Sentence examples for reasoning from knowledge from inspiring English sources

The presented prediction algorithm performed well across all tests, indicating that automated reasoning from knowledge graphs representing publicly available knowledge can be used to accurately predict unknown ADRs that have been observed in clinical practice.

This paper introduces a new approach and framework for an autonomous design-to-fabrication system that integrates cognitive capabilities, such as reasoning from knowledge models and autonomous planning, and embeds these in the machines themselves to automatically fabricate customized parts.

In this paper, we fill these gaps and first draw a precise picture of the complexity of default reasoning from conditional knowledge bases: Given a conditional knowledge base KB and a default φ→ψ, does KB entail φ→ψ?

In other words, animals and most human beings most of the time are purely empiricists; a rational person, however, is one who can engage in genuine a priori reasoning, moving from knowledge of a true cause via deduction to necessary effects.

The resulting probabilistic generalizations of default reasoning from conditional knowledge bases allow for handling in a uniform framework strict logical knowledge, default logical knowledge, as well as purely probabilistic knowledge.

As an important additional feature, they also allow for expressing terminological default knowledge, which is semantically interpreted as in Lehmann's lexicographic entailment in default reasoning from conditional knowledge bases.

Our results complement and extend previous results, and contribute in refining the tractability/intractability frontier of default reasoning from conditional knowledge bases.

We then analyze the nonmonotonic behavior of both approaches along benchmark examples and along general properties for default reasoning from conditional knowledge bases.

We present an approach where probabilistic logic is combined with default reasoning from conditional knowledge bases in Kraus et al.'s System P, Pearl's System Z, and Lehmann's lexicographic entailment.

How can knowledge be closed if in one step of valid deductive reasoning I can go from knowledge that my car is parked on Main Street to a belief that it has not been stolen since I parked it, which it seems I do not know?

The device description structures proposed for content adaptation for the mobile devices just take into account a range of factors such as screen size and image format and do not support the knowledge reasoning from those device descriptions.