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Theory formation by abduction : a case study based on the chemical revolution
Abduction is the process of constructing explanations. This chapter suggests that automated abduction is a key to advancing beyond the "routine theory revision" methods developed in early AI research towards automated reasoning systems capable of "world model revision" - dramatic changes in systems of beliefs such as occur in children's cognitive development and in scientific revolutions. The chapter describes a general approach to automating theory revision based upon computational methods for theory formation by abduction. The approach is based on the idea that, when an anomaly is encountered, the best course is often simply to suppress parts of the original theory thrown into question by the contradiction and to derive an explanation of the anomalous observation based on relatively solid, basic principles. This process of looking for explanations of unexpected new phenomena can lead by abductive inference to new hypotheses that can form crucial parts of a revised theory. As an illustration, the chapter shows how some of Lavoisier's key insights during the Chemical Revolution can be viewed as examples of theory formation by abduction
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Theory formation by abduction : initial results of a case study based on the chemical revolution
Abduction is the process of constructing explanations. This chapter suggests that automated abduction is a key to advancing beyond the "routine theory revision" methods developed in early AI research towards automated reasoning systems capable of "world model revision" β dramatic changes in systems of beliefs such as occur in children's cognitive development and in scientific revolutions. The chapter describes a general approach to automating theory revision based upon computational methods for theory formation by abduction. The approach is based on the idea that, when an anomaly is encountered, the best course is often simply to suppress parts of the original theory thrown into question by the contradiction and to derive an explanation of the anomalous observation based on relatively solid, basic principles. This process of looking for explanations of unexpected new phenomena can lead by abductive inference to new hypotheses that can form crucial parts of a revised theory. As an illustration, the chapter shows how some of Lavoisier's key insights during the Chemical Revolution can be viewed as examples of theory formation by abduction
Tractability of Theory Patching
In this paper we consider the problem of `theory patching', in which we are
given a domain theory, some of whose components are indicated to be possibly
flawed, and a set of labeled training examples for the domain concept. The
theory patching problem is to revise only the indicated components of the
theory, such that the resulting theory correctly classifies all the training
examples. Theory patching is thus a type of theory revision in which revisions
are made to individual components of the theory. Our concern in this paper is
to determine for which classes of logical domain theories the theory patching
problem is tractable. We consider both propositional and first-order domain
theories, and show that the theory patching problem is equivalent to that of
determining what information contained in a theory is `stable' regardless of
what revisions might be performed to the theory. We show that determining
stability is tractable if the input theory satisfies two conditions: that
revisions to each theory component have monotonic effects on the classification
of examples, and that theory components act independently in the classification
of examples in the theory. We also show how the concepts introduced can be used
to determine the soundness and completeness of particular theory patching
algorithms.Comment: See http://www.jair.org/ for any accompanying file
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