Cross domain inference and problem embedding
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
In Robert E. Cummins & John L. Pollock (eds.), Philosophy and AI: Essays at the Interface. MIT Press (1992)
I.1. Two reasons for studying inference. Inference is studied for two distinct reasons: for its bearing on justification and for its bearing on learning. By and large, philosophy has focused on the role of inference in justification, leaving its role in learning to psychology and artificial intelligence. This difference of role leads to a difference of conception. An inference based theory of learning does not require a conception of inference according to which a good inference is one that justifies its conclusion, whereas, obviously, an inference based theory of justification does require such a conception.1 Because of its focus on normative issues of justification, philosophy has taken a retrospective approach to inference, whereas a focus on learning naturally leads to a prospective approach. A focus on learning leads us to ask, "Given what is known, what should be inferred? How can what is known lead, via inference, to new knowledge?" A focus on justification has led philosophers to concentrate instead on a retrospective question: "Given a belief, can it be validly inferred from what is known? How can what is known justify, via inference, a new belief?" Thus, for philosophy, inference can be regarded as permissive: one needn't worry about what to infer, only about whether what has been arrived at somehow or other is or can be inferentially justified. A theory of learning, on the other hand, requires a conception of inference that is directive, for the problem of inference based learning is precisely the problem of what to infer.
|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
No citations found.
Similar books and articles
Stephen Hetherington (2001). Why There Need Not Be Any Grue Problem About Inductive Inference as Such. Philosophy 76 (1):127-136.
Peter Pagin (2012). Assertion, Inference, and Consequence. Synthese 187 (3):869 - 885.
Ralph Wedgwood (2012). Justified Inference. Synthese 189 (2):1-23.
Keith Frankish (2005). Non-Monotonic Inference. In Alex Barber (ed.), Encyclopedia of Language and Linguistics. Elsevier.
William G. Lycan (2002). Explanation and Epistemology. In Paul K. Moser (ed.), The Oxford Handbook of Epistemology. Oxford University Press. 413.
P. D. Magnus (2008). Demonstrative Induction and the Skeleton of Inference. International Studies in the Philosophy of Science 22 (3):303 – 315.
John D. Norton (2003). A Material Theory of Induction. Philosophy of Science 70 (4):647-670.
Alan R. Rhoda (2008). Fumerton's Principle of Inferential Justification, Skepticism, and the Nature of Inference. Journal of Philosophical Research 33:215-234.
Added to index2009-01-28
Total downloads13 ( #100,596 of 1,089,064 )
Recent downloads (6 months)1 ( #69,801 of 1,089,064 )
How can I increase my downloads?