93 research outputs found
Neurons and Symbols: A Manifesto
We discuss the purpose of neural-symbolic integration including its
principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model
in the broader context of multi-agent systems, machine learning and
automated reasoning, and list some of the challenges for the area of
neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty
A Semantic Framework for Neural-Symbolic Computing
Two approaches to AI, neural networks and symbolic systems, have been proven
very successful for an array of AI problems. However, neither has been able to
achieve the general reasoning ability required for human-like intelligence. It
has been argued that this is due to inherent weaknesses in each approach.
Luckily, these weaknesses appear to be complementary, with symbolic systems
being adept at the kinds of things neural networks have trouble with and
vice-versa. The field of neural-symbolic AI attempts to exploit this asymmetry
by combining neural networks and symbolic AI into integrated systems. Often
this has been done by encoding symbolic knowledge into neural networks.
Unfortunately, although many different methods for this have been proposed,
there is no common definition of an encoding to compare them. We seek to
rectify this problem by introducing a semantic framework for neural-symbolic
AI, which is then shown to be general enough to account for a large family of
neural-symbolic systems. We provide a number of examples and proofs of the
application of the framework to the neural encoding of various forms of
knowledge representation and neural network. These, at first sight disparate
approaches, are all shown to fall within the framework's formal definition of
what we call semantic encoding for neural-symbolic AI
Relational Knowledge Extraction from Attribute-Value Learners
Bottom Clause Propositionalization (BCP) is a recent propositionalization method which allows fast relational learning. Propositional learners can use BCP to obtain accuracy results comparable with Inductive Logic Programming (ILP) learners. However, differently from ILP learners, what has been learned cannot normally be represented in first-order logic. In this paper, we propose an approach and introduce a novel algorithm for extraction of first-order rules from propositional rule learners, when dealing with data propositionalized with BCP. A theorem then shows that the extracted first-order rules are consistent with their propositional version. The algorithm was evaluated using the rule learner RIPPER, although it can be applied on any propositional rule learner. Initial results show that the accuracies of both RIPPER and the extracted first-order rules can be comparable to those obtained by Aleph (a traditional ILP system), but our approach is considerably faster (obtaining speed-ups of over an order of magnitude), generating a compact rule set with at least the same representation power as standard ILP learners
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