scholarly journals aspBEEF: Explaining Predictions Through Optimal Clustering

Proceedings ◽  
2020 ◽  
Vol 54 (1) ◽  
pp. 51
Author(s):  
Pedro Cabalar ◽  
Rodrigo Martín ◽  
Brais Muñiz ◽  
Gilberto Pérez
Keyword(s):  
Machine Learning ◽  
Answer Set Programming ◽  
Programming Paradigm ◽  
Learning Classifier ◽  
Np Complete ◽  
Np Problems ◽  
Answer Set

In this paper we introduce aspBEEF, a tool for generating explanations for the outcome of an arbitrary machine learning classifier. This is done using Grover’s et al. framework known as Balanced English Explanations of Forecasts (BEEF) that generates explanations in terms of in terms of finite intervals over the values of the input features. Since the problem of obtaining an optimal BEEF explanation has been proved to be NP-complete, BEEF existing implementation computes an approximation. In this work we use instead an encoding into the Answer Set Programming paradigm, specialized in solving NP problems, to guarantee that the computed solutions are optimal.

scholarly journals The Answer Set Programming Paradigm

AI Magazine ◽  
2016 ◽  
Vol 37 (3) ◽  
pp. 13-24 ◽  
Author(s):  
Tomi Janhunen ◽  
Ilkka Nimelä
Keyword(s):  
Answer Set Programming ◽  
Programming Paradigm ◽  
Application Problem ◽  
Answer Set

In this article, we give an overview of the answer set programming paradigm, explain its strengths, and illustrate its main features in terms of examples and an application problem.

scholarly journals Efficiently Coupling the I-DLV Grounder with ASP Solvers

2018 ◽  
Vol 20 (2) ◽  
pp. 205-224 ◽  
Author(s):  
FRANCESCO CALIMERI ◽  
CARMINE DODARO ◽  
DAVIDE FUSCÀ ◽  
SIMONA PERRI ◽  
JESSICA ZANGARI
Keyword(s):  
Machine Learning ◽  
Answer Set Programming ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Selection Of ◽  
Answer Set

We present ${{{{$\mathscr{I}$}-}\textsc{dlv}}+{{$\mathscr{MS}$}}}$, a new answer set programming (ASP) system that integrates an efficient grounder, namely ${{{$\mathscr{I}$}-}\textsc{dlv}}$, with an automatic selector that inductively chooses a solver: depending on some inherent features of the instantiation produced by ${{{$\mathscr{I}$}-}\textsc{dlv}}$, machine learning techniques guide the selection of the most appropriate solver. The system participated in the latest (7th) ASP competition, winning the regular track, category SP (i.e., one processor allowed).

scholarly journals Towards automated integration of guess and check programs in answer set programming: a meta-interpreter and applications

2006 ◽  
Vol 6 (1-2) ◽  
pp. 23-60 ◽  
Author(s):  
THOMAS EITER ◽  
AXEL POLLERES
Keyword(s):  
Problem Solving ◽  
Logic Program ◽  
Answer Set Programming ◽  
Polynomial Hierarchy ◽  
Logic Programs ◽  
Complete Problems ◽  
Polynomial Size ◽  
Disjunctive Logic Programs ◽  
Np Complete ◽  
Answer Set

Answer set programming (ASP) with disjunction offers a powerful tool for declaratively representing and solving hard problems. Many NP-complete problems can be encoded in the answer set semantics of logic programs in a very concise and intuitive way, where the encoding reflects the typical “guess and check” nature of NP problems: The property is encoded in a way such that polynomial size certificates for it correspond to stable models of a program. However, the problem-solving capacity of full disjunctive logic programs (DLPs) is beyond NP, and captures a class of problems at the second level of the polynomial hierarchy. While these problems also have a clear “guess and check” structure, finding an encoding in a DLP reflecting this structure may sometimes be a non-obvious task, in particular if the “check” itself is a co-NP-complete problem; usually, such problems are solved by interleaving separate guess and check programs, where the check is expressed by inconsistency of the check program. In this paper, we present general transformations of head-cycle free (extended) disjunctive logic programs into stratified and positive (extended) disjunctive logic programs based on meta-interpretation techniques. The answer sets of the original and the transformed program are in simple correspondence, and, moreover, inconsistency of the original program is indicated by a designated answer set of the transformed program. Our transformations facilitate the integration of separate “guess” and “check” programs, which are often easy to obtain, automatically into a single disjunctive logic program. Our results complement recent results on meta-interpretation in ASP, and extend methods and techniques for a declarative “guess and check” problem solving paradigm through ASP.

scholarly journals A General Framework for Stable Roommates Problems using Answer Set Programming

2020 ◽  
Vol 20 (6) ◽  
pp. 911-925
Author(s):  
ESRA ERDEM ◽  
MÜGE FIDAN ◽  
DAVID MANLOVE ◽  
PATRICK PROSSER
Keyword(s):  
General Framework ◽  
Answer Set Programming ◽  
The Other ◽  
Stable Marriage ◽  
Stable Marriage Problem ◽  
Marriage Problem ◽  
Programming Paradigm ◽  
Formal Framework ◽  
The Stable Marriage Problem ◽  
Answer Set

AbstractThe Stable Roommates problem (SR) is characterized by the preferences of agents over other agents as roommates: each agent ranks all others in strict order of preference. A solution to SR is then a partition of the agents into pairs so that each pair shares a room, and there is no pair of agents that would block this matching (i.e., who prefers the other to their roommate in the matching). There are interesting variations of SR that are motivated by applications (e.g., the preference lists may be incomplete (SRI) and involve ties (SRTI)), and that try to find a more fair solution (e.g., Egalitarian SR). Unlike the Stable Marriage problem, every SR instance is not guaranteed to have a solution. For that reason, there are also variations of SR that try to find a good-enough solution (e.g., Almost SR). Most of these variations are NP-hard. We introduce a formal framework, called SRTI-ASP, utilizing the logic programming paradigm Answer Set Programming, that is provable and general enough to solve many of such variations of SR. Our empirical analysis shows that SRTI-ASP is also promising for applications.

