Technical Note: \cal Q-Learning
Machine Learning
Adapting Self-Adaptive Parameters in Evolutionary Algorithms
Applied Intelligence
Learning to Be Thoughtless: Social Norms and Individual Computation
Computational Economics
An Evolutionary Dynamical Analysis of Multi-Agent Learning in Iterated Games
Autonomous Agents and Multi-Agent Systems
Self-organizing learning array and its application to economic and financial problems
Information Sciences: an International Journal
Shaping multi-agent systems with gradient reinforcement learning
Autonomous Agents and Multi-Agent Systems
Simulation for behavior learning of multi-agent robot
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Differential evolution with dynamic stochastic selection for constrained optimization
Information Sciences: an International Journal
A study of self-organization mechanisms in ad hoc and sensor networks
Computer Communications
A reinforcement learning model for supply chain ordering management: An application to the beer game
Decision Support Systems
Ensemble strategies with adaptive evolutionary programming
Information Sciences: an International Journal
Multi-goal Q-learning of cooperative teams
Expert Systems with Applications: An International Journal
A note on the learning effect in multi-agent optimization
Expert Systems with Applications: An International Journal
Speeding up learning automata based multi agent systems using the concepts of stigmergy and entropy
Expert Systems with Applications: An International Journal
Self-adaptive learning based particle swarm optimization
Information Sciences: an International Journal
Reinforcement learning approach to goal-regulation in a self-evolutionary manufacturing system
Expert Systems with Applications: An International Journal
Adaptive learning in service operations
Decision Support Systems
Induced states in a decision tree constructed by Q-learning
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In order to improve the ability of achieving good performance in self-organizing teams, this paper presents a self-adaptive learning algorithm for team members. Members of the self-organizing teams are simulated by agents. In the virtual self-organizing team, agents adapt their knowledge according to cooperative principles. The self-adaptive learning algorithm is approached to learn from other agents with minimal costs and improve the performance of the self-organizing team. In the algorithm, agents learn how to behave (choose different game strategies) and how much to think about how to behave (choose the learning radius). The virtual team is self-adaptively improved according to the strategies' ability of generating better quality solutions in the past generations. Six basic experiments are manipulated to prove the validity of the adaptive learning algorithm. It is found that the adaptive learning algorithm often causes agents to converge to optimal actions, based on agents' continually updated cognitive maps of how actions influence the performance of the virtual self-organizing team. This paper considered the influence of relationships in self-organizing teams over existing works. It is illustrated that the adaptive learning algorithm is beneficial to both the development of self-organizing teams and the performance of the individual agent.