Compression and Intelligence: Social Environments and Communication

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.

[1]  David L. Dowe,et al.  A computer program capable of passing I.Q. tests , 2008 .

[2]  P. Schönemann.  On artificial intelligence , 1985, Behavioral and Brain Sciences.

[3]  David L. Dowe,et al.  Minimum Message Length and Kolmogorov Complexity , 1999, Comput. J..

[4]  C. S. Wallace,et al.  An Information Measure for Classification , 1968, Comput. J..

[5]  John R. Searle,et al.  Minds, brains, and programs , 1980, Behavioral and Brain Sciences.

[6]  David L. Dowe,et al.  MML, hybrid Bayesian network graphical models, statistical consistency, invarianc , 2010 .

[7]  José Hernández-Orallo,et al.  On the Computational Measurement of Intelligence Factors , 2011 .

[8]  David L. Dowe,et al.  MML clustering of multi-state, Poisson, von Mises circular and Gaussian distributions , 2000, Stat. Comput..

[9]  C. S. Wallace,et al.  MML mixture modelling of multi-state, Poisson, von Mises circular and Gaussian distributions , 1997 .

[10]  David L. Dowe,et al.  Foreword re C. S. Wallace , 2008, Comput. J..

[11]  A. M. Turing,et al.  Computing Machinery and Intelligence , 1950, The Philosophy of Artificial Intelligence.

[12]  Khalid Sayood,et al.  Introduction to Data Compression, Third Edition (Morgan Kaufmann Series in Multimedia Information and Systems) , 2005 .

[13]  Giovanni Motta,et al.  Handbook of Data Compression , 2009 .

[14]  José Hernández-Orallo,et al.  Beyond the Turing Test , 2000, J. Log. Lang. Inf..

[15]  Ernest Nagel,et al.  Scientific psychology : principles and approaches , 1965 .

[16]  J. Hernández-Orallo.  Constructive reinforcement learning , 2000 .

[17]  Shane Legg,et al.  Universal Intelligence: A Definition of Machine Intelligence , 2007, Minds and Machines.

[18]  David L. Dowe,et al.  Minimum Message Length and Statistically Consistent Invariant (Objective?) Bayesian Probabilistic Inference—From (Medical) “Evidence” , 2008 .

[19]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[20]  José Hernández-Orallo,et al.  Measuring universal intelligence: Towards an anytime intelligence test , 2010, Artif. Intell..

[21]  Peter Grünwald,et al.  Invited review of the book Statistical and Inductive Inference by Minimum Message Length , 2006 .

[22]  Joel Veness,et al.  A Monte-Carlo AIXI Approximation , 2009, J. Artif. Intell. Res..

[23]  Ray J. Solomonoff,et al.  A Formal Theory of Inductive Inference. Part I , 1964, Inf. Control..

[24]  David L. Dowe,et al.  The Turing Test , 2003 .

[25]  R. Buck,et al.  MICHAEL SCRIVEN AN ESSENTIAL UNPREDICTABILITY IN HUMAN BEHAVIOR * , 2006 .

[26]  Khalid Sayood,et al.  Introduction to Data Compression , 1996 .

[27]  G. Chaitin.  Gödel's theorem and information , 1982 .

[28]  David L. Dowe,et al.  A Non-Behavioural, Computational Extension to the Turing Test , 1998 .

[29]  Richard S. Sutton,et al.  Introduction to Reinforcement Learning , 1998 .

[30]  C. Q. Lee,et al.  The Computer Journal , 1958, Nature.

[31]  David L. Dowe,et al.  A computational extension to the Turing test , 1997 .

[32]  David Lewis,et al.  Scriven on human unpredictability , 1966 .

[33]  C. S. Wallace,et al.  Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics) , 2005 .

[34]  Ray J. Solomonoff,et al.  A Formal Theory of Inductive Inference. Part II , 1964, Inf. Control..

[35]  C. Allen,et al.  Stanford Encyclopedia of Philosophy , 2011 .

[36]  Richard S. Sutton,et al.  Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding , 1995, NIPS.

[37]  David L. Dowe,et al.  Intrinsic classification by MML - the Snob program , 1994 .