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
Solving the exploration's problem with several creatures more efficiently
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
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
Are several creatures more efficient than a single one?
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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The goal is to optimize the behavior of moving creatures by using "time-shuffling" techniques. The "creatures' exploration problem" is used as an example for a multi-agent problem modeled by cellular automata. The task of the creatures is to visit all empty cells in an environment with a minimum number of steps. The behavior of a creature is modeled by an automaton taking care of the collisions. Time-shuffling means that two behaviors (algorithms X and Y) are sequentially alternated with a certain time period. Ten different "uniform" (non-time-shuffled) algorithms with good performance from former investigations were used. We defined three time-shuffling modes differing in the way how the algorithms are interchanged. New metrics are used for such multi-agent systems, especially the success rate(number of successful explored environments) and the mean normalized work(cost). Time-shuffled systems with a time period of around 100 have resulted in much better success rates and lower cost compared to the uniform systems.