Cooperative multi-agent systems (MAS) are ones in which several agents attempt, through
their interaction, to jointly solve tasks or to maximize utility. Due to the interactions among the …

Providing a guided tour of the pioneering work and major technical issues, Multiagent
Robotic Systems addresses learning and adaptation in decentralized autonomous robots. Its …

Reinforcement learning (RL) is known for its efficiency and practicality in single-agent
planning, but it faces numerous challenges when applied to multi-agent scenarios. In this …

Multiagent systems and data mining have recently attracted considerable attention in the
field of computing. Reinforcement learning is the most commonly used learning process for …

We propose a new classification for multi-agent learning algorithms, with each league of
players characterized by both their possible strategies and possible beliefs. Using this …

We formulate an automatic strategy acquisition problem for the multi-agent card game
“Hearts” as a reinforcement learning problem. The problem can approximately be dealt with …

Evolving networks, which are composed of objects and relationships that change over time,
are prevalent in many real-world domains and have become an significant research topic in …

In this paper, we propose a reputation–oriented reinforcement learning algorithm for buying
and selling agents in electronic market environments. We take into account the fact that …

This paper introduces a design methodology of a fault-tolerant autonomous multi-robot
system (MRS). An important fundamental topic for this type of system is the design of an on …

Reinforcement learning is considered as a strong method for learning in multiagent systems
environments. However, it still has some drawbacks, including modeling other learning …