Wains: A pattern-seeking artificial life species

Amy De Buitléir, Michael Russell, Mark Daly

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

We describe the initial phase of a research project to develop an artificial life framework designed to extract knowledge from large data sets with minimal preparation or ramp-up time. In this phase, we evolved an artificial life population with a new brain architecture. The agents have sufficient intelligence to discover patterns in data and to make survival decisions based on those patterns. The species uses diploid reproduction, Hebbian learning, and Kohonen self-organizing maps, in combination with novel techniques such as using pattern-rich data as the environment and framing the data analysis as a survival problem for artificial life. The first generation of agents mastered the pattern discovery task well enough to thrive. Evolution further adapted the agents to their environment by making them a little more pessimistic, and also by making their brains more efficient.

Original languageEnglish
Pages (from-to)399-423
Number of pages25
JournalArtificial Life
Volume18
Issue number4
DOIs
Publication statusPublished - Oct 2012

Keywords

  • Agent
  • Artificial life
  • Data mining
  • Diploid
  • Wain

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