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14
Inferring Conservation Laws in Particle Physics: A Case Study
 in the Problem of Induction”, The British Journal for the Philosophy of Science, Forthcoming
, 2001
"... This paper develops a meansends analysis of an inductive problem that arises in particle physics: how to infer from observed reactions conservation principles that govern all reactions among elementary particles. I show that there is a reliable inference procedure that is guaranteed to arrive at an ..."
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Cited by 16 (3 self)
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This paper develops a meansends analysis of an inductive problem that arises in particle physics: how to infer from observed reactions conservation principles that govern all reactions among elementary particles. I show that there is a reliable inference procedure that is guaranteed to arrive at an empirically adequate set of conservation principles as more and more evidence is obtained. An interesting feature of reliable procedures for finding conservation principles is that in certain precisely defined circumstances they must introduce hidden particles. Among the reliable inductive methods there is a unique procedure that minimizes convergence time as well as the number of times that the method revises its conservation principles. Thus the aims of reliable, fast and steady convergence to an empirically adequate theory single out a unique optimal inference for a given set of observed reactions–including prescriptions for when exactly to introduce hidden particles.
The Logic of Reliable and Efficient Inquiry
 Journal of Philosophical Logic
, 2001
"... This paper pursues a thoroughgoing instrumentalist, or meansends, approach to the theory of inductive inference. I consider three epistemic aims: convergence to a correct theory, fast convergence to a correct theory and steady con vergence to a correct theory (avoiding retractions). For each of ..."
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Cited by 11 (1 self)
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This paper pursues a thoroughgoing instrumentalist, or meansends, approach to the theory of inductive inference. I consider three epistemic aims: convergence to a correct theory, fast convergence to a correct theory and steady con vergence to a correct theory (avoiding retractions). For each of these, two questions arise: (1) What is the structure of inductive problems in which these aims are feasible ? (2) When feasible, what are the inference methods that attain them? Formal learning theory provides the tools for a complete set of answers to these questions.
MeansEnds Epistemology
 The Monist
, 2001
"... This paper describes the cornerstones of a meansends approach to the philosophy of inductive inference. I begin with a fallibilist ideal of convergence to the truth in the long run, or in the "limit of inquiry". I determine which methods are optimal for attaining additional epistemic a ..."
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Cited by 4 (1 self)
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This paper describes the cornerstones of a meansends approach to the philosophy of inductive inference. I begin with a fallibilist ideal of convergence to the truth in the long run, or in the "limit of inquiry". I determine which methods are optimal for attaining additional epistemic aims (notably fast and steady convergence to the truth). Meansends vindications of (a version of) Occam's Razor and the natural generalizations in a Goodmanian Riddle of Induction illustrate the power of this approach. The paper establishes a hierarchy of meansends notions of empirical success, and discusses a number of issues, results and applications of meansends epistemology.
On the Synthesis of Strategies Identifying Recursive Functions
 Proceedings of the 14th Annual Conference on Computational Learning Theory, Lecture Notes in Artificial Intelligence 2111
, 2001
"... Abstract. A classical learning problem in Inductive Inference consists of identifying each function of a given class of recursive functions from a finite number of its output values. Uniform learning is concerned with the design of single programs solving infinitely many classical learning problems. ..."
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Cited by 3 (3 self)
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Abstract. A classical learning problem in Inductive Inference consists of identifying each function of a given class of recursive functions from a finite number of its output values. Uniform learning is concerned with the design of single programs solving infinitely many classical learning problems. For that purpose the program reads a description of an identification problem and is supposed to construct a technique for solving the particular problem. As can be proved, uniform solvability of collections of solvable identification problems is rather influenced by the description of the problems than by the particular problems themselves. When prescribing a specific inference criterion (for example learning in the limit), a clever choice of descriptions allows uniform solvability of all solvable problems, whereas even the most simple classes of recursive functions are not uniformly learnable without restricting the set of possible descriptions. Furthermore the influence of the hypothesis spaces on uniform learnability is analysed. 1
On Uniform Learning of Classes of Recursive Functions
, 2000
"... A classical learning problem in inductive inference consists of identifying each function of a given class of recursive functions from a finite number of its output values. Uniform learning is concerned with the design of single programs solving infinitely many classical learning problems. For that ..."
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Cited by 1 (1 self)
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A classical learning problem in inductive inference consists of identifying each function of a given class of recursive functions from a finite number of its output values. Uniform learning is concerned with the design of single programs solving infinitely many classical learning problems. For that purpose the program reads a description of an identification problem and is supposed to construct a technique for solving the particular problem. As can be proved, uniform solvability of collections of solvable identification problems is rather influenced by the description of the problems than by the particular problems themselves. When prescribing a specific inference criterion (for example learning in the limit), a clever choice of descriptions allows uniform solvability of all solvable problems, whereas even the most simple classes of recursive functions are not learnable uniformly without restricting the set of possible descriptions. Furthermore the influence of the hypothesis spaces on uniform learnability is analysed.
The Intrinsic Complexity of Learning: A Survey
, 2007
"... The theory of learning in the limit has been a focus of study by several researchers over the last three decades. There have been several suggestions on how to measure the complexity or hardness of learning. In this paper we survey the work done in one specific such measure, called intrinsic comple ..."
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Cited by 1 (0 self)
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The theory of learning in the limit has been a focus of study by several researchers over the last three decades. There have been several suggestions on how to measure the complexity or hardness of learning. In this paper we survey the work done in one specific such measure, called intrinsic complexity of learning. We will be mostly concentrating on learning languages, with only a brief look at function learning.
MECHANISM AND PERSONAL IDENTITY
"... The soul is a number which moves itself. Xenocrate (see 44) Abstract: Some thought experiences seem to refute the possibility of subjective experience for machines. By using the recursion theorem of Kleene, I try to invalidate these refutations. A new paradox occurs. I generalize an idea used in the ..."
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The soul is a number which moves itself. Xenocrate (see 44) Abstract: Some thought experiences seem to refute the possibility of subjective experience for machines. By using the recursion theorem of Kleene, I try to invalidate these refutations. A new paradox occurs. I generalize an idea used in the foundation of Quantum Mechanics to suggest a step toward a solution.*
Inductive Inference Systems for Learning Classes of Algorithmically Generated Sets and Structures
"... Abstract. Computability theorists have extensively studied sets whose elements can be enumerated by Turing machines. These sets, also called computably enumerable sets, can be identified with their Gödel codes. Although each Turing machine has a unique Gödel code, different Turing machines can enume ..."
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Abstract. Computability theorists have extensively studied sets whose elements can be enumerated by Turing machines. These sets, also called computably enumerable sets, can be identified with their Gödel codes. Although each Turing machine has a unique Gödel code, different Turing machines can enumerate the same set. Thus, knowing a computably enumerable set means knowing one of its infinitely many Gödel codes. In the approach to learning theory stemming from E.M. Gold’s seminal paper [9], an inductive inference learner for a computably enumerable set A is a system or a device, usually algorithmic, which when successively (one by one) fed data for A outputs a sequence of Gödel codes (one by one) that at certain point stabilize at codes correct for A. The convergence is called semantic or behaviorally correct, unless the same code for A is eventually output, in which case it is also called syntactic or explanatory. There are classes of sets that are semantically inferable, but not syntactically inferable.