Many real-world tasks involve multiple agents with partial observability and limited
communication. Learning is challenging in these settings due to local viewpoints of agents …

Much of the success of single agent deep reinforcement learning (DRL) in recent years can
be attributed to the use of experience replay memories (ERM), which allow Deep Q …

Recent trends in pervasive computing promote the vision of Collective Adaptive Systems
(CASs): large-scale collections of relatively simple agents that act and coordinate with no …

Multiagent systems (MASs) have received extensive attention in a variety of domains, such
as robotics and distributed control. This paper focuses on how independent learners (ILs …

Particle swarm optimisation (PSO) is a general purpose optimisation algorithm used to
address hard optimisation problems. The algorithm operates as a result of a number of …

The effectiveness distributed database systems highly depends on the state of site that its
task is to allocate fragments. This allocation purpose is performed for obtaining the minimum …

This paper considers the uniform machine scheduling problem with release dates so as to
minimize the total completion time. The problem is known to be NP-hard in the strong sense …

Abstract The Iterated Prisoner's Dilemma (IPD) game has been commonly used to
investigate the cooperation among competitors. However, most previous studies on the IPD …

Cloud computing is the ability to provide new technologies and standard cloud services.
One of the essential features of cloud computing is the provision of “unlimited” computer …

In reinforcement learning algorithms, leveraging multiple views of the environment can
improve the learning of complicated policies. In multi-view environments, due to the fact that …