Q-learning (QL) is a popular reinforcement learning algorithm that is guaranteed to converge
to optimal policies in Markov decision processes. However, QL exhibits an artifact: in …

Social dilemmas have attracted extensive interest in the research of multiagent systems in
order to study the emergence of cooperative behaviors among selfish agents …

In this paper, we present a new framework for large scale online kernel learning, making
kernel methods efficient and scalable for large-scale online learning applications. Unlike the …

Multi-agent systems appear in a wide variety of fields and there have been several studies
on multi-agent reinforcement learning. Dilemma problems are typical classes of multi-agent …

This paper proposes a behavioral strategy called expectation of cooperation with which
cooperation in the prisoner's dilemma game spreads over agent networks by incorporating …

This paper proposes an interaction strategy called the extended expectation-of-cooperation
(EEoC) that is intended to spread cooperative activities in prisoner's dilemma situations over …

Humans appraise the environment in daily life. We are implementing appraisal mechanisms
into reinforcement learning agents. One of such mechanisms we proposed is the utility …

This paper proposes a behavioral strategy called expectation of cooperation strategy with
which cooperation in the prisoner's dilemma game emerges in agent networks by …

It is desirable for multi-agent simulation to be run in parallel; if many agents run
simultaneously, the total run time is reduced. It is popular to use GPGPU technology as an …

Agents in open multiagent systems (OMAS) are likely to have a social trust responsibility that
is not found when multiagent societies are primarily closed. Open systems are those that …