Flexible scheduling of diagnostic tests in automotive manufacturing

The car of the future will be driven by software and offer a variety of customisation options. Enabling these customisation options forces modern automotive manufacturers to update their standardised scheduling concepts for testing and commissioning cars. A flexible scheduling concept means that every chosen customer configuration code must have its own testing procedure. This concept is essential to provide individual testing workflows where the time and resources are optimised for every car. Manual scheduling is complicated due to constraints on time, predecessor-successor relationships, mutual exclusion criteria, resources and status conditions on the car engineering and assembly line. Applied methods to handle the mathematical formulation for the corresponding industrial optimisation problem and its implementation are not yet available. This paper presents a procedure for automated and non-preemptive scheduling in the testing and commissioning of cars, which is built on a Boolean satisfiability problem on parallel and identical machines with temporal and resource constraints. The presented method is successfully implemented and evaluated on a variant assembly line of an automotive Original Equipment Manufacturer. This paper is the starting point for an automated workflow planning and scheduling process in automotive manufacturing.

Linear Cryptanalyses of Three AEADs with GIFT-128 as Underlying Primitives

This paper considers the linear cryptanalyses of Authenticated Encryptions with Associated Data (AEADs) GIFT-COFB, SUNDAE-GIFT, and HyENA. All of these proposals take GIFT-128 as underlying primitives. The automatic search with the Boolean satisfiability problem (SAT) method is implemented to search for linear approximations that match the attack settings concerning these primitives. With the newly identified approximations, we launch key-recovery attacks on GIFT-COFB, SUNDAE-GIFT, and HyENA when the underlying primitives are replaced with 16-round, 17-round, and 16-round versions of GIFT-128. The resistance of GIFT-128 against linear cryptanalysis is also evaluated. We present a 24-round key-recovery attack on GIFT-128 with a newly obtained 19-round linear approximation. We note that the attack results in this paper are far from threatening the security of GIFT-COFB, SUNDAE-GIFT, HyENA, and GIFT-128.

Analysis of comparative effectiveness of state-of-the-art heuristics for CDCL SAT solvers

The Conflict-Driven Clause Learning algorithms for solving the Boolean satisfiability problem comprise the major part of the methods used to solve various instances of the problems that arise in industry and science. In recent years there have been proposed several major heuristics for these algorithms which are assumed to be de facto good for the solvers’ performance over diverse sets of benchmarks. The goal of this paper is to evaluate the contribution of each separate heuristic to the performance of a state-of-the-art solver, see the extent to which they are beneficial, and figure out if the heuristics have any particular features that need to be taken into account.

Compilation-Based Approaches to Parallel Planning: An Empirical Comparison

Automated planning deals with finding a sequence of actions, a plan, to reach a goal. One of the possible approaches to automated planning is a compilation of a planning problem to a Boolean satisfiability problem or to a constraint satisfaction problem, which takes direct advantage of the advancements of satisfiability and constraint satisfaction solvers. This paper provides a comparison of three encodings proposed for the compilation of planning problems: Transition constraints for parallel planning (TCPP), Relaxed relaxed exist-Step encoding and Reinforced Encoding. We implemented the encodings using the programming language Picat 2.8, we suggested certain modifications, and we compared the performance of the encodings on benchmarks from international planning competitions.

Accelerating the Search of Differential and Linear Characteristics with the SAT Method

The introduction of the automatic search boosts the cryptanalysis of symmetric-key primitives to some degree. However, the performance of the automatic search is not always satisfactory for the search of long trails or ciphers with large state sizes. Compared with the extensive attention on the enhancement for the search with the mixed integer linear programming (MILP) method, few works care for the acceleration of the automatic search with the Boolean satisfiability problem (SAT) or satisfiability modulo theories (SMT) method. This paper intends to fill this vacancy. Firstly, with the additional encoding variables of the sequential counter circuit for the original objective function in the standard SAT method, we put forward a new encoding method to convert the Matsui’s bounding conditions into Boolean formulas. This approach does not rely on new auxiliary variables and significantly reduces the consumption of clauses for integrating multiple bounding conditions into one SAT problem. Then, we evaluate the accelerating effect of the novel encoding method under different sets of bounding conditions. With the observations and experience in the tests, a strategy on how to create the sets of bounding conditions that probably achieve extraordinary advances is proposed. The new idea is applied to search for optimal differential and linear characteristics for multiple ciphers. For PRESENT, GIFT-64, RECTANGLE, LBlock, TWINE, and some versions in SIMON and SPECK families of block ciphers, we obtain the complete bounds (full rounds) on the number of active S-boxes, the differential probability, as well as the linear bias. The acceleration method is also employed to speed up the search of related-key differential trails for GIFT-64. Based on the newly identified 18-round distinguisher with probability 2−58, we launch a 26-round key-recovery attack with 260.96 chosen plaintexts. To our knowledge, this is the longest attack on GIFT-64. Lastly, we note that the attack result is far from threatening the security of GIFT-64 since the designers recommended users to double the number of rounds under the related-key attack setting.

