Abstract. The classical NP-complete problem of Boolean Satisfiability (SAT) has seen much interest in not just the theoretical computer science community, but also in areas where practical solutions to this problem enable significant practical applications. Since the first development of the basic search based algorithm proposed by Davis, Putnam, Logemann and Loveland (DPLL) about forty years ago, this area has seen active research effort with many interesting contributions that have culminated in state-of-the-art SAT solvers today being able to handle problem instances with thousands, and in same cases even millions, of variables. In this paper we examine some of the main ideas along this passage that have led to our current capabilities. Given the depth of the literature in this field, it is impossible to do this in any comprehensive way; rather we focus on techniques with consistent demonstrated efficiency in available solvers. For the most part, we focus on techniques within the basic DPLL search framework, but also briefly describe other approaches and look at some possible future research directions.

No abstract

Within the verification community, there has been a recent increase in interest in Quantified Boolean Formula evaluation (QBF) as many interesting sequential circuit verification problems can be formulated as QBF instances. A closely related research area to QBF is Boolean Satisfiability (SAT). Recent advances in SAT research have resulted in some very efficient SAT solvers. One of the critical techniques employed in these solvers is Conflict Driven Learning. In this paper, we adapt conflict driven learning for application in a QBF setting. We show that conflict driven learning can be regarded as a resolution process on the clauses. We prove that under certain conditions, tautology clauses obtained from resolution in QBF also obey the rules for implication and conflicts of regular (non-tautology) clauses; and therefore they can be treated as regular clauses and used in future search. We have implemented this idea in a new QBF solver called Quaffle and our initial experiments show that conflict driven learning can greatly speed up the solution process for most of the benchmarks we tested.

No abstract

No abstract

Satisfiability solvers can now be effectively deployed in practical applications.

No abstract

Abstract-Peer-to-peer (P2P) worms exploit common vulnerabilities in member hosts of a P2P network and spread topologically in the P2P network, a potentially more effective strategy than random scanning for locating victims. This paper describes the danger posed by P2P worms and initiates the study of possible mitigation mechanisms. In particular, the paper explores the feasibility of a self-defense infrastructure inside a P2P network, outlines the challenges, evaluates how well this defense mechanism contains P2P worms, and reveals correlations between containment and the overlay topology of a P2P network. Our experiments suggest a number of design directions to improve the resilience of P2P networks to worm attacks.