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

Created by W.Langdon from gp-bibliography.bib Revision:1.7642

@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 S Gunaratne Ivan Garibay

Citations