David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Philosophy of Science 57 (1):78-95 (1990)
A computational theory of induction must be able to identify the projectible predicates, that is to distinguish between which predicates can be used in inductive inferences and which cannot. The problems of projectibility are introduced by reviewing some of the stumbling blocks for the theory of induction that was developed by the logical empiricists. My diagnosis of these problems is that the traditional theory of induction, which started from a given (observational) language in relation to which all inductive rules are formulated, does not go deep enough in representing the kind of information used in inductive inferences. As an interlude, I argue that the problem of induction, like so many other problems within AI, is a problem of knowledge representation. To the extent that AI-systems are based on linguistic representations of knowledge, these systems will face basically the same problems as did the logical empiricists over induction. In a more constructive mode, I then outline a non-linguistic knowledge representation based on conceptual spaces. The fundamental units of these spaces are "quality dimensions". In relation to such a representation it is possible to define "natural" properties which can be used for inductive projections. I argue that this approach evades most of the traditional problems
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
Hannes Leitgeb (2011). New Life for Carnap's "Aufbau?". Synthese 180 (2):265 - 299.
Allen Newell (1992). SOAR as a Unified Theory of Cognition: Issues and Explanations. Behavioral and Brain Sciences 15 (3):464-492.
Robert Kowalenko (2012). Reply to Israel on the New Riddle of Induction. Philosophia 40 (3):549-552.
Wolfgang Balzer & Klaus Manhart (2014). Scientific Processes and Social Processes. Erkenntnis 79 (S8):1393-1412.
Nicholaos Jones (2009). General Relativity and the Standard Model: Why Evidence for One Does Not Disconfirm the Other. Studies in History and Philosophy of Modern Physics 40 (2):124-132.
Similar books and articles
John D. Norton (2013). A Material Dissolution of the Problem of Induction. Synthese 191 (4):1-20.
John D. Norton (2003). A Material Theory of Induction. Philosophy of Science 70 (4):647-670.
Peter Gardenfors (2004). Conceptual Spaces as a Framework for Knowledge Representation. Mind and Matter 2 (2):9-27.
Massimiliano Badino (2004). An Application of Information Theory to the Problem of the Scientific Experiment. Synthese 140 (3):355 - 389.
Audun Öfsti (1962). Some Problems of Counter‐Inductive Policy as Opposed to Inductive. Inquiry 5 (1-4):267-283.
F. Bergadano (1993). Machine Learning and the Foundations of Inductive Inference. Minds and Machines 3 (1):31-51.
John D. Norton, The Inductive Significance of Observationally Indistinguishable Spacetimes: (Peter Achinstein has the Last Laugh).
D. C. Stove (1986). The Rationality of Induction. Oxford University Press.
Alex McLean (2010). Unifying Conceptual Spaces: Concept Formation in Musical Creative Systems. [REVIEW] Minds and Machines 20 (4):503-532.
John D. Norton (2010). There Are No Universal Rules for Induction. Philosophy of Science 77 (5):765-777.
Added to index2009-01-28
Total downloads34 ( #94,431 of 1,725,833 )
Recent downloads (6 months)2 ( #268,271 of 1,725,833 )
How can I increase my downloads?