Artificial General Intelligence

Volume 6830 of the series Lecture Notes in Computer Science pp 21-30

Coherence Progress: A Measure of Interestingness Based on Fixed Compressors

  • Tom SchaulAffiliated withIDSIA, University of Lugano
  • , Leo PapeAffiliated withIDSIA, University of Lugano
  • , Tobias GlasmachersAffiliated withIDSIA, University of Lugano
  • , Vincent GrazianoAffiliated withIDSIA, University of Lugano
  • , Jürgen SchmidhuberAffiliated withIDSIA, University of Lugano

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The ability to identify novel patterns in observations is an essential aspect of intelligence. In a computational framework, the notion of a pattern can be formalized as a program that uses regularities in observations to store them in a compact form, called a compressor. The search for interesting patterns can then be stated as a search to better compress the history of observations. This paper introduces coherence progress, a novel, general measure of interestingness that is independent of its use in a particular agent and the ability of the compressor to learn from observations. Coherence progress considers the increase in coherence obtained by any compressor when adding an observation to the history of observations thus far. Because of its applicability to any type of compressor, the measure allows for an easy, quick, and domain-specific implementation. We demonstrate the capability of coherence progress to satisfy the requirements for qualitatively measuring interestingness on a Wikipedia dataset.


compression interestingness curiosity wikipedia