scholarly journals Light-Weight Integration of SAT Solving into First-Order Reasoners – First Experiments

10.29007/89kc ◽  
2018 ◽  
Author(s):  
Stephan Schulz
Keyword(s):  
Search Space ◽  
Light Weight ◽  
Sat Solving ◽  
First Order ◽  
Proof Search ◽  
Sat Solver

We describe a light-weight integration of the propositional SAT solver PicoSAT and the saturation-based superposition prover E. The proof search is driven by the saturation prover. Periodically, the saturation is interrupted, and all first-order clauses are grounded. The resulting ground problem is converted to a propositional format and handed to the SAT solver. If the SAT solver reports unsatisfiability, the proof is extracted and reported on the first-order level. First experiments demonstrate the viability of the approach and suggest future extensions. They also yield interesting information about the structure of the search space.

scholarly journals SAT solving experiments in Vampire

10.29007/5l47 ◽  
2018 ◽  
Author(s):  
Armin Biere ◽  
Ioan Dragan ◽  
Laura Kovács ◽  
Andrei Voronkov
Keyword(s):  
State Of The Art ◽  
Theorem Prover ◽  
Sat Solving ◽  
First Order ◽  
Automated Theorem Prover ◽  
Sat Solver ◽  
Overall Performance

In order to better understand how well a state of the art SAT solver would behave in the framework of a first-order automated theorem prover we have decided to integrate Lingeling, best performing SAT solver, inside Vampire’s AVATAR framework. In this paper we propose two ways of integrating a SAT solver inside of Vampire and evaluate overall performance of this combination. Our experiments show that by using a state of the art SAT solver in Vampire we manage to solve more problems. Surprisingly though, there are cases where combination of the two solvers does not always prove to generate best results.

scholarly journals AVATAR Modulo Theories

10.29007/k6tp ◽  
2018 ◽  
Author(s):  
Giles Reger ◽  
Nikolaj Bjorner ◽  
Martin Suda ◽  
Andrei Voronkov
Keyword(s):  
Theorem Proving ◽  
Satisfiability Modulo Theories ◽  
Theorem Prover ◽  
Ground Theory ◽  
Smt Solver ◽  
First Order ◽  
Proof Search ◽  
Smt Solvers ◽  
Sat Solver ◽  
A New Technique

This paper introduces a new technique for reasoning with quantifiers and theories. Traditionally, first-order theorem provers (ATPs) are well suited to reasoning with first-order problems containing many quantifiers and satisfiability modulo theories (SMT) solvers are well suited to reasoning with first-order problems in ground theories such as arithmetic. A recent development in first-order theorem proving has been the AVATAR architecture which uses a SAT solver to guide proof search based on a propositional abstraction of the first-order clause space. The approach turns a single proof search into a sequence of proof searches on (much) smaller sub-problems. This work extends the AVATAR approach to use a SMT solver in place of the SAT solver, with the effect that the first-order solver only needs to consider ground-theory-consistent sub-problems. The new architecture has been implemented using the Vampire theorem prover and Z3 SMT solver. Our experimental results, and the results of recent competitions, show that such a combination can be highly effective.

scholarly journals Modern Cooperative Parallel SAT Solving

10.29007/jnvf ◽  
2018 ◽  
Author(s):  
Norbert Manthey ◽  
Davide Lanti ◽  
Ahmed Irfan
Keyword(s):  
Search Space ◽  
Sat Solving ◽  
Space Partitioning ◽  
Iterative Search ◽  
Sat Solvers ◽  
Algorithm Portfolio ◽  
Alternative Approach ◽  
Sat Solver ◽  
Portfolio Approach ◽  
Iterative Partitioning

Nowadays, powerful parallel SAT solvers are based on an algorithm portfolio. Thealternative approach, (iterative) search space partitioning, cannot keep up, although, ac-cording to the literature, iterative partitioning systems should scale better than portfoliosolvers. In this paper we identify key problems in current parallel cooperative SAT solvingapproaches, most importantly communication, how to partition the search space, and howto utilize the sequential search engine. First, we improve on each problem separately. Ina further step, we show that combining all the improvements leads to a state-of-the-artparallel SAT solver, which does not use the portfolio approach, but instead relies on it-erative partitioning. The experimental evaluation of this system completely changes thepicture about the performance of search space partitioning SAT solvers: on instances ofa combined benchmark of recent SAT competitions, the presented approach can keep upwith the winners of last years SAT competition. The combined improvements improve theexisting cooperative solver splitter by 24%: instead of 561 out of 880 instances, the newsolver Pcasso can solve 696 instances.

