TY - JOUR A1 - Cabalar, Pedro A1 - Fandinno, Jorge A1 - Schaub, Torsten H. A1 - Schellhorn, Sebastian T1 - Gelfond-Zhang aggregates as propositional formulas JF - Artificial intelligence N2 - Answer Set Programming (ASP) has become a popular and widespread paradigm for practical Knowledge Representation thanks to its expressiveness and the available enhancements of its input language. One of such enhancements is the use of aggregates, for which different semantic proposals have been made. In this paper, we show that any ASP aggregate interpreted under Gelfond and Zhang's (GZ) semantics can be replaced (under strong equivalence) by a propositional formula. Restricted to the original GZ syntax, the resulting formula is reducible to a disjunction of conjunctions of literals but the formulation is still applicable even when the syntax is extended to allow for arbitrary formulas (including nested aggregates) in the condition. Once GZ-aggregates are represented as formulas, we establish a formal comparison (in terms of the logic of Here-and-There) to Ferraris' (F) aggregates, which are defined by a different formula translation involving nested implications. In particular, we prove that if we replace an F-aggregate by a GZ-aggregate in a rule head, we do not lose answer sets (although more can be gained). This extends the previously known result that the opposite happens in rule bodies, i.e., replacing a GZ-aggregate by an F-aggregate in the body may yield more answer sets. Finally, we characterize a class of aggregates for which GZ- and F-semantics coincide. KW - Aggregates KW - Answer Set Programming Y1 - 2019 U6 - https://doi.org/10.1016/j.artint.2018.10.007 SN - 0004-3702 SN - 1872-7921 VL - 274 SP - 26 EP - 43 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Gebser, Martin A1 - Schaub, Torsten H. T1 - Tableau calculi for logic programs under answer set semantics JF - ACM transactions on computational logic N2 - We introduce formal proof systems based on tableau methods for analyzing computations in Answer Set Programming (ASP). Our approach furnishes fine-grained instruments for characterizing operations as well as strategies of ASP solvers. The granularity is detailed enough to capture a variety of propagation and choice methods of algorithms used for ASP solving, also incorporating SAT-based and conflict-driven learning approaches to some extent. This provides us with a uniform setting for identifying and comparing fundamental properties of ASP solving approaches. In particular, we investigate their proof complexities and show that the run-times of best-case computations can vary exponentially between different existing ASP solvers. Apart from providing a framework for comparing ASP solving approaches, our characterizations also contribute to their understanding by pinning down the constitutive atomic operations. Furthermore, our framework is flexible enough to integrate new inference patterns, and so to study their relation to existing ones. To this end, we generalize our approach and provide an extensible basis aiming at a modular incorporation of additional language constructs. This is exemplified by augmenting our basic tableau methods with cardinality constraints and disjunctions. KW - Theory KW - Answer Set Programming KW - tableau calculi KW - proof complexity Y1 - 2013 U6 - https://doi.org/10.1145/2480759.2480767 SN - 1529-3785 VL - 14 IS - 2 PB - Association for Computing Machinery CY - New York ER - TY - JOUR A1 - Mileo, Alessandra A1 - Schaub, Torsten H. A1 - Merico, Davide A1 - Bisiani, Roberto T1 - Knowledge-based multi-criteria optimization to support indoor positioning JF - Annals of mathematics and artificial intelligence N2 - Indoor position estimation constitutes a central task in home-based assisted living environments. Such environments often rely on a heterogeneous collection of low-cost sensors whose diversity and lack of precision has to be compensated by advanced techniques for localization and tracking. Although there are well established quantitative methods in robotics and neighboring fields for addressing these problems, they lack advanced knowledge representation and reasoning capacities. Such capabilities are not only useful in dealing with heterogeneous and incomplete information but moreover they allow for a better inclusion of semantic information and more general homecare and patient-related knowledge. We address this problem and investigate how state-of-the-art localization and tracking methods can be combined with Answer Set Programming, as a popular knowledge representation and reasoning formalism. We report upon a case-study and provide a first experimental evaluation of knowledge-based position estimation both in a simulated as well as in a real setting. KