473 research outputs found
Connectionist Inference Models
The performance of symbolic inference tasks has long been a challenge to connectionists. In this paper, we present an extended survey of this area. Existing connectionist inference systems are reviewed, with particular reference to how they perform variable binding and rule-based reasoning, and whether they involve distributed or localist representations. The benefits and disadvantages of different representations and systems are outlined, and conclusions drawn regarding the capabilities of connectionist inference systems when compared with symbolic inference systems or when used for cognitive modeling
Learning to Find Proofs and Theorems by Learning to Refine Search Strategies: The Case of Loop Invariant Synthesis
We propose a new approach to automated theorem proving where an
AlphaZero-style agent is self-training to refine a generic high-level expert
strategy expressed as a nondeterministic program. An analogous teacher agent is
self-training to generate tasks of suitable relevance and difficulty for the
learner. This allows leveraging minimal amounts of domain knowledge to tackle
problems for which training data is unavailable or hard to synthesize. As a
specific illustration, we consider loop invariant synthesis for imperative
programs and use neural networks to refine both the teacher and solver
strategies
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