Computation in artificially evolved, non-uniform cellular automata
Theoretical Computer Science - Special issue: cellular automata
Evolution of Parallel Cellular Machines: The Cellular Programming Approach
Evolution of Parallel Cellular Machines: The Cellular Programming Approach
SOS++: finding smart behaviors using learning and evolution
ICAL 2003 Proceedings of the eighth international conference on Artificial life
Evolving Multi-creature Systems for All-to-All Communication
ACRI '08 Proceedings of the 8th international conference on Cellular Automata for Reseach and Industry
Solving All-to-All Communication with CA Agents More Effectively with Flags
PaCT '09 Proceedings of the 10th International Conference on Parallel Computing Technologies
Optimal 6-state algorithms for the behavior of several moving creatures
ACRI'06 Proceedings of the 7th international conference on Cellular Automata for Research and Industry
PaCT'07 Proceedings of the 9th international conference on Parallel Computing Technologies
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We modeled a multi-agent system as a two-dimensional Cellular Automata and searched for a rule in order to solve the all-to-all communication task in shortest time. The rule contains two finite state machines (FSM) controlling the behavior of the uniform agents. The moving FSM controls the moving actions and the color FSM controls the changing of the cell's color. Colors are used for indirect communication. In addition the agents receive an acknowledgment whenever they meet and communicate successfully. The FSMs were evolved by a genetic algorithm. It could be shown that acknowledging and especially coloring increases the performance of the agents. Certain initial configurations cannot be solved without coloring. Even with coloring, symmetric configurations cannot be solved when the initial colors are the same.