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Modal Logics for Qualitative Spatial Reasoning (1996)
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BibTeX
@MISC{Bennett96modallogics,
author = {Brandon Bennett and G. F. Shwartz and Minimal Artificial},
title = {Modal Logics for Qualitative Spatial Reasoning},
year = {1996}
}
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Abstract
Spatial reasoning is essential for many AI applications. In most existing systems the representation is primarily numerical, so the information that can be handled is limited to precise quantitative data. However, for many purposes the ability to manipulate high-level qualitative spatial information in a flexible way would be extremely useful. Such capabilities can be proveded by logical calculi; and indeed 1st-order theories of certain spatial relations have been given [20]. But computing inferences in 1st-order logic is generally intractable unless special (domain dependent) methods are known. 0-order modal logics provide an alternative representation which is more expressive than classical 0-order logic and yet often more amenable to automated deduction than 1st-order formalisms. These calculi are usually interpreted as propositional logics: non-logical constants are taken as denoting propositions. However, they can also be given a nominal interpretation in which the constants stand...
Keyphrases
qualitative spatial reasoning modal logic high-level qualitative spatial information denoting proposition nominal interpretation certain spatial relation 1st-order formalism many purpose 0-order modal logic quantitative data logical calculus spatial reasoning propositional logic flexible way 1st-order theory alternative representation many ai application non-logical constant 1st-order logic classical 0-order logic