Method for Adaptation of Algorithms to GPU Architecture

We propose a generalized method for adapting and optimizing algorithms for efficient execution on modern graphics processing units (GPU). The method consists of several steps. First, build a control flow graph (CFG) of the algorithm. Next, transform the CFG into a tree of loops and merge non-parallelizable loops into parallelizable ones. Finally, map the resulting loops tree to the tree of GPU computational units, unrolling the algorithm’s loops as necessary for the match. The mapping should be performed bottom-up, from the lowest GPU architecture levels to the highest ones, to minimize off-chip memory access and maximize register file usage. The method provides programmer with a convenient and robust mental framework and strategy for GPU code optimization. We demonstrate the method by adapting to a GPU the DPLL backtracking search algorithm for solving the Boolean satisfiability problem (SAT). The resulting GPU version of DPLL outperforms the CPU version in raw tree search performance sixfold for regular Boolean satisfiability problems and twofold for irregular ones.

Discrete Artificial Dragonflies Algorithm in Agent Based Modelling for Exact Boolean kSatisfiability Problem

The development of metaheuristics and Boolean Satisfiability representation plays an important part in a neural network (NN) and Artificial Intelligence (AI) communities. In this paper, a new hybrid discrete version of the artificial dragonfly algorithm (DADA) applying a minimum objective function in agent-based modelling (ABM) obeying a specified procedure to optimize the states of neurons, for optimal Boolean Exact Satisfiability representation on NETLOGO as a dynamic platform. We combined the artificial dragonfly algorithm for its random searching ability that encourages diverse solutions and formation of static swarm’s mechanism to stimulus computational problems to converge to the best global optimal search space. The global performance of the proposed DADA was compared with genetic algorithm (GA) that are available in the literature based on the global minimum ratio (gM), Local Minimum Ratio (yM), Computational time (CPU) and Hamming distance (HD). The final results showed good agreement between the proposed DADA and discrete version of GA to efficiently optimize the Exact-kSAT problem. It found that DADA-ABM has high potentiality for optimizing or modelling a network that is very hard or often impossible to capture by exact or traditional optimization modelling techniques such as Boolean satisfiability problem is better than existing methods in the literature.

NLocalSAT: Boosting Local Search with Solution Prediction

The Boolean satisfiability problem (SAT) is a famous NP-complete problem in computer science. An effective way for solving a satisfiable SAT problem is the stochastic local search (SLS). However, in this method, the initialization is assigned in a random manner, which impacts the effectiveness of SLS solvers. To address this problem, we propose NLocalSAT. NLocalSAT combines SLS with a solution prediction model, which boosts SLS by changing initialization assignments with a neural network. We evaluated NLocalSAT on five SLS solvers (CCAnr, Sparrow, CPSparrow, YalSAT, and probSAT) with instances in the random track of SAT Competition 2018. The experimental results show that solvers with NLocalSAT achieve 27% ~ 62% improvement over the original SLS solvers.

Unsatisfiability Proofs for Weight 16 Codewords in Lam's Problem

In the 1970s and 1980s, searches performed by L. Carter, C. Lam, L. Thiel, and S. Swiercz showed that projective planes of order ten with weight 16 codewords do not exist. These searches required highly specialized and optimized computer programs and required about 2,000 hours of computing time on mainframe and supermini computers. In 2010, these searches were verified by D. Roy using an optimized C program and 16,000 hours on a cluster of desktop machines. We performed a verification of these searches by reducing the problem to the Boolean satisfiability problem (SAT). Our verification uses the cube-and-conquer SAT solving paradigm, symmetry breaking techniques using the computer algebra system Maple, and a result of Carter that there are ten nonisomorphic cases to check. Our searches completed in about 30 hours on a desktop machine and produced nonexistence proofs of about 1 terabyte in the DRAT (deletion resolution asymmetric tautology) format.