Alternate Social Theory Discovery Using Genetic Programming: Towards Better Understanding the Artificial Anasazi

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Gunaratne:2017:GECCO,
  author =       "Chathika Gunaratne and Ivan Garibay",
  title =        "Alternate Social Theory Discovery Using Genetic
                 Programming: Towards Better Understanding the
                 Artificial Anasazi",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4920-8",
  address =      "Berlin, Germany",
  pages =        "115--122",
  size =         "8 pages",
  URL =          "http://doi.acm.org/10.1145/3071178.3071332",
  DOI =          "doi:10.1145/3071178.3071332",
  acmid =        "3071332",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, agent-based
                 modeling, artificial anasazi, calibration, theory
                 discovery",
  month =        "15-19 " # jul,
  abstract =     "A pressing issue with agent-based model (ABM)
                 replicability is the ambiguity behind micro-behaviour
                 rules of the agents. In practice, modellers choose
                 between competing theories, each describing separate
                 candidate solutions. Pattern-oriented modelling (POM)
                 and stylized facts matching recommend testing theories
                 against patterns extracted from real-world data. Yet,
                 manually, POM is tedious and prone to human error. In
                 this study, we present a genetic programming strategy
                 to evolve debatable assumptions on agent
                 micro-behaviours. After proper modularization of the
                 candidate micro-behaviors, genetic programming can
                 discover candidate micro-behaviors which reproduce
                 patterns found in real-world data. We illustrate this
                 strategy by evolving the decision tree representing the
                 farm-seeking strategy of agents in the Artificial
                 Anasazi ABM. Through evolutionary theory discovery, we
                 obtain multiple candidate decision trees for
                 farm-seeking which fit the archaeological data better
                 than the calibrated original model in the literature.
                 We emphasize the necessity to explore a range of
                 components that influence the agents' decision making
                 process and demonstrate that this is achievable through
                 an evolutionary process if the rules are modularized as
                 required. The end result is a set of plausible
                 candidate solutions that closely fit the real-world
                 data, which can then be nominated by domain experts.",
  notes =        "Also known as
                 \cite{Gunaratne:2017:AST:3071178.3071332} GECCO-2017 A
                 Recombination of the 26th International Conference on
                 Genetic Algorithms (ICGA-2017) and the 22nd Annual
                 Genetic Programming Conference (GP-2017)",
}

Genetic Programming entries for Chathika Gunaratne Ivan Garibay

Citations