Modeling agent behavior through online evolutionary and reinforcement learning
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Junges:2011:FedCSIS,
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author = "Robert Junges and Franziska Klugl",
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title = "Modeling agent behavior through online evolutionary
and reinforcement learning",
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booktitle = "2011 Federated Conference on Computer Science and
Information Systems (FedCSIS 2011)",
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year = "2011",
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month = "18-21 " # sep,
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pages = "643--650",
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size = "8 pages",
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address = "Szczecin",
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abstract = "The process of creation and validation of an
agent-based simulation model requires the modeller to
undergo a number of prototyping, testing, analysing and
re-designing rounds. The aim is to specify and
calibrate the proper low-level agent behaviour that
truly produces the intended macro-level phenomena. We
assume that this development can be supported by agent
learning techniques, specially by generating
inspiration about behaviours as starting points for the
modeller. In this contribution we address this
learning-driven modelling task and compare two methods
that are producing decision trees: reinforcement
learning with a post-processing step for generalisation
and Genetic Programming.",
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keywords = "genetic algorithms, genetic programming, agent
behaviour modelling, agent learning technique,
agent-based simulation model, decision tree,
generalisation, learning-driven modelling task,
macrolevel phenomena, online evolutionary, redesigning
round, reinforcement learning, decision trees, learning
(artificial intelligence), multi-agent systems",
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URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6078268",
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notes = "Also known as \cite{6078268}",
- }
Genetic Programming entries for
Robert Junges
Franziska Klugl
Citations