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  • 1
    Electronic Resource
    Electronic Resource
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 8 (1992), S. 0 
    ISSN: 1467-8640
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 4 (1988), S. 0 
    ISSN: 1467-8640
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Type of Medium: Electronic Resource
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  • 3
    Electronic Resource
    Electronic Resource
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 6 (1990), S. 0 
    ISSN: 1467-8640
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Halperri argues for alternative non-Bayesian approaches to uncertainty based on problems he perceives in the Bayesian approach. In particular, he argues for a distinction between degrees of belief and statistical statements (based on the concept of random sampling). In this response I show that there is no difference between these two concepts in the Bayesian framework, and that the replacement of variables by constants in probabilistic predicate calculus expressions is valid, despite Halpern's objections. The main reason for his rejection of the simpler approach is that he does not condition his belief statements on the evidence used to form these beliefs, and so gets into trouble when new evidence is received. This failure to properly take evidence into account invalidates most of his other criticisms. While I approve of his call for more formal rigor in representing Bayesian practice, his claim to have provided a semantics is misleading – what he has provided is not operational.
    Type of Medium: Electronic Resource
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  • 4
    Electronic Resource
    Electronic Resource
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 4 (1988), S. 0 
    ISSN: 1467-8640
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: This essay addresses a number of issues centered around the question of what is the best method for representing and reasoning about common sense (sometimes called plausible inference). Drew McDermott has shown that a direct translation of commonsense reasoning into logical form leads to insurmountable difficulties, from which McDermott concluded that we must resort to procedural ad hocery. This paper shows that the difficulties McDermott described are a result of insisting on using logic as the language of commonsense reasoning. If, instead, (Bayesian) probability is used, none of the technical difficulties found in using logic arise. For example, in probability, the problem of referential opacity cannot occur and nonmonotonic logics (which McDermott showed don't work anyway) are not necessary. The difficulties in applying logic to the real world are shown to arise from the limitations of truth semantics built into logic–probability substitutes the more reasonable notion of belief. In Bayesian inference, many pieces of evidence are combined to get an overall measure of belief in a proposition. This is much closer to commonsense patterns of thought than long chains of logical inference to the true conclusions. Also it is shown that English expressions of the “IF A THEN B” form are best interpreted as conditional probabilities rather than universally quantified expressions. Bayesian inference is applied to a simple example of linguistic information to illustrate the potential of this type of inference for AI. This example also shows how to deal with vague information, which has so far been the province of fuzzy logic. It is further shown that Bayesian inference gives a theoretical basis for inductive inference that is borne out in practice. Instead of insisting that probability is the best language for commonsense reasoning, a major point of this essay is to show that real inference is a complex interaction between probability, logic, and other formal representation and reasoning systems.
    Type of Medium: Electronic Resource
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  • 5
    Electronic Resource
    Electronic Resource
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 10 (1994), S. 0 
    ISSN: 1467-8640
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Type of Medium: Electronic Resource
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