A Preference-Based Approach to Backbone Computation with Application to Argumentation

The backbone of a constraint satisfaction problem consists of those variables that take the same value in all solutions. Algorithms for determining the backbone of propositional formulas, i.e., Boolean satisfiability (SAT) instances, find various real-world applications. From the knowledge representation and reasoning (KRR) perspective, one interesting connection is that of backbones and the so-called ideal semantics in abstract argumentation. In this paper, we propose a new backbone algorithm which makes use of a "SAT with preferences" solver, i.e., a SAT solver which is guaranteed to output a most preferred satisfying assignment w.r.t. a given preference over literals of the SAT instance at hand. We also show empirically that the proposed approach is specifically effective in computing the ideal semantics of argumentation frameworks, noticeably outperforming an other state-of-the-art backbone solver as well as the winning approach of the recent ICCMA 2017 argumentation solver competition in the ideal semantics track.Peer reviewe

A Resolution Prover for Coalition Logic

We present a prototype tool for automated reasoning for Coalition Logic, a non-normal modal logic that can be used for reasoning about cooperative agency. The theorem prover CLProver is based on recent work on a resolution-based calculus for Coalition Logic that operates on coalition problems, a normal form for Coalition Logic. We provide an overview of coalition problems and of the resolution-based calculus for Coalition Logic. We then give details of the implementation of CLProver and present the results for a comparison with an existing tableau-based solver

ON SIMPLE BUT HARD RANDOM INSTANCES OF PROPOSITIONAL THEORIES AND LOGIC PROGRAMS

In the last decade, Answer Set Programming (ASP) and Satisfiability (SAT) have been used to solve combinatorial search problems and practical applications in which they arise. In each of these formalisms, a tool called a solver is used to solve problems. A solver takes as input a specification of the problem – a logic program in the case of ASP, and a CNF theory for SAT – and produces as output a solution to the problem. Designing fast solvers is important for the success of this general-purpose approach to solving search problems. Classes of instances that pose challenges to solvers can help in this task. In this dissertation we create challenging yet simple benchmarks for existing solvers in ASP and SAT.We do so by providing models of simple logic programs as well as models of simple CNF theories. We then randomly generate logic programs as well as CNF theories from these models. Our experimental results show that computing answer sets of random logic programs as well as models of random CNF theories with carefully chosen parameters is hard for existing solvers. We generate random logic programs with 2-literals, and our experiments show that it is hard for ASP solvers to obtain answer sets of purely negative and constraint-free programs, indicating the importance of these programs in the development of ASP solvers. An easy-hard-easy pattern emerges as we compute the average number of choice points generated by ASP solvers on randomly generated 2-literal programs with an increasing number of rules. We provide an explanation for the emergence of this pattern in these programs. We also theoretically study the probability of existence of an answer set for sparse and dense 2-literal programs. We consider simple classes of mixed Horn formulas with purely positive 2- literal clauses and purely negated Horn clauses. First we consider a class of mixed Horn formulas wherein each formula has m 2-literal clauses and k-literal negated Horn clauses. We show that formulas that are generated from the phase transition region of this class are hard for complete SAT solvers. The second class of Mixed Horn Formulas we consider are obtained from completion of a certain class of random logic programs. We show the appearance of an easy-hard-easy pattern as we generate formulas from this class with increasing numbers of clauses, and that the formulas generated in the hard region can be used as benchmarks for testing incomplete SAT solvers

ON EQUIVALENCY REASONING FOR CONFLICT DRIVEN CLAUSE LEARNING SATISFIABILITY SOLVERS

Satisfiability problem or SAT is the problem of deciding whether a Boolean function evaluates to true for at least one of the assignments in its domain. The satisfiability problem is the first problem to be proved NP-complete. Therefore, the problems in NP can be encoded into SAT instances. Many hard real world problems can be solved when encoded efficiently into SAT instances. These facts give SAT an important place in both theoretical and practical computer science. In this thesis we address the problem of integrating a special class of equivalency reasoning techniques, the strongly connected components or SCC based reasoning, into the class of conflict driven clause learning or CDCL SAT solvers. Because of the complications that arise from integrating the equivalency reasoning in CDCL SAT solvers, to our knowledge, there has been no CDCL solver which has applied SCC based equivalency reasoning dynamically during the search. We propose a method to overcome these complications. The method is integrated into a prominent satisfiability solver: MiniSat. The equivalency enhanced MiniSat, Eq-MiniSat, is used to explore the advantages and disadvantages of the equivalency reasoning in conflict clause learning satisfiability solvers. Different implementation approaches for Eq-MiniSat are discussed. The experimental results on 16 families of instances shows that equivalency reasoning does not have noticeable effects for the instances in one family. The equivalency reasoning enables Eq-MiniSat to outperform MiniSat on eight classes of instances. For the remaining seven families, MiniSat outperforms Eq- MiniSat. The experimental results for random instances demonstrate that almost in all cases the number of branchings for Eq-Minisat is smaller than Minisat

Automated Deduction – CADE 28

This open access book constitutes the proceeding of the 28th International Conference on Automated Deduction, CADE 28, held virtually in July 2021. The 29 full papers and 7 system descriptions presented together with 2 invited papers were carefully reviewed and selected from 76 submissions. CADE is the major forum for the presentation of research in all aspects of automated deduction, including foundations, applications, implementations, and practical experience. The papers are organized in the following topics: Logical foundations; theory and principles; implementation and application; ATP and AI; and system descriptions