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 a much 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.
KeywordsIntelligence Evolutionary Computation Artificial Life Social Intelligence Intelligence Test Universal Distribution
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- 3.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)Google Scholar
- 4.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)Google Scholar
- 6.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)Google Scholar
- 8.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)Google Scholar
- 9.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)Google Scholar
- 11.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)Google Scholar
- 13.Hibbard, B.: Bias and No Free Lunch in Formal Measures of Intelligence. Journal of Artificial General Intelligence 1(1), 54–61 (2009)Google Scholar
- 14.Krebs, J.R., Dawkins, R.: Animal signals: mind-reading and manipulation. Behavioural Ecology: an evolutionary approach 2, 380–402 (1984)Google Scholar
- 15.Langton, C.G.: Artificial life: An overview. The MIT Press, Cambridge (1997)Google Scholar
- 16.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)Google Scholar
- 18.Levin, L.A.: Universal sequential search problems. Problems of Information Transmission 9(3), 265–266 (1973)Google Scholar
- 19.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)Google Scholar