Work-in-Progress: A Hybrid Collaborative Multi Agent Decision Making Algorithm With Factored-Value Max-Plus

Cotae, Paul; Reindorf, Nii Emil Alexander; Kang, Myong; Velazquez, Alexander

doi:10.1109/blackseacom58138.2023.10299698

Cited by 1 publication

(2 citation statements)

References 14 publications

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“…The only requirement for our algorithm is that the domain is "connected" regardless of the number of edges. The algorithm is provided in [24] and it is part of the Hybrid Max-Plus Algorithm with Cost as described next.…”

Section: Distributed Max-plus Algorithm With Costmentioning

confidence: 99%

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Collaborative Cost Multi-Agent Decision-Making Algorithm with Factored-Value Monte Carlo Tree Search and Max-Plus

Alexander-Reindorf,

Cotae

2023

Games

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In this paper, we describe the Factored Value MCTS Hybrid Cost-Max-Plus algorithm, a collection of decision-making algorithms (centralized, decentralized, and hybrid) for a multi-agent system in a collaborative setting that considers action costs. Our proposed algorithm is made up of two steps. In the first step, each agent searches for the best individual actions with the lowest cost using the Monte Carlo Tree Search (MCTS) algorithm. Each agent’s most promising activities are chosen and presented to the team. The Hybrid Cost Max-Plus method is utilized for joint action selection in the second step. The Hybrid Cost Max-Plus algorithm improves the well-known centralized and distributed Max-Plus algorithm by incorporating the cost of actions in agent interactions. The Max-Plus algorithm employed the Coordination Graph framework, which exploits agent dependencies to decompose the global payoff function as the sum of local terms. In terms of the number of agents and their interactions, the suggested Factored Value MCTS-Hybrid Cost-Max-Plus method is online, anytime, distributed, and scalable. Our contribution competes with state-of-the-art methodologies and algorithms by leveraging the locality of agent interactions for planning and acting utilizing MCTS and Max-Plus algorithms.

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Section: Distributed Max-plus Algorithm With Costmentioning

confidence: 99%

“…Games 2023, 14, 75 13 of 20 is provided in [24] and it is part of the Hybrid Max-Plus Algorithm with Cost as described next.…”

Section: Hybrid Max-plus Algorithm With Costmentioning

confidence: 99%

Collaborative Cost Multi-Agent Decision-Making Algorithm with Factored-Value Monte Carlo Tree Search and Max-Plus

Alexander-Reindorf,

Cotae

2023

Games

Self Cite

View full text Add to dashboard Cite

show abstract

Work-in-Progress: A Hybrid Collaborative Multi Agent Decision Making Algorithm With Factored-Value Max-Plus

Cited by 1 publication

References 14 publications

Collaborative Cost Multi-Agent Decision-Making Algorithm with Factored-Value Monte Carlo Tree Search and Max-Plus

Collaborative Cost Multi-Agent Decision-Making Algorithm with Factored-Value Monte Carlo Tree Search and Max-Plus

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