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Minimum Message Length and Kolmogorov Complexity
 Computer Journal
, 1999
"... this paper is to describe some of the relationships among the different streams and to try to clarify some of the important differences in their assumptions and development. Other studies mentioning the relationships appear in [1, Section IV, pp. 10381039], [2, sections 5.2, 5.5] and [3, p. 465] ..."
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Cited by 127 (29 self)
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this paper is to describe some of the relationships among the different streams and to try to clarify some of the important differences in their assumptions and development. Other studies mentioning the relationships appear in [1, Section IV, pp. 10381039], [2, sections 5.2, 5.5] and [3, p. 465]
Beyond the Turing Test
 J. Logic, Language & Information
"... Abstract. We define the main factor of intelligence as the ability to comprehend, formalising this ability with the help of new constructs based on descriptional complexity. The result is a comprehension test, or Ctest, exclusively defined in terms of universal descriptional machines (e.g universal ..."
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Cited by 43 (20 self)
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Abstract. We define the main factor of intelligence as the ability to comprehend, formalising this ability with the help of new constructs based on descriptional complexity. The result is a comprehension test, or Ctest, exclusively defined in terms of universal descriptional machines (e.g universal Turing machines). Despite the absolute and nonanthropomorphic character of the test it is equally applicable to both humans and machines. Moreover, it correlates with classical psychometric tests, thus establishing the first firm connection between information theoretic notions and traditional IQ tests. The Turing Test is compared with the Ctest and their joint combination is discussed. As a result, the idea of the Turing Test as a practical test of intelligence should be left behind, and substituted by computational and factorial tests of different cognitive abilities, a much more useful approach for artificial intelligence progress and for many other intriguing questions that are presented beyond the Turing Test.
Universal Algorithmic Intelligence: A mathematical topdown approach
 Artificial General Intelligence
, 2005
"... Artificial intelligence; algorithmic probability; sequential decision theory; rational ..."
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Cited by 31 (7 self)
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Artificial intelligence; algorithmic probability; sequential decision theory; rational
On the computational measurement of intelligence factors
 National Institute of Standards and Technology
, 2000
"... In this paper we develop a computational framework for the measurement of different factors or abilities which are usually found in intelligent behaviours. For this, we first develop a scale for measuring the complexity of an instance of a problem, depending on the descriptional complexity (Levin LT ..."
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Cited by 23 (10 self)
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In this paper we develop a computational framework for the measurement of different factors or abilities which are usually found in intelligent behaviours. For this, we first develop a scale for measuring the complexity of an instance of a problem, depending on the descriptional complexity (Levin LT variant) of the ‘explanation ’ of the answer to the problem. We centre on the establishment of either deductive and inductive abilities, and we show that their evaluation settings are special cases of the general framework. Some classical dependencies between them are shown and a way to separate these dependencies is developed. Finally, some variants of the previous factors and other possible ones to be taken into account are investigated. In the end, the application of these measurements for the evaluation of AI progress is discussed.
Three kinds of probabilistic induction: Universal distributions and convergence theorems
 The Computer Journal
"... ..."
Complexity Preserving Functions
"... It’s widely recognized that compression is useful and even necessary for inductive learning, where a short description will capture the ‘regularities’. We introduce complexitypreserving functions that preserve these regularities of the concept. They are based on the universal information distance [B ..."
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It’s widely recognized that compression is useful and even necessary for inductive learning, where a short description will capture the ‘regularities’. We introduce complexitypreserving functions that preserve these regularities of the concept. They are based on the universal information distance [Bennett et al. ‘97] and define for an instance a set of elements sharing the same complexity type. This corresponds to the twopart code [Rissanen ‘89] of the MDL principle, when it is interpreted as the first term describing the set and the second term the element in the set [Rissanen 99]. We investigate its importance in inductive learning.
Inductive Reasoning and Chance Discovery*
"... Abstract. This paper argues that chance (risk or opportunity) discovery is challenging, from a reasoning point of view, because it represents a dilemma for inductive reasoning. Chance discovery shares many features with the grue paradox. Consequently, Bayesian approaches represent a potential soluti ..."
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Abstract. This paper argues that chance (risk or opportunity) discovery is challenging, from a reasoning point of view, because it represents a dilemma for inductive reasoning. Chance discovery shares many features with the grue paradox. Consequently, Bayesian approaches represent a potential solution. The Bayesian solution evaluates alternative models generated using a temporal logic planner to manage the chance. Surprise indices are used in monitoring the conformity of the real world and the assessed probabilities. Game theoretic approaches are proposed to deal with multiagent interaction in chance management. Key words: Bayesian confirmation, chance discovery, inductive reasoning 1.
A Comparison of Three Fitness Prediction Strategies for Interactive Genetic Algorithms *
"... The human fatigue problem is one of the most significant problems encountered by interactive genetic algorithms (IGA). Different strategies have been proposed to address this problem, such as easing evaluation methods, accelerating IGA convergence via speedup algorithms, and fitness prediction. This ..."
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The human fatigue problem is one of the most significant problems encountered by interactive genetic algorithms (IGA). Different strategies have been proposed to address this problem, such as easing evaluation methods, accelerating IGA convergence via speedup algorithms, and fitness prediction. This paper studies the performance of fitness prediction strategies. Three prediction schemes, the neural network (NN), the Bayesian learning algorithm (BLA), and a novel prediction method based on algorithmic probability (ALP), are examined. Numerical simulations are performed in order to compare the performances of these three schemes.
1.4. On Gödel’s theorem and Algorithmic Complexity........... 20
, 704
"... Information, complexity, brains and reality (Kolmogorov Manifesto) ..."