{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/62656"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/62656","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Parameter search in an agent-based model of pedestrian movement in retail environments","abstract":"Parameter search in an agent-based model of pedestrian movement in retail environments is part of a research effort by data-driven architecture in the Cognitive Machine Group at the MIT Media Lab. The approach pursued in this thesis is agent-based modeling, with an ultimate goal to use generative behaviors in agents to study effects of architectural and managerial decisions on retail environments. In this thesis, I designed and implemented an agent training module as a part of a software system which simulates and learns patterns of human pedestrian movement in retail environments. 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