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.
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