This is an important paper in the development of neural reasoning capabilities which should reduce the brittleness of purely symbolic approaches: Neural Logic Machine
The potential reasoning capabilities, such as with regard to multi-step inference, as in problem solving and theorem proving, are most interesting, but there are important contemporary applications in machine learning and question answering. I’ll just provide a few hightlights from the paper on the latter and some more points and references on the former below.
Continue reading “Neural Logic Machines”
Here is a graphic on how various reasoning technologies fit the practical requirements for reasoning discussed below:
This proved surprisingly controversial during correspondence with colleagues from the Vulcan work on SILK and its evolution at http://www.coherentknowledge.com.
The requirements that motivated this were the following: Continue reading “Requirements for Logical Reasoning”
The standard for defining ontologies these days is OWL and Protege. Unfortunately, OWL lacks any notion of exceptions in inheritance or any other notion of defeasibility.
So, although you may want to say that birds fly, you’re ontology will be broken (or become much more complicated) when you realize there are birds that can’t fly, such as penguins or ostriches, or even sick or injured birds.
Practically speaking, you need something like courteous logic or the defeasibility in SILK to handle this (or any 1980s expert system shell or even earlier frame system). OWL is very hard on mortal man (e.g., mainstream IT) in this regard.
How can I tell OWL that a pronoun is a noun but that pronouns are a closed class of words, unlike nouns, verbs, adjectives, and adverbs (in general). Well, I’ll have to tell it about open-class nouns versus closed class nouns. What a pain!
This is why we use Protege primarily as a drafting tool and, for example, SILK, to do reasoning. Non-defeasible description logic and first-order reasoners are difficult to get along with, in practice (and make sustainable knowledge repositories too difficult – which inhibits adoption, obviously).