Automatic Proving of Fuzzy Formulae with Fuzzy Logic Programming and SMT

In this paper we deal with propositional fuzzy formulae containing severalpropositional symbols linked with connectives defined in a lattice of truth degrees more complex than Bool. We firstly recall an SMT (Satisfiability Modulo Theories) based method for automatically proving theorems in relevant infinitely valued (including Łukasiewicz and G¨odel) logics. Next, instead of focusing on satisfiability (i.e., proving the existence of at least one model) or unsatisfiability, our interest moves to the problem of finding the whole set of models (with a finite domain) for a given fuzzy formula. We propose an alternative method based on fuzzy logic programming where the formula is conceived as a goal whose derivation tree contains on its leaves all the models of the original formula, by exhaustively interpreting each propositional symbol in all the possible forms according the whole setof values collected on the underlying lattice of truth-degrees

On the semantics of hybrid ASP systems based on Clingo

[Abstract]: Over the last decades, the development of Answer Set Programming (ASP) has brought about an expressive modeling language powered by highly performant systems. At the same time, it gets more and more difficult to provide semantic underpinnings capturing the resulting constructs and inferences. This is even more severe when it comes to hybrid ASP languages and systems that are often needed to handle real-world applications. We address this challenge and introduce the concept of abstract and structured theories that allow us to formally elaborate upon their integration with ASP. We then use this concept to make the semantic characterization of clingo’s theory-reasoning framework precise. This provides us with a formal framework in which we can elaborate upon the formal properties of existing hybridizations of clingo, such as clingcon, clingo[dl], and clingo[lp].This work was supported by DFG grant SCHA 550/11, Germany, by grant PID2020-116201GB-I00 funded by MCIN/AEI/ 10.13039/501100011033, Spain, by Xunta de Galicia and the European Union, GPC ED431B 2022/33, by European COST action CA17124 DigForASP, EU, and by the National Science Foundation (NSF 95-3101-0060-402), USA.Xunta de Galicia; ED431B 2022/33Deutsche Forschungsgemeinschaft; SCHA 550/11United States. National Science Foundation; NSF 95-3101-0060-40

Distributed First Order Logic

Distributed First Order Logic (DFOL) has been introduced more than ten years ago with the purpose of formalising distributed knowledge-based systems, where knowledge about heterogeneous domains is scattered into a set of interconnected modules. DFOL formalises the knowledge contained in each module by means of first-order theories, and the interconnections between modules by means of special inference rules called bridge rules. Despite their restricted form in the original DFOL formulation, bridge rules have influenced several works in the areas of heterogeneous knowledge integration, modular knowledge representation, and schema/ontology matching. This, in turn, has fostered extensions and modifications of the original DFOL that have never been systematically described and published. This paper tackles the lack of a comprehensive description of DFOL by providing a systematic account of a completely revised and extended version of the logic, together with a sound and complete axiomatisation of a general form of bridge rules based on Natural Deduction. The resulting DFOL framework is then proposed as a clear formal tool for the representation of and reasoning about distributed knowledge and bridge rules

Admissibility of Π₂-inference rules: Interpolation, model completion, and contact algebras

We devise three strategies for recognizing admissibility of non-standard inference rules via interpolation, uniform interpolation, and model completions. We apply our machinery to the case of symmetric implication calculus S2IC, where we also supply a finite axiomatization of the model completion of its algebraic counterpart, via the equivalent theory of contact algebras. Using this result we obtain a finite basis for admissible Π2-rules

Automated Reasoning

This volume, LNAI 13385, constitutes the refereed proceedings of the 11th International Joint Conference on Automated Reasoning, IJCAR 2022, held in Haifa, Israel, in August 2022. The 32 full research papers and 9 short papers presented together with two invited talks were carefully reviewed and selected from 85 submissions. The papers focus on the following topics: Satisfiability, SMT Solving,Arithmetic; Calculi and Orderings; Knowledge Representation and Jutsification; Choices, Invariance, Substitutions and Formalization; Modal Logics; Proofs System and Proofs Search; Evolution, Termination and Decision Prolems. This is an open access book

A Multi-Engine Approach to Answer Set Programming

Answer Set Programming (ASP) is a truly-declarative programming paradigm proposed in the area of non-monotonic reasoning and logic programming, that has been recently employed in many applications. The development of efficient ASP systems is, thus, crucial. Having in mind the task of improving the solving methods for ASP, there are two usual ways to reach this goal: $(i)$ extending state-of-the-art techniques and ASP solvers, or $(ii)$ designing a new ASP solver from scratch. An alternative to these trends is to build on top of state-of-the-art solvers, and to apply machine learning techniques for choosing automatically the "best" available solver on a per-instance basis. In this paper we pursue this latter direction. We first define a set of cheap-to-compute syntactic features that characterize several aspects of ASP programs. Then, we apply classification methods that, given the features of the instances in a {\sl training} set and the solvers' performance on these instances, inductively learn algorithm selection strategies to be applied to a {\sl test} set. We report the results of a number of experiments considering solvers and different training and test sets of instances taken from the ones submitted to the "System Track" of the 3rd ASP Competition. Our analysis shows that, by applying machine learning techniques to ASP solving, it is possible to obtain very robust performance: our approach can solve more instances compared with any solver that entered the 3rd ASP Competition. (To appear in Theory and Practice of Logic Programming (TPLP).)Comment: 26 pages, 8 figure

PEALT: A reasoning tool for numerical aggregation of trust evidence

We present a tool that supports the understanding and validation of mechanisms that numerically aggregate trust evidence { which may stem from heterogenous sources such as geographical information, reputation, and threat levels. The tool is based on a policy com- position language Peal [3] and can declare Peal expressions and intended analyses of such expressions as input. The analyses include vacuity checking, sensitivity analysis of thresh- olds, and policy re nement. We develop and implement two methods for generating veri - cation conditions for analyses, using the SMT solver Z3 as backend. One method is explicit and space intense, the other one is symbolic and so linear in the analysis expressions. We experimentally investigate this space-time tradeo by observing the Z3 code generation and its running time on randomly generated analyses and on a non-random benchmark modeling majority voting. Our ndings suggest both methods have complementary value and may scale up su ciently for the analysis of most realistic case studies

A Tree Locality-Sensitive Hash for Secure Software Testing

Bugs in software that make it through testing can cost tens of millions of dollars each year, and in some cases can even result in the loss of human life. In order to eliminate bugs, developers may use symbolic execution to search through possible program states looking for anomalous states. Most of the computational effort to search through these states is spent solving path constraints in order to determine the feasibility of entering each state. State merging can make this search more efficient by combining program states, allowing multiple execution paths to be analyzed at the same time. However, a merge with dissimilar path constraints dramatically increases the time necessary to solve the path constraint. Currently, there are no distance measures for path constraints, and pairwise comparison of program states is not scalable. A hashing method is presented that clusters constraints in such a way that similar constraints are placed in the same cluster without requiring pairwise comparisons between queries. When combined with other state-of-the-art state merging techniques, the hashing method allows the symbolic executor to execute more instructions per second and find more terminal execution states than the other techniques alone, without decreasing the high path coverage achieved by merging many states together