Abstract
In this paper, we introduce some new methodologies in a general path problem. Finding a good path is always a desirable task and it can be also crucial in emergency and panic situations, in which people tend to assume different and unpredictable behaviors. In this paper, we analyse an escape situation in which the environment is a labyrinth and people are agents that act as two different kinds of ant colonies. In particular, we assume that people act according to opposite behaviors: (i) cooperatively, helping each other and the group; (ii) non cooperatively, helping just themselves, and no caring about the rest of the group. So, we use in a path problem an Ant Colony Algorithm based on two breeds of colonies: a cooperative and a non-cooperative one. We imagine that their task is to find the exit of the labyrinth making decisions according to the ACO rules and according to their breed. Every breed has, in fact, two different strategies. Via a game theory approach, we investigate how these two strategies affect the final payoff of each breed.
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Crespi, C., Fargetta, G., Pavone, M., Scollo, R.A., Scrimali, L. (2020). A Game Theory Approach for Crowd Evacuation Modelling. In: Filipič, B., Minisci, E., Vasile, M. (eds) Bioinspired Optimization Methods and Their Applications. BIOMA 2020. Lecture Notes in Computer Science(), vol 12438. Springer, Cham. https://doi.org/10.1007/978-3-030-63710-1_18
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