Answer Sets and the Language of Answer Set Programming

AI Magazine ◽  
2016 ◽  
Vol 37 (3) ◽  
pp. 7-12 ◽  
Author(s):  
Vladimir Lifschitz
Keyword(s):  
Answer Set Programming ◽  
Declarative Programming ◽  
Logic Programs ◽  
Special Issue ◽  
Programming Paradigm ◽  
Introductory Article ◽  
Answer Set Semantics ◽  
Answer Sets ◽  
Semantics Of Logic Programs ◽  
Answer Set

Answer set programming is a declarative programming paradigm based on the answer set semantics of logic programs. This introductory article provides the mathematical background for the discussion of answer set programming in other contributions to this special issue.

scholarly journals Very Hard Electoral Control Problems

2019 ◽  
Vol 33 ◽  
pp. 1933-1940 ◽  
Author(s):  
Zack Fitzsimmons ◽  
Edith Hemaspaandra ◽  
Alexander Hoover ◽  
David E. Narváez
Keyword(s):  
Answer Set Programming ◽  
Polynomial Hierarchy ◽  
Control Problems ◽  
Complete Problems ◽  
Electoral Control ◽  
Election Systems ◽  
High Level ◽  
Np Problems ◽  
Very High ◽  
Answer Set

It is important to understand how the outcome of an election can be modified by an agent with control over the structure of the election. Electoral control has been studied for many election systems, but for all these systems the winner problem is in P, and so control is in NP. There are election systems, such as Kemeny, that have many desirable properties, but whose winner problems are not in NP. Thus for such systems control is not in NP, and in fact we show that it is typically complete for ∑p2 (i.e., NPNP, the second level of the polynomial hierarchy). This is a very high level of complexity. Approaches that perform quite well for solving NP problems do not necessarily work for ∑p2-complete problems. However, answer set programming is suited to express problems in ∑p2, and we present an encoding for Kemeny control.

scholarly journals Disjunctive answer set solvers via templates

2015 ◽  
Vol 16 (4) ◽  
pp. 465-497 ◽  
Author(s):  
REMI BROCHENIN ◽  
MARCO MARATEA ◽  
YULIYA LIERLER
Keyword(s):  
Answer Set Programming ◽  
Declarative Programming ◽  
Stable Model ◽  
Combinatorial Search ◽  
Logic Programs ◽  
Transition Systems ◽  
Satisfiability Solvers ◽  
Programming Paradigm ◽  
New Ideas ◽  
Answer Set

AbstractAnswer 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 ΣP2-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.

scholarly journals Dual-normal logic programs – the forgotten class

2015 ◽  
Vol 15 (4-5) ◽  
pp. 495-510 ◽  
Author(s):  
JOHANNES K. FICHTE ◽  
MIROSŁAW TRUSZCZYŃSKI ◽  
STEFAN WOLTRAN
Keyword(s):  
Answer Set Programming ◽  
Expressive Power ◽  
Declarative Programming ◽  
The Novel ◽  
Logic Programs ◽  
Programming Paradigm ◽  
Normal Logic ◽  
Consistency Problem ◽  
Theoretical Standpoint ◽  
Answer Set

AbstractDisjunctive Answer Set Programming is a powerful declarative programming paradigm with complexity beyond NP. Identifying classes of programs for which the consistency problem is in NP is of interest from the theoretical standpoint and can potentially lead to improvements in the design of answer set programming solvers. One of such classes consists of dual-normal programs, where the number of positive body atoms in proper rules is at most one. Unlike other classes of programs, dual-normal programs have received little attention so far. In this paper we study this class. We relate dual-normal programs to propositional theories and to normal programs by presenting several inter-translations. With the translation from dual-normal to normal programs at hand, we introduce the novel class of body-cycle free programs, which are in many respects dual to head-cycle free programs. We establish the expressive power of dual-normal programs in terms of SE- and UE-models, and compare them to normal programs. We also discuss the complexity of deciding whether dual-normal programs are strongly and uniformly equivalent.

scholarly journals A multi-engine approach to answer-set programming

2013 ◽  
Vol 14 (6) ◽  
pp. 841-868 ◽  
Author(s):  
MARCO MARATEA ◽  
LUCA PULINA ◽  
FRANCESCO RICCA
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Answer Set Programming ◽  
Machine Learning Techniques ◽  
Algorithm Selection ◽  
The Third ◽  
Selection Strategies ◽  
Learning Techniques ◽  
Test Sets ◽  
Answer Set

AbstractAnswer-set programming (ASP) is a truly declarative programming paradigm proposed in the area of non-monotonic reasoning and logic programming, which 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 atrainingset and the solvers' performance on these instances, inductively learn algorithm selection strategies to be applied to atestset. 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 Third 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 Third ASP Competition.

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