I need someone to perform for me an experiment in Machine Reasoning aspect of Artificial Intelligence (i.e. Inference). Nothing esoteric, this is covered in basic computer science college classes. I have a large database of related "things". A lot of them contain data-entry error. For a human the errors are easy to spot, because you see some things that are just not possible based on other things in the data. But the data is very large, so it's not easy for a human to scan the ENTIRE database for the obvious errors. I will provide simple rules, like: RULE 1. If Thing A has some attributes A.x,A.y,A.z and Thing B has some attributes B.x,B.y,B.z, then if Thing C has attributes C.x which falls between A.x and B.x, then the either the value of C.y must fall between A.y and B.y, or the value of C.z must fall between A.z and B.z, or at least one of the 9 attributes (A.x,A.y,A.z,B.x,B.y,B.z,C.x,C.y,C.z) must be wrong. RULE 2. All attributes of some given Thing T[n] (assuming T is the class of A,B,C) are correct (e.g. because the operator has checked them to be true). (there will be other such rules, which I will provide) The Machine Reasoning program should then scan the database (a collection of Class[T]) and find errors in the data, based on these rules.
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a Machine Reasoning language like PROLOG should be good for this project, but you probably have to write some additional code in another language to parse the data.