On more realistic environment distributions for defining, evaluating and developing intelligence

One insightful view of the notion of intelligence is the ability to perform well in a diverse set of tasks, problems or environments. One of the key issues is therefore the choice of this set, which can be formalised as a `distribution¿. Formalising and properly defining this distribution is an important challenge to understand what intelligence is and to achieve artificial general intelligence (AGI). In this paper, we agree with previous criticisms that a universal distribution using a reference universal Turing machine (UTM) over tasks, environments, etc., is perhaps amuch too general distribution, since, e.g., the probability of other agents appearing on the scene or having some social interaction is almost 0 for many reference UTMs. Instead, we propose the notion of Darwin-Wallace distribution for environments, which is inspired by biological evolution, artificial life and evolutionary computation. However, although enlightening about where and how intelligence should excel, this distribution has so many options and is uncomputable in so many ways that we certainly need a more practical alternative. We propose the use of intelligence tests over multi-agent systems, in such a way that agents with a certified level of intelligence at a certain degree are used to construct the tests for the next degree. This constructive methodology can then be used as a more realistic intelligence test and also as a testbed for developing and evaluating AGI systems.We thank the anonymous reviewers for their helpful comments. We also thank the funding from the Spanish MEC and MICINN for projects TIN2009-06078-E/TIN, Consolider-Ingenio CSD2007-00022 and TIN2010-21062- C02, for MEC FPU grant AP2006-02323, and Generalitat Valenciana for Prometeo/2008/051Hernández Orallo, J.; Dowe, DL.; España Cubillo, S.; Hernández-Lloreda, MV.; Insa Cabrera, J. (2011). On more realistic environment distributions for defining, evaluating and developing intelligence. En Artificial General Intelligence. Springer Verlag (Germany). 6830:82-91. https://doi.org/10.1007/978-3-642-22887-2_9S82916830Dowe, D.L.: Foreword re C. S. Wallace. Computer Journal 51(5), 523–560 (2008); Christopher Stewart WALLACE (1933-2004) memorial special issueDowe, D.L.: Minimum Message Length and statistically consistent invariant (objective?) Bayesian probabilistic inference - from (medical) “evidence”. Social Epistemology 22(4), 433–460 (2008)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)Dowe, D.L., Hajek, A.R.: A computational extension to the Turing Test. In: 4th Conf. of the Australasian Cognitive Science Society, Newcastle, Australia (1997)Goertzel, B.: The Embodied Communication Prior: A characterization of general intelligence in the context of Embodied social interaction. In: 8th IEEE International Conference on, Cognitive Informatics, ICCI 2009, pp. 38–43. IEEE, Los Alamitos (2009)Goertzel, B., Bugaj, S.V.: AGI Preschool: a framework for evaluating early-stage human-like AGIs. In: Intl. Conf. on Artificial General Intelligence (AGI 2009) (2009)Hernández-Orallo, J.: Beyond the Turing Test. J. Logic, Language & Information 9(4), 447–466 (2000)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 (2000)Hernández-Orallo, J.: A (hopefully) non-biased universal environment class for measuring intelligence of biological and artificial systems. In: Hutter, M., et al. (eds.) Artificial General Intelligence, pp. 182–183 (2010)Hernández-Orallo, J., Dowe, D.L.: Measuring universal intelligence: Towards an anytime intelligence test. Artificial Intelligence 174(18), 1508–1539 (2010)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)Herrmann, E., Call, J., Hernández-Lloreda, M.V., Hare, B., Tomasello, M.: Humans have evolved specialized skills of social cognition: The cultural intelligence hypothesis. Science 317(5843), 1360–1366 (2007)Hibbard, B.: Bias and No Free Lunch in Formal Measures of Intelligence. Journal of Artificial General Intelligence 1(1), 54–61 (2009)Krebs, J.R., Dawkins, R.: Animal signals: mind-reading and manipulation. Behavioural Ecology: an evolutionary approach 2, 380–402 (1984)Langton, C.G.: Artificial life: An overview. The MIT Press, Cambridge (1997)Legg, S., Hutter, M.: A collection of definitions of intelligence. In: Proc. of the 2007 Conf. on Artificial General Intelligence, pp. 17–24. IOS Press, Amsterdam (2007)Legg, S., Hutter, M.: Universal intelligence: A definition of machine intelligence. Minds and Machines 17(4), 391–444 (2007)Levin, L.A.: Universal sequential search problems. Problems of Information Transmission 9(3), 265–266 (1973)Sanghi, P., Dowe, D.L.: A computer program capable of passing IQ tests. In: Proc. 4th ICCS International Conference on Cognitive Science (ICCS 2003), Sydney, Australia, pp. 570–575 (2003)Schmidhuber, J.: A computer scientist’s view of life, the universe, and everything. In: Foundations of Computer Science, p. 201. Springer, Heidelberg (1997)Schmidhuber, J.: The Speed Prior: a new simplicity measure yielding near-optimal computable predictions. In: Kivinen, J., Sloan, R.H. (eds.) COLT 2002. LNCS (LNAI), vol. 2375, pp. 123–127. Springer, Heidelberg (2002)Solomonoff, R.J.: A formal theory of inductive inference. Part I. Information and control 7(1), 1–22 (1964)Stone, P., Veloso, M.: Towards collaborative and adversarial learning: A case study in robotic soccer. Intl. J. of Human-Computers Studies 48(1), 83–104 (1998)Tomasello, M., Herrmann, E.: Ape and human cognition: What’s the difference? Current Directions in Psychological Science 19(1), 3–8 (2010

Personal intelligence expressed: A theoretical analysis

An individual\u27s cumulative life decisions help determine that person\u27s well-being. To make good decisions requires knowing something about who one is and who one wants to be. It seems plausible that personality may draw on a specifically tailored intelligence that supports its own self-understanding and contributes to such life decisions. This personal intelligence (PI) helps the individual meet his or her own personal needs and to fit in with (or stand out from) the environment. What are people high in PI actually like relative to those lower in the skills? Drawing on a 2008 theory of PI-related abilities, the author reviews several literatures to examine what features distinguish the behavior of people high in PI from those lower in such skills. The feature list sets the stage for future research in distinguishing high-PI individuals from low-PI individuals according to their life expressions