Automatic Rule Identification for Agent-Based Crowd Models Through Gene Expression Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Zhong:2014:AAMAS,
  author =       "Jinghui Zhong and Linbo Luo and Wentong Cai and 
                 Michael Lees",
  title =        "Automatic Rule Identification for Agent-Based Crowd
                 Models Through Gene Expression Programming",
  booktitle =    "13th International Conference on Autonomous Agents and
                 Multiagent Systems (AAMAS 2014)",
  metis_id =     "402420",
  year =         "2014",
  editor =       "Alessio Lomuscio and Paul Scerri and Ana Bazzan and 
                 Michael Huhns",
  pages =        "1125)",
  address =      "Paris",
  month =        "5-9 " # may,
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, agent-based modelling, crowd
                 simulation, decision rules, evolutionary algorithm",
  isbn13 =       "978-1-4503-2738-1",
  URL =          "http://aamas2014.lip6.fr/proceedings/aamas/p1125.pdf",
  size =         "8 pages",
  abstract =     "Agent-based modelling of human crowds has now become
                 an important and active research field, with a wide
                 range of applications such as military training,
                 evacuation analysis and digital game. One of the
                 significant and challenging tasks in agent-based crowd
                 modelling is the design of decision rules for agents,
                 so as to reproduce desired emergent phenomena
                 behaviours. The common approach in agent-based crowd
                 modelling is to design decision rules empirically based
                 on model developer's experiences and domain specific
                 knowledge. In this paper, an evolutionary framework is
                 proposed to automatically extract decision rules for
                 agent-based crowd models, so as to reproduce an
                 objective crowd behaviour. To automate the rule
                 extraction process, the problem of finding optimal
                 decision rules from objective crowd behaviours is
                 formulated as a symbolic regression problem. An
                 evolutionary framework based on gene expression
                 programming is developed to solve the problem. The
                 proposed algorithm is tested using crowd evacuation
                 simulations in three scenarios with differing
                 complexity. Our results demonstrate the feasibility of
                 the approach and shows that our algorithm is able to
                 find decision rules for agents, which in turn can
                 generate the prescribed macro-scale dynamics.",
  notes =        "info@ifaamas.org http://aamas2014.lip6.fr/
                 http://aamas2014.lip6.fr/tools/pdf/AAMAS2014_booklet.pdf",
}

Genetic Programming entries for Jinghui Zhong Linbo Luo Wentong Cai Michael Lees

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