Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
ECAI '96 Selected papers from the Workshop on Distributed Artificial Intelligence Meets Machine Learning, Learning in Multi-Agent Environments
Collective and Cooperative Group Behaviors: Biologically Inspired Experiments in Robotics
The 4th International Symposium on Experimental Robotics IV
A Model of Adaptation in Collaborative Multi-Agent Systems
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Learning and Measuring Specialization in Collaborative Swarm Systems
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Dynamic Polyethism and Competition for Tasks in Threshold Reinforcement Models of Social Insects
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Analysis of Dynamic Task Allocation in Multi-Robot Systems
International Journal of Robotics Research
Adaptive task assignment for multiple mobile robots via swarm intelligence approach
Robotics and Autonomous Systems
Knowledge propagation in a distributed omnidirectional vision system
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Marco Somalvico Memorial Issue
Analysis of a stochastic model of adaptive task allocation in robots
Engineering Self-Organising Systems
Evolving team behaviors with specialization
Genetic Programming and Evolvable Machines
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Distributed learning is the learning process of multiple autonomous agents in a varying environment, where each agent has only partial information about the global task. In this paper, we investigate the influence of different reinforcement signals (local and global) and team diversity (homogeneous and heterogeneous agents) on the learned solutions. We compare the learned solutions with those obtained by systematic search in a simple case study in which pairs of agents have to collaborate in order to solve the task without any explicit communication. The results show that policies which allow teammates to specialize find an adequate diversity of the team and, in general, achieve similar or better performances than policies which force homogeneity. However, in this specific case study, the achieved team performances appear to be independent of the locality or globality of the reinforcement signal.