262 research outputs found
A Deep Reinforcement Learning Approach to First-Order Logic Theorem Proving
Automated theorem provers have traditionally relied on manually tuned
heuristics to guide how they perform proof search. Deep reinforcement learning
has been proposed as a way to obviate the need for such heuristics, however,
its deployment in automated theorem proving remains a challenge. In this paper
we introduce TRAIL, a system that applies deep reinforcement learning to
saturation-based theorem proving. TRAIL leverages (a) a novel neural
representation of the state of a theorem prover and (b) a novel
characterization of the inference selection process in terms of an
attention-based action policy. We show through systematic analysis that these
mechanisms allow TRAIL to significantly outperform previous
reinforcement-learning-based theorem provers on two benchmark datasets for
first-order logic automated theorem proving (proving around 15% more theorems)
Proceedings of the Automated Reasoning Workshop (ARW 2019)
Preface
This volume contains the proceedings of ARW 2019, the twenty sixths Workshop on Automated Rea-
soning (2nd{3d September 2019) hosted by the Department of Computer Science, Middlesex University,
England (UK). Traditionally, this annual workshop which brings together, for a two-day intensive pro-
gramme, researchers from different areas of automated reasoning, covers both traditional and emerging
topics, disseminates achieved results or work in progress. During informal discussions at workshop ses-
sions, the attendees, whether they are established in the Automated Reasoning community or are only at
their early stages of their research career, gain invaluable feedback from colleagues. ARW always looks
at the ways of strengthening links between academia, industry and government; between theoretical and
practical advances. The 26th ARW is affiliated with TABLEAUX 2019 conference.
These proceedings contain forteen extended abstracts contributed by the participants of the workshop
and assembled in order of their presentations at the workshop. The abstracts cover a wide range of topics
including the development of reasoning techniques for Agents, Model-Checking, Proof Search for classical
and non-classical logics, Description Logics, development of Intelligent Prediction Models, application of
Machine Learning to theorem proving, applications of AR in Cloud Computing and Networking.
I would like to thank the members of the ARW Organising Committee for their advice and assis-
tance. I would also like to thank the organisers of TABLEAUX/FroCoS 2019, and Andrei Popescu, the
TABLEAUX Conference Chair, in particular, for the enormous work related to the organisation of this
affiliation. I would also like to thank Natalia Yerashenia for helping in preparing these proceedings.
London Alexander Bolotov
September 201
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