GP Generation of Pedestrian Behavioral Rules in an Evacuation Model Based on SCA

摘要

This paper presents a research in the context of pedestrian dynamics according to Situated Cellular Agent (SCA), a Multi-Agent Systems approach whose roots are on Cellular Automata (CA). The aim of this work is to apply Genetic Programming (GP) approach, a well known Machine Learning method belonging to the family of Evolutionary Algorithms, to generate suitable behavioral rules for pedestrians in an evacuation scenario. The main contribution of this work is in the design of a testset of GP generated behaviors to represent basic behavioral models of evacuees populating a only locally known environment, a typical scenario for CA-based models.