Modeling agent behavior through online evolutionary and reinforcement learning

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Junges:2011:FedCSIS,

• author = "Robert Junges and Franziska Klugl",
• title = "Modeling agent behavior through online evolutionary and reinforcement learning",
• booktitle = "2011 Federated Conference on Computer Science and Information Systems (FedCSIS 2011)",
• year = "2011",
• month = "18-21 " # sep,
• pages = "643--650",
• size = "8 pages",
• address = "Szczecin",
• 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.",
• 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",
• URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6078268",
• notes = "Also known as \cite{6078268}",

}

Genetic Programming entries for Robert Junges Franziska Klugl

Citations