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Compression and intelligence: social environments and communication

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Compression and intelligence: social environments and communication

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dc.contributor.author Dowe, David L. es_ES
dc.contributor.author Hernández Orallo, José es_ES
dc.contributor.author Das, Paramjit K, es_ES
dc.date.accessioned 2014-02-25T12:35:03Z
dc.date.issued 2011
dc.identifier.isbn 978-3-642-22886-5
dc.identifier.issn 0302-9743
dc.identifier.uri http://hdl.handle.net/10251/35960
dc.description.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 `mind-reading¿ 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. es_ES
dc.description.sponsorship We thank the anonymous reviewers for their helpful comments, and we thank Kurt Kleiner for some challenging and ultimately very helpful questions in the broad area of this work. We also acknowledge the funding from the Spanish MEC and MICINN for projects TIN2009-06078-E/TIN, Consolider-Ingenio CSD2007-00022 and TIN2010-21062-C02, and Generalitat Valenciana for Prometeo/2008/051.
dc.format.extent 8 es_ES
dc.language Inglés es_ES
dc.publisher Springer Verlag (Germany) es_ES
dc.relation.ispartof Artificial General Intelligence es_ES
dc.relation.ispartofseries Lecture Notes in Computer Science;vol. 6830
dc.rights Reserva de todos los derechos es_ES
dc.subject Two-part compression es_ES
dc.subject Minimum Message Length (MML) es_ES
dc.subject Solomonoff theory of prediction es_ES
dc.subject Tests of intelligence es_ES
dc.subject Communication es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Compression and intelligence: social environments and communication es_ES
dc.type Capítulo de libro es_ES
dc.embargo.lift 10000-01-01
dc.embargo.terms forever es_ES
dc.identifier.doi 10.1007/978-3-642-22887-2_21
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//TIN2009-06078-E/ES/ANYTIME UNIVERSAL INTELLIGENCE/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MEC//CSD2007-00022/ES/Agreement Technologies/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//PROMETEO08%2F2008%2F051/ES/Advances on Agreement Technologies for Computational Entities (atforce)/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//TIN2010-21062-C02-02/ES/SWEETLOGICS-UPV/
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Dowe, DL.; Hernández Orallo, J.; Das, PK. (2011). Compression and intelligence: social environments and communication. En Artificial General Intelligence. Springer Verlag (Germany). 6830:204-211. https://doi.org/10.1007/978-3-642-22887-2_21 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename 4th International Conference, AGI 2011 es_ES
dc.relation.conferencedate August 3-6, 2011 es_ES
dc.relation.conferenceplace Mountain View, CA, USA es_ES
dc.relation.publisherversion http://link.springer.com/chapter/10.1007/978-3-642-22887-2_21 es_ES
dc.description.upvformatpinicio 204 es_ES
dc.description.upvformatpfin 211 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 6830 es_ES
dc.relation.senia 201443
dc.contributor.funder Ministerio de Ciencia e Innovación
dc.contributor.funder Generalitat Valenciana
dc.contributor.funder Ministerio de Educación y Ciencia es_ES
dc.description.references Chaitin, G.J.: Godel’s theorem and information. International Journal of Theoretical Physics 21(12), 941–954 (1982) es_ES
dc.description.references Dowe, D.L.: Foreword re C. S. Wallace. Computer Journal 51(5), 523–560 (2008); Christopher Stewart WALLACE (1933-2004) memorial special issue es_ES
dc.description.references Dowe, D.L.: Minimum Message Length and statistically consistent invariant (objective?) Bayesian probabilistic inference - from (medical) “evidence”. Social Epistemology 22(4), 433–460 (2008) es_ES
dc.description.references Dowe, D.L.: MML, hybrid Bayesian network graphical models, statistical consistency, invariance and uniqueness. In: Bandyopadhyay, P.S., Forster, M.R. (eds.) Handbook of the Philosophy of Science. Philosophy of Statistics, vol. 7, pp. 901–982. Elsevier, Amsterdam (2011) es_ES
dc.description.references Dowe, D.L., Hajek, A.R.: A computational extension to the Turing Test. Technical Report #97/322, Dept Computer Science, Monash University, Melbourne, Australia, 9 pp (1997) es_ES