scholarly journals CSE_E 1.0: An Integrated Automated Theorem Prover for First-Order Logic

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1142
Author(s):  
Feng Cao ◽  
Yang Xu ◽  
Jun Liu ◽  
Shuwei Chen ◽  
Xinran Ning
Keyword(s):  
Order Logic ◽  
Theorem Prover ◽  
First Order Logic ◽  
Inference Mechanism ◽  
First Order ◽  
Proof Search ◽  
Interdisciplinary Field ◽  
Heuristic Strategies ◽  
Automated Theorem Prover ◽  
Hard Problems

First-order logic is an important part of mathematical logic, and automated theorem proving is an interdisciplinary field of mathematics and computer science. The paper presents an automated theorem prover for first-order logic, called C S E _ E 1.0, which is a combination of two provers contradiction separation extension (CSE) and E, where CSE is based on the recently-introduced multi-clause standard contradiction separation (S-CS) calculus for first-order logic and E is the well-known equational theorem prover for first-order logic based on superposition and rewriting. The motivation of the combined prover C S E _ E 1.0 is to (1) evaluate the capability, applicability and generality of C S E _ E , and (2) take advantage of novel multi-clause S-CS dynamic deduction of CSE and mature equality handling of E to solve more and harder problems. In contrast to other improvements of E, C S E _ E 1.0 optimizes E mainly from the inference mechanism aspect. The focus of the present work is given to the description of C S E _ E including its S-CS rule, heuristic strategies, and the S-CS dynamic deduction algorithm for implementation. In terms of combination, in order not to lose the capability of E and use C S E _ E to solve some hard problems which are unsolved by E, C S E _ E 1.0 schedules the running of the two provers in time. It runs plain E first, and if E does not find a proof, it runs plain CSE, then if it does not find a proof, some clauses inferred in the CSE run as lemmas are added to the original clause set and the combined clause set handed back to E for further proof search. C S E _ E 1.0 is evaluated through benchmarks, e.g., CASC-26 (2017) and CASC-J9 (2018) competition problems (FOFdivision). Experimental results show that C S E _ E 1.0 indeed enhances the performance of E to a certain extent.

scholarly journals Probabilistic Inference for Predicate Constraint Satisfaction

2020 ◽  
Vol 34 (02) ◽  
pp. 1644-1651
Author(s):  
Yuki Satake ◽  
Hiroshi Unno ◽  
Hinata Yanagi
Keyword(s):  
Graphical Models ◽  
Constraint Satisfaction ◽  
Linear Time ◽  
Predicate Logic ◽  
Probabilistic Inference ◽  
Search Space ◽  
Constraint Satisfaction Problems ◽  
Safety Properties ◽  
First Order ◽  
Guided Inductive

In this paper, we present a novel constraint solving method for a class of predicate Constraint Satisfaction Problems (pCSP) where each constraint is represented by an arbitrary clause of first-order predicate logic over predicate variables. The class of pCSP properly subsumes the well-studied class of Constrained Horn Clauses (CHCs) where each constraint is restricted to a Horn clause. The class of CHCs has been widely applied to verification of linear-time safety properties of programs in different paradigms. In this paper, we show that pCSP further widens the applicability to verification of branching-time safety properties of programs that exhibit finitely-branching non-determinism. Solving pCSP (and CHCs) however is challenging because the search space of solutions is often very large (or unbounded), high-dimensional, and non-smooth. To address these challenges, our method naturally combines techniques studied separately in different literatures: counterexample guided inductive synthesis (CEGIS) and probabilistic inference in graphical models. We have implemented the presented method and obtained promising results on existing benchmarks as well as new ones that are beyond the scope of existing CHC solvers.

scholarly journals Deep Network Guided Proof Search

10.29007/8mwc ◽  
2018 ◽  
Author(s):  
Sarah Loos ◽  
Geoffrey Irving ◽  
Christian Szegedy ◽  
Cezary Kaliszyk
Keyword(s):  
Deep Learning ◽  
Program Analysis ◽  
Network Models ◽  
Theorem Prover ◽  
Neural Network Models ◽  
Two Phase ◽  
First Order ◽  
Proof Search ◽  
System Verification ◽  
Theory Reasoning

