Dealing Automatically with Exceptions by Introducing Specificity in ASP

Answer Set Programming (ASP), via normal logic programs, is known as a suitable framework for default reasoning since it offers both a valid formal model and operational systems. However, in front of a real world knowledge representation problem, it is not easy to represent information in this framework. That is why the present article proposed to deal with this issue by generating in an automatic way the suitable normal logic program from a compact representation of the information. This is done by using a method, based on specificity, that has been developed for default logic and which is adapted here to ASP both in theoretical and practical points of view

SMT-Based False Positive Elimination in Static Program Analysis

Static program analysis for bug detection in large C/C++ projects typically uses a high-level abstraction of the original program under investigation. As a result, so-called false positives are often inevitable, i.e., warnings that are not true bugs. In this work we present a novel abstraction refinement approach to automatically investigate and eliminate such false positives. Central to our approach is to view static analysis as a model checking problem, to iteratively compute infeasible sub-paths of infeasible paths using SMT solvers, and refine our models by adding observer automata to exclude such paths. Based on this new framework we present an implementation of the approach into the static analyzer Goanna and discuss a number of real-life experiments on larger C code projects, demonstrating that we were able to remove most false positives automatically

Generalized property directed reachability

The IC3 algorithm was recently introduced for proving properties of finite state reactive systems. It has been applied very success-fully to hardware model checking. We provide a specification of the algorithm using an abstract transition system and highlight its dual operation: model search and conflict resolution. We then generalize it along two dimensions. Along one dimension we address nonlinear fixed-point operators (push-down systems) and evaluate the algorithm on Boolean programs. In the second dimension we leverage proofs and models and generalize the method to Boolean constraints involving theories

A General Artificial Intelligence Approach for Skeptical Reasoning

International audienc

Improvements to core-guided binary search for MaxSAT

Maximum Satisfiability (MaxSAT) and its weighted variants are wellknown optimization formulations of Boolean Satisfiability (SAT). Motivated by practical applications, recent years have seen the development of core-guided algorithms for MaxSAT. Among these, core-guided binary search with disjoint cores (BCD) represents a recent robust solution. This paper identifies a number of inefficiencies in the original BCD algorithm, related with the computation of lower and upper bounds during the execution of the algorithm, and develops solutions for them. In addition, the paper proposes two additional novel techniques, which can be implemented on top of core-guided MaxSAT algorithms that maintain both lower and upper bounds. Experimental results, obtained on representative problem instances, indicate that the proposed optimizations yield significant performance gains, and allow solving more problem instances