The Multi-engine ASP Solver ME-ASP: Progress Report

MEASP is a multi-engine solver for ground ASP programs. It exploits algorithm selection techniques based on classification to select one among a set of out-of-the-box heterogeneous ASP solvers used as black-box engines. In this paper we report on (i) a new optimized implementation of MEASP; and (ii) an attempt of applying algorithm selection to non-ground programs. An experimental analysis reported in the paper shows that (i) the new implementation of \measp is substantially faster than the previous version; and (ii) the multi-engine recipe can be applied to the evaluation of non-ground programs with some benefits

Disjunctive Answer Set Solvers via Templates

Answer set programming is a declarative programming paradigm oriented towards difficult combinatorial search problems. A fundamental task in answer set programming is to compute stable models, i.e., solutions of logic programs. Answer set solvers are the programs that perform this task. The problem of deciding whether a disjunctive program has a stable model is $\Sigma^P_2$-complete. The high complexity of reasoning within disjunctive logic programming is responsible for few solvers capable of dealing with such programs, namely DLV, GnT, Cmodels, CLASP and WASP. In this paper we show that transition systems introduced by Nieuwenhuis, Oliveras, and Tinelli to model and analyze satisfiability solvers can be adapted for disjunctive answer set solvers. Transition systems give a unifying perspective and bring clarity in the description and comparison of solvers. They can be effectively used for analyzing, comparing and proving correctness of search algorithms as well as inspiring new ideas in the design of disjunctive answer set solvers. In this light, we introduce a general template, which accounts for major techniques implemented in disjunctive solvers. We then illustrate how this general template captures solvers DLV, GnT and Cmodels. We also show how this framework provides a convenient tool for designing new solving algorithms by means of combinations of techniques employed in different solvers.Comment: To appear in Theory and Practice of Logic Programming (TPLP

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

The Design of the Fifth Answer Set Programming Competition

Answer Set Programming (ASP) is a well-established paradigm of declarative programming that has been developed in the field of logic programming and nonmonotonic reasoning. Advances in ASP solving technology are customarily assessed in competition events, as it happens for other closely-related problem-solving technologies like SAT/SMT, QBF, Planning and Scheduling. ASP Competitions are (usually) biennial events; however, the Fifth ASP Competition departs from tradition, in order to join the FLoC Olympic Games at the Vienna Summer of Logic 2014, which is expected to be the largest event in the history of logic. This edition of the ASP Competition series is jointly organized by the University of Calabria (Italy), the Aalto University (Finland), and the University of Genova (Italy), and is affiliated with the 30th International Conference on Logic Programming (ICLP 2014). It features a completely re-designed setup, with novelties involving the design of tracks, the scoring schema, and the adherence to a fixed modeling language in order to push the adoption of the ASP-Core-2 standard. Benchmark domains are taken from past editions, and best system packages submitted in 2013 are compared with new versions and solvers. To appear in Theory and Practice of Logic Programming (TPLP).Comment: 10 page

CASP Solutions for Planning in Hybrid Domains

CASP is an extension of ASP that allows for numerical constraints to be added in the rules. PDDL+ is an extension of the PDDL standard language of automated planning for modeling mixed discrete-continuous dynamics. In this paper, we present CASP solutions for dealing with PDDL+ problems, i.e., encoding from PDDL+ to CASP, and extensions to the algorithm of the EZCSP CASP solver in order to solve CASP programs arising from PDDL+ domains. An experimental analysis, performed on well-known linear and non-linear variants of PDDL+ domains, involving various configurations of the EZCSP solver, other CASP solvers, and PDDL+ planners, shows the viability of our solution.Comment: Under consideration in Theory and Practice of Logic Programming (TPLP

a bound for the accuracy of sensors acquiring compositional data

AbstractAmong the many challenges that the Internet of Things poses, the accuracy of the sensor network and relative data flow is of the foremost importance: sensors monitor the surrounding environment of an object and give information on its position, situation or context, and an error in the acquired data can lead to inappropriate decisions and uncontrolled consequences. Given a sensor network that gathers relative data – that is data for which ratios of parts are more important than absolute values – acquired data have a compositional nature and all values need to be scaled. To analyze these data a common practice is to map bijectively compositions into the ordinary euclidean space through a suitable transformation, so that standard multivariate analysis techniques can be used. In this paper an error bound on the commonly used asymmetric log-ratio transformation is found in the Simplex. The purpose is to highlight areas of the Simplex where the transformation is ill conditioned and to isolate values for which the additive log-ratio transform cannot be accurately computed. Results show that the conditioning of the transformation is strongly affected by the closeness of the transformed values and that not negligible distortions can be generated due to the unbounded propagation of the errors. An explicit formula for the accuracy of the sensors given the maximum allowed tolerance has been derived, and the critical values in the Simplex where the transformation is component-wise ill conditioned have been isolated

Pitfalls of local explainability in complex black-box models

Post hoc models are becoming popular as additional tools to evaluate the results of black-box models and to provide explanations of the predictions they give. In this paper the main concerns that Local Induced models raise in the pointwise explanation of heavily overparametrized black-box models are discussed in depth, highlighting some vulnerabilities, some underrated issues and giving some warnings on the potentially negative effect on user trust of this explainability framewor

Computing Answer Sets of a Logic Program via-enumeration of SAT certificates

Answer set programming is a new programming paradigm proposed based on the answer set semantics of Prolog. It is well known that an answer set for a logic program is also a model of the program\u27s completion. The converse is true when the logic program is tight . Lin and Zhao showed that for non-tight programs the models of completion which do not correspond to answer sets can be eliminated by adding to the completion what they called loop formulas . Nevertheless, their solver ASSAT 1 has some disadvantages: it can work only with basic rules, and it can compute only one answer set. Answer set solver CMODELS-1 is a system that computes answer sets for logic programs that are tight or can be transformed into tight programs, and does not suffer from these limitations. We are going to present a new system CMODELS-2 , that is able to fix ASSAT\u27s disadvantages. Another attractive feature of the new system is that it organizes the search process more efficiently then ASSAT, because it does not explore the same part of the search tree more than once. In the rest of the paper we will omit number 2 in the name of the system

Adaptive quick reduct for feature drift detection

Data streams are ubiquitous and related to the proliferation of low-cost mobile devices, sensors, wireless networks and the Internet of Things. While it is well known that complex phenomena are not stationary and exhibit a concept drift when observed for a sufficiently long time, relatively few studies have addressed the related problem of feature drift. In this paper, a variation of the QuickReduct algorithm suitable to process data streams is proposed and tested: it builds an evolving reduct that dynamically selects the relevant features in the stream, removing the redundant ones and adding the newly relevant ones as soon as they become such. Tests on five publicly available datasets with an artificially injected drift have confirmed the effectiveness of the proposed method

A Tool for Encoding Controlled Natural Language Specifications as ASP Rules.

Answer Set Programming (ASP) is a popular declarative programming language for solving hard combinatorial problems. Albeit ASP has been widely adopted in both academic and industrial contexts, it might be difficult for people who are not familiar with logic programming conventions to use it. In this paper, we propose a translation of English sentences expressed in a controlled natural language (CNL) form into ASP. In particular, we first provide a definition of the type of sentences allowed by our CNL and their translation as ASP rules, and then exemplify the usage of CNL for the specification of well-known combinatorial problems