Automatic Rule Identification for Agent-Based Crowd Models Through Gene Expression Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Zhong:2014:AAMAS,
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author = "Jinghui Zhong and Linbo Luo and Wentong Cai and
Michael Lees",
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title = "Automatic Rule Identification for Agent-Based Crowd
Models Through Gene Expression Programming",
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booktitle = "13th International Conference on Autonomous Agents and
Multiagent Systems (AAMAS 2014)",
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metis_id = "402420",
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year = "2014",
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editor = "Alessio Lomuscio and Paul Scerri and Ana Bazzan and
Michael Huhns",
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pages = "1125)",
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address = "Paris",
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month = "5-9 " # may,
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publisher = "ACM",
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keywords = "genetic algorithms, genetic programming, gene
expression programming, agent-based modelling, crowd
simulation, decision rules, evolutionary algorithm",
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isbn13 = "978-1-4503-2738-1",
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URL = "http://aamas2014.lip6.fr/proceedings/aamas/p1125.pdf",
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size = "8 pages",
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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.",
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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
Citations