Neuro-symbolic Architectures for Context Understanding
- URL: http://arxiv.org/abs/2003.04707v1
- Date: Mon, 9 Mar 2020 15:04:07 GMT
- Title: Neuro-symbolic Architectures for Context Understanding
- Authors: Alessandro Oltramari, Jonathan Francis, Cory Henson, Kaixin Ma, and
Ruwan Wickramarachchi
- Abstract summary: We propose the use of hybrid AI methodology as a framework for combining the strengths of data-driven and knowledge-driven approaches.
Specifically, we inherit the concept of neuro-symbolism as a way of using knowledge-bases to guide the learning progress of deep neural networks.
- Score: 59.899606495602406
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Computational context understanding refers to an agent's ability to fuse
disparate sources of information for decision-making and is, therefore,
generally regarded as a prerequisite for sophisticated machine reasoning
capabilities, such as in artificial intelligence (AI). Data-driven and
knowledge-driven methods are two classical techniques in the pursuit of such
machine sense-making capability. However, while data-driven methods seek to
model the statistical regularities of events by making observations in the
real-world, they remain difficult to interpret and they lack mechanisms for
naturally incorporating external knowledge. Conversely, knowledge-driven
methods, combine structured knowledge bases, perform symbolic reasoning based
on axiomatic principles, and are more interpretable in their inferential
processing; however, they often lack the ability to estimate the statistical
salience of an inference. To combat these issues, we propose the use of hybrid
AI methodology as a general framework for combining the strengths of both
approaches. Specifically, we inherit the concept of neuro-symbolism as a way of
using knowledge-bases to guide the learning progress of deep neural networks.
We further ground our discussion in two applications of neuro-symbolism and, in
both cases, show that our systems maintain interpretability while achieving
comparable performance, relative to the state-of-the-art.
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