagent) →
- Byzantine teams
- distributed belief learning
- Distributed Team Orchestrating Algorithm
- DTOA
- Markov decision process
- supervisor network
- team fictitious play
- team-Nash equilibrium
- team-Nash gap
- zero-sum potential team games
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