BigraphER: rewriting and analysis engine for bigraphs

BigraphER is a suite of open-source tools providing an effi- cient implementation of rewriting, simulation, and visualisation for bigraphs, a universal formalism for modelling interacting systems that evolve in time and space and first introduced by Milner. BigraphER consists of an OCaml library that provides programming interfaces for the manipulation of bigraphs, their constituents and reaction rules, and a command-line tool capable of simulating Bigraphical Reactive Systems (BRSs) and computing their transition systems. Other features are native support for both bigraphs and bigraphs with sharing, stochastic reaction rules, rule priorities, instantiation maps, parameterised controls, predicate checking, graphical output and integration with the probabilistic model checker PRISM

Effective problem solving using SAT solvers

In this article we demonstrate how to solve a variety of problems and puzzles using the built-in SAT solver of the computer algebra system Maple. Once the problems have been encoded into Boolean logic, solutions can be found (or shown to not exist) automatically, without the need to implement any search algorithm. In particular, we describe how to solve the $n$-queens problem, how to generate and solve Sudoku puzzles, how to solve logic puzzles like the Einstein riddle, how to solve the 15-puzzle, how to solve the maximum clique problem, and finding Graeco-Latin squares.Comment: To appear in Proceedings of the Maple Conference 201

Weight-Aware Core Extraction in SAT-Based MaxSAT Solving

Peer reviewe

Solving and Verifying the Boolean Pythagorean Triples Problem via Cube-and-Conquer

We solved a long-outstanding open problem in Ramsey theory, using SAT solving

Encoding Redundancy for Satisfaction-Driven Clause Learning

Satisfaction-Driven Clause Learning (SDCL) is a recent SAT solving paradigm that aggressively trims the search space of possible truth assignments. To determine if the SAT solver is currently exploring a dispensable part of the search space, SDCL uses the so-called positive reduct of a formula: The positive reduct is an easily solvable propositional formula that is satisfiable if the current assignment of the solver can be safely pruned from the search space. In this paper, we present two novel variants of the positive reduct that allow for even more aggressive pruning. Using one of these variants allows SDCL to solve harder problems, in particular the well-known Tseitin formulas and mutilated chessboard problems. For the first time, we are able to generate and automatically check clausal proofs for large instances of these problems

Evolutionary algorithms for the satisfiability problem

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