Exploring and evolving process-oriented control for real and virtual fire fighting robots

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

@InProceedings{Hardey:2012:GECCO,

• author = "Kathryn Hardey and Eren Corapcioglu and Molly Mattis and Mark Goadrich and Matthew Jadud",
• title = "Exploring and evolving process-oriented control for real and virtual fire fighting robots",
• booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference",
• year = "2012",
• editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb",
• isbn13 = "978-1-4503-1177-9",
• pages = "105--112",
• keywords = "genetic algorithms, genetic programming, artificial life/robotics/evolvable hardware",
• month = "7-11 " # jul,
• organisation = "SIGEVO",
• address = "Philadelphia, Pennsylvania, USA",
• DOI = "doi:10.1145/2330163.2330179",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "Current research in evolutionary robotics is largely focused on creating controllers by either evolving neural networks or refining genetic programs based on grammar trees. We propose the use of the parallel, dataflow languages for the construction of effective robotic controllers and the evolution of new controllers using genetic programming techniques. These languages have the advantages of being built on concurrent execution frameworks that lend themselves to formal verification along with being visualized as a dataflow graph. In this paper, we compare and contrast the development and subsequent evolution of one such process-oriented control algorithm. Our control software was built from composable, communicating processes executing in parallel, and we tested our solution in an annual fire-fighting robotics competition. Subsequently, we evolved new controllers in a virtual simulation of this parallel dataflow domain, and in doing so discovered and quantified more efficient solutions. This research demonstrates the effectiveness of using process networks as the basis for evolutionary robotics.",
• notes = "Also known as \cite{2330179} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)",

}

Genetic Programming entries for Kathryn Hardey Eren Corapcioglu Molly Mattis Mark Goadrich Matthew Jadud

Citations