Compression and intelligence: social environments and communication

  • Authors:

  • David L. Dowe;José Hernández-Orallo;Paramjit K. Das

  • Affiliations:

  • Computer Science and Software Engineering, Clayton School of Information Technology, Monash University, Australia;DSIC, Universitat Politècnica de València, València, Spain;Computer Science and Software Engineering, Clayton School of Information Technology, Monash University, Australia

  • Venue:
  • AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
  • Year:
  • 2011

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Abstract

Compression has been advocated as one of the principles which pervades inductive inference and prediction - and, from there, it has also been recurrent in definitions and tests of intelligence. However, this connection is less explicit in new approaches to intelligence. In this paper, we advocate that the notion of compression can appear again in definitions and tests of intelligence through the concepts of 'mindreading' and 'communication' in the context of multi-agent systems and social environments. Our main position is that two-part Minimum Message Length (MML) compression is not only more natural and effective for agents with limited resources, but it is also much more appropriate for agents in (co-operative) social environments than one-part compression schemes - particularly those using a posterior-weighted mixture of all available models following Solomonoff's theory of prediction. We think that the realisation of these differences is important to avoid a naive view of 'intelligence as compression' in favour of a better understanding of how, why and where (one-part or two-part, lossless or lossy) compression is needed.