dc.description.references Dowe, D.L., Hajek, A.R.: A non-behavioural, computational extension to the Turing Test. In: Intl. Conf. on Computational Intelligence & multimedia applications (ICCIMA 1998), Gippsland, Australia, pp. 101–106 (February 1998) es_ES
dc.description.references Hernández-Orallo, J.: Beyond the Turing Test. J. Logic, Language & Information 9(4), 447–466 (2000) es_ES
dc.description.references Hernández-Orallo, J.: Constructive reinforcement learning. International Journal of Intelligent Systems 15(3), 241–264 (2000) es_ES
dc.description.references Hernández-Orallo, J.: On the computational measurement of intelligence factors. In: Meystel, A. (ed.) Performance metrics for intelligent systems workshop, pp. 1–8. National Institute of Standards and Technology, Gaithersburg, MD, U.S.A (2000) es_ES
dc.description.references Hernández-Orallo, J., Dowe, D.L.: Measuring universal intelligence: Towards an anytime intelligence test. Artificial Intelligence 174(18), 1508–1539 (2010) es_ES
dc.description.references Hernández-Orallo, J., Minaya-Collado, N.: A formal definition of intelligence based on an intensional variant of Kolmogorov complexity. In: Proc. Intl Symposium of Engineering of Intelligent Systems (EIS 1998), pp. 146–163. ICSC Press (1998) es_ES
dc.description.references Legg, S., Hutter, M.: Universal intelligence: A definition of machine intelligence. Minds and Machines 17(4), 391–444 (2007) es_ES
dc.description.references Lewis, D.K., Shelby-Richardson, J.: Scriven on human unpredictability. Philosophical Studies: An International Journal for Philosophy in the Analytic Tradition 17(5), 69–74 (1966) es_ES
dc.description.references Oppy, G., Dowe, D.L.: The Turing Test. In: Zalta, E.N. (ed.) Stanford Encyclopedia of Philosophy, Stanford University, Stanford (2011), http://plato.stanford.edu/entries/turing-test/ es_ES
dc.description.references Salomon, D., Motta, G., Bryant, D.C.O.N.: Handbook of data compression. Springer-Verlag New York Inc., Heidelberg (2009) es_ES
dc.description.references Sanghi, P., Dowe, D.L.: A computer program capable of passing I.Q. tests. In: 4th International Conference on Cognitive Science (and 7th Australasian Society for Cognitive Science Conference), vol. 2, pp. 570–575. Univ. of NSW, Sydney, Australia (July 2003) es_ES
dc.description.references Sayood, K.: Introduction to data compression. Morgan Kaufmann, San Francisco (2006) es_ES
dc.description.references Scriven, M.: An essential unpredictability in human behavior. In: Wolman, B.B., Nagel, E. (eds.) Scientific Psychology: Principles and Approaches, pp. 411–425. Basic Books (Perseus Books), New York (1965) es_ES
dc.description.references Searle, J.R.: Minds, brains and programs. Behavioural and Brain Sciences 3, 417–457 (1980) es_ES
dc.description.references Solomonoff, R.J.: A formal theory of inductive inference. Part I. Information and control 7(1), 1–22 (1964) es_ES
dc.description.references Sutton, R.S.: Generalization in reinforcement learning: Successful examples using sparse coarse coding. Advances in neural information processing systems, 1038–1044 (1996) es_ES
dc.description.references Sutton, R.S., Barto, A.G.: Reinforcement learning: An introduction. The MIT Press, Cambridge (1998) es_ES
dc.description.references Turing, A.M.: Computing machinery and intelligence. Mind 59, 433–460 (1950) es_ES
dc.description.references Veness, J., Ng, K.S., Hutter, M., Silver, D.: A Monte Carlo AIXI Approximation. Journal of Artificial Intelligence Research, JAIR 40, 95–142 (2011) es_ES
dc.description.references Wallace, C.S.: Statistical and Inductive Inference by Minimum Message Length. Springer, Heidelberg (2005) es_ES
dc.description.references Wallace, C.S., Boulton, D.M.: An information measure for classification. Computer Journal 11(2), 185–194 (1968) es_ES
dc.description.references Wallace, C.S., Dowe, D.L.: Intrinsic classification by MML - the Snob program. In: Proc. 7th Australian Joint Conf. on Artificial Intelligence, pp. 37–44. World Scientific, Singapore (November 1994) es_ES
dc.description.references Wallace, C.S., Dowe, D.L.: Minimum message length and Kolmogorov complexity. Computer Journal 42(4), 270–283 (1999); Special issue on Kolmogorov complexity es_ES
dc.description.references Wallace, C.S., Dowe, D.L.: MML clustering of multi-state, Poisson, von Mises circular and Gaussian distributions. Statistics and Computing 10, 73–83 (2000) es_ES


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