Automatic discovery of algorithms for multi-agent systems

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Sjors {van Berkel} and Daniel Turi and 
                 Andrei Pruteanu and Stefan Dulman",
  title =        "Automatic discovery of algorithms for multi-agent
  booktitle =    "GECCO 2012 Evolutionary computation and multi-agent
                 systems and simulation (ECoMASS)",
  year =         "2012",
  editor =       "Forrest Stonedahl and Rick Riolo",
  isbn13 =       "978-1-4503-1178-6",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "337--344",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Philadelphia, Pennsylvania, USA",
  DOI =          "doi:10.1145/2330784.2330833",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Automatic algorithm generation for large-scale
                 distributed systems is one of the holy grails of
                 artificial intelligence and agent-based modeling. It
                 has direct applicability in future engineered
                 (embedded) systems, such as mesh networks of sensors
                 and actuators where there is a high need to harness
                 their capabilities via algorithms that have good
                 scalability characteristics. NetLogo has been
                 extensively used as a teaching and research tool by
                 computer scientists, for example for exploring
                 distributed algorithms. Inventing such an algorithm
                 usually involves a tedious reasoning process for each
                 individual idea. In this paper, we report preliminary
                 results in our effort to push the boundary of the
                 discovery process even further, by replacing the
                 classical approach with a guided search strategy that
                 makes use of genetic programming targeting the NetLogo
                 simulator. The effort moves from a manual model
                 implementation to an automated discovery process. The
                 only activity that is required is the implementation of
                 primitives and the configuration of the tool-chain. In
                 this paper, we explore the capabilities of our
                 framework by re-inventing five well-known distributed
  notes =        "Also known as \cite{2330833} Distributed at

                 ACM Order Number 910122.",

Genetic Programming entries for Sjors van Berkel Daniel Turi Andrei Pruteanu Stefan Dulman