Download Machine Learning, Meta-Reasoning and Logics by D. Paul Benjamin (auth.), Pavel B. Brazdil, Kurt Konolige PDF

By D. Paul Benjamin (auth.), Pavel B. Brazdil, Kurt Konolige (eds.)

This ebook incorporates a choice of papers provided on the foreign Workshop computing device studying, Meta-Reasoning and Logics held in inn de Mar in Sesimbra, Portugal, 15-17 February 1988. the entire papers have been edited afterwards. The Workshop encompassed numerous fields of man-made Intelligence: computing device studying, trust Revision, Meta-Reasoning and Logics. the target of this Workshop used to be not just to handle the typical matters in those parts, but in addition to check the way to problematic cognitive architectures for structures in a position to studying from event, revising their ideals and reasoning approximately what they comprehend. Acknowledgements The modifying of this publication has been supported by means of COST-13 venture computer studying and information Acquisition funded through the fee o/the ecu groups which has lined a considerable a part of the prices. different sponsors who've supported this paintings have been Junta Nacional de lnvestiga~ao Cientlfica (JNICT), lnstituto Nacional de lnvestiga~ao Cientlfica (INIC), Funda~ao Calouste Gulbenkian. I desire to exhibit my gratitude to these kind of associations. eventually my specific due to Paula Pereira and AnaN ogueira for his or her assist in getting ready this quantity. This paintings integrated retyping all of the texts and getting ready the camera-ready reproduction. creation 1 1. Meta-Reasoning and computer studying the 1st bankruptcy is anxious with the position meta-reasoning performs in clever platforms in a position to studying. As we will see from the papers that seem during this bankruptcy, there are essentially diverse colleges of thought.

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Extra resources for Machine Learning, Meta-Reasoning and Logics

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1 Deriving the Instance Language Bias In this section we show how the instance language bias for an operational system can be derived from background knowledge. We first describe the considerations involved in choosing useful bias, and discuss the implementation of bias derivations a goal-directed reasoning process. The following section outlines the theory of tree-structured biases. Derivation of an instance language bias from a set of determinations can of course be carried out using a standard resolution theorem prover, since the determinations are first-order axioms.

SuitablePet(Felix). A concept description e. matches an instance a. ). , of the instances; the derivation can use facts from the articulation theory Th a (which thus links instance-level terms to concept-level terms). In order to have complete matching, which is necessary for the VS process to work [Mitchell 1978], Tha must entail either D. ~ e. or D. ~ Ie. for any instance description D. and any concept description , J ' J ' e.. When these relationships hold without relying on facts in the articulation theory, we have J what is commonly known as the single-representation trick, since then the instances and concepts have to be described using the same predicates.

We can in fact capture this by making the second argument of the binary schema a truth-valued variable. that can take on positive or negative polarity and in that way modify the meaning of a fonnula. Following Davies and Russell [1987] we write the truth-valued variables as a prefIX on the fonnula. We w illtypic ally Russell & Grosof 30 use the letters ijk ... for such variables. " The addition of truth-valued variables to the language significantly reduces the length of fonnulre in general, and allows for a unifonn treatment.

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