Sentence examples for abstract inference from inspiring English sources

We introduce the concepts of abstract inference modules and abstract modular inference systems to study general principles behind the design and analysis of model generating programs, or solvers, for integrated multi-logic systems.

ToM refers to the ability to contemplate other's thoughts, desires, and behavioral dispositions by abstract inference (Premack and Woodruff 1978; Frith and Frith 2003).

There does not seem any possible way of overcoming this ignorance, since the very nature of physical reasoning allows only the most abstract inferences, and only the most abstract properties of our perceptions can be regarded as having objective validity.

They also allow, indeed, encourage, the use of well-honed spatial inferences to substitute for and support abstract inferences (e.g. Larkin & Simon, 1987; Tversky, 2011).

Nonetheless, the use of time as a source of information for abstract inferences (such as predicting future conflict likelihood) has received little emphasis (Appelbaum et al., 2012; Wendt & Kiesel, 2011), and thus the underlying processes of encoding this temporal information as a usable contextual cue remain largely unknown.

Combination of information generally goes along with the level of abstractness, time granularity and robustness, such that large CTS architectures must perform fusion gradually on different levels — starting from sensor-based recognitions to highly abstract logical inferences.

It is dubious whether this notion can be unpacked without referring to abstract objects (same inference patterns?), but in any event it cannot be used to pick out all tokens of a word, as we have been using the word 'word'word

In this paper, we focus on the concept of trust and abstract a decentralized trust inference model, where the trust an entity has for a neighbor forms the basic building block of this model.

Though we use the term "network" it is important to distinguish between this type of abstract network used for inference, and gene networks that represent physiochemical interactions in the cell, though the line between these can be fuzzy.

One view is that people understand an 'ordinary' or indicative conditional, 'if there is a triangle on the blackboard then there is a circle' (if A then B) by thinking about rules of inference, either abstract (Braine & O'Brien, 1998; Rips, 1994) or domain specific (Fiddick, Cosmides, & Tooby, 2000; Holyoak & Cheng, 1995).

These previous studies focused on more complicated learning problems in which the higher-level inferences made through hierarchical Bayesian inference concern very abstract forms of knowledge, although there have also been studies that used Bayesian methods to explain causal inference in perception [29].