Learning useful communication structures for groups of agents

Coordination of altruistic agents to solve optimization problems can be significantly enhanced when inter-agent communication is allowed. In this paper we present an evolutionary approach to learn optimal communication structures for groups of agents. The agents learn to solve the Online Partitioning Problem, but our ideas can easily be adapted to other problem fields. With our approach we can find the optimal communication partners for each agent in a static environment. In a dynamic environment we figure out a simple relation between each position of agents in space and the optimal number of communication partners. A concept for the establishment of relevant communication connections between certain agents will be shown whereby the space the agents are located in will be divided into several regions. These regions will be described mathematically. After a learning process the algorithm assigns an appropriate number of communication partners for every agent in an - arbitrary located - group.1st IFIP International Conference on Biologically Inspired Cooperative Computing - CommunicationRed de Universidades con Carreras en Informática (RedUNCI

Using Cellular Automata with Evolutionary Learned Rules to Solve the Online Partitioning Problem

and scalable sets that are composed of autonomous individuals have become more and more important. The Online Partitioning Problem (OPP) deals with the distribution of huge sets of agents onto different targets in consideration of several objectives. The agents can only interact locally and there is no central instance or global knowledge. In this paper we work on this problem field by modifying ideas from the area of cellular automata (CA). We expand the well known Majority/Density Classification Task for one-dimensional CAs to two-dimensional CAs. The transition rules for the CA will be learned by using a genetic algorithm (GA). Each individual in the GA is a set of transition rules with additional distance information. This approach shows very good behaviour compared to other strategies for the OPP and is very fast once an appropriate set of rules is learned by the GA

Using Cellular Automata with Evolutionary Learned Rules to Solve the Online Partitioning Problem

In recent computer science research highly robust and scalable sets that are composed of autonomous individuals have become more and more important. The Online Partitioning Problem (OPP) deals with the distribution of huge sets of agents onto different targets in consideration of several objectives. The agents can only interact locally and there is no central instance or global knowledge. In this paper we work on this problem field by modifying ideas from the area of cellular automata (CA). We expand the well known Majority /Density Classification Task for one-dimensional CAs to two-dimensional CAs. The transition rules for the CA will be learned by using a genetic algorithm (GA). Each individual in the GA is a set of transition rules with additional distance information. This approach shows very good behaviour compared to other strategies for the OPP and is very fast once an appropriate set of rules is learned by the GA