multi-agent multi-armed bandits

Definition

Multi-agent multi-armed bandits (MA-MAB) extend the classic MAB problem to scenarios with multiple interacting agents, each making sequential decisions to maximize individual or collective rewards. It's crucial for understanding decentralized decision-making and resource allocation in dynamic environments.

At a glance

Executive summary

Multi-agent multi-armed bandits (MA-MAB) model situations where multiple decision-makers learn and act in uncertain environments. Recent research highlights the importance of 'procedural fairness' in these systems, ensuring all agents have an equal say in decisions, rather than just focusing on fair outcomes.

TL;DR

It's a framework where multiple AI agents learn to make the best choices from many options, often needing to balance individual goals with fairness for everyone involved.

Key points

Multiple agents make sequential decisions to learn optimal actions in uncertain environments
Solves problems of decentralized resource allocation and decision-making with agent interaction
Used by researchers in AI, game theory, economics, and operations research for complex systems
Differs from single-agent MAB by introducing inter-agent dynamics like coordination and fairness
Current research trend focuses on incorporating diverse fairness objectives, including procedural fairness

Use cases

Dynamic spectrum allocation in wireless networks, where multiple devices compete for bandwidth.

Online recommendation systems, balancing user satisfaction with fair exposure for content creators.

Traffic signal control in smart cities, optimizing flow for multiple intersections and vehicles.

Resource management in cloud computing, allocating virtual machines to diverse user requests.

Clinical trial design, where multiple patient groups receive different treatments and outcomes are monitored.

Definition

At a glance

Executive summary

TL;DR

Key points

Use cases

Also known as

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