Deep learning techniques lie at the heart of several significant AI advances in recent years including object recognition and detection, image captioning, machine translation, speech recognition and synthesis, and playing the game of Go.Automated first-order theorem provers can aid in the formalization and verification of mathematical theorems and play a crucial role in program analysis, theory reasoning, security, interpolation, and system verification.Here we suggest deep learning based guidance in the proof search of the theorem prover E. We train and compare several deep neural network models on the traces of existing ATP proofs of Mizar statements and use them to select processed clauses during proof search. We give experimental evidence that with a hybrid, two-phase approach, deep learning based guidance can significantly reduce the average number of proof search steps while increasing the number of theorems proved.Using a few proof guidance strategies that leverage deep neural networks, we have found first-order proofs of 7.36% of the first-order logic translations of the Mizar Mathematical Library theorems that did not previously have ATP generated proofs. This increases the ratio of statements in the corpus with ATP generated proofs from 56% to 59%.

scholarly journals Dolius: A Distributed Parallel SAT Solving Framework

10.29007/hvqt ◽  
2018 ◽  
Author(s):  
Gilles Audemard ◽  
Benoît Hoessen ◽  
Saïd Jabbour ◽  
Cédric Piette
Keyword(s):  
Shared Memory ◽  
State Of The Art ◽  
Divide And Conquer ◽  
Sat Solving ◽  
Sat Solvers ◽  
Sat Solver

Over the years, parallel SAT solving becomes more and more important. However, most of state-of-the-art parallel SAT solvers are portfolio-based ones. They aim at running several times the same solver with different parameters. In this paper, we propose a tool called Dolius, mainly based on the divide and conquer paradigm. In contrast to most current parallel efficient engines, Dolius does not need shared memory, can be distributed, and scales well when a large number of computing units is available. Furthermore, our tool contains an API allowing to plug any SAT solver in a simple way.

Motion Planning for Dynamic Systems Using First-Order Logic

2002 ◽  
Author(s):  
Lei Yan ◽  
K. Krishnamurthy
Keyword(s):  
Motion Planning ◽  
Dynamic Systems ◽  
Initial Conditions ◽  
Search Space ◽  
Order Logic ◽  
First Order Logic ◽  
Knowledge Representation And Reasoning ◽  
Know How ◽  
First Order ◽  
Knowledge Based

The problem of motion planning for a class of dynamic systems is considered in this study. A knowledge-based approach is used to determine the initial conditions that will yield a certain desired state of the dynamic system. The search space is limited by using a set of rules because reasoning about dynamic systems is basically searching an infinite space. In this study, first-order logic is used for knowledge representation and reasoning. The methodology is applied to playing a pool game. The dynamics of the motion of the balls are complicated and significant expertise is required to know how to strike the balls. Simulated results presented show how the rules help in finding the appropriate strategies for playing the game.

scholarly journals Breeding Theorem Proving Heuristics with Genetic Algorithms

10.29007/gms9 ◽  
2018 ◽  
Author(s):  
Simon Schäfer ◽  
Stephan Schulz
Keyword(s):  
Genetic Algorithms ◽  
Large Scale ◽  
First Order ◽  
Proof Search ◽  
Theorem Provers ◽  
Search Heuristics ◽  
Practical Performance ◽  
Set Up ◽  
The Given ◽  
Selection Of

First-order theorem provers have to search for proofs in an infinitespace of possible derivations. Proof search heuristics play a vitalrole for the practical performance of these systems. In the currentgeneration of saturation-based theorem provers like SPASS, E,Vampire or Prover~9, one of the most important decisions is theselection of the next clause to process with the given clausealgorithms. Provers offer a wide variety of basic clause evaluationfunctions, which can often be parameterized and combined in manydifferent ways. Finding good strategies is usually left to the usersor developers, often backed by large-scale experimentalevaluations. We describe a way to automatize this process usinggenetic algorithms, evaluating a population of different strategieson a test set, and applying mutation and crossover operators to goodstrategies to create the next generation. We describe the design andexperimental set-up, and report on first promising results.

scholarly journals Efficient Breadth-First Reduct Search

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 833
Author(s):  
Veera Boonjing ◽  
Pisit Chanvarasuth
Keyword(s):  
Information System ◽  
Search Strategy ◽  
Search Space ◽  
Graph Search ◽  
Search Problem ◽  
Breadth First Search ◽  
Time And Space ◽  
First Order ◽  
Pruning Rule

This paper formulates the problem of determining all reducts of an information system as a graph search problem. The search space is represented in the form of a rooted graph. The proposed algorithm uses a breadth-first search strategy to search for all reducts starting from the graph root. It expands nodes in breadth-first order and uses a pruning rule to decrease the search space. It is mathematically shown that the proposed algorithm is both time and space efficient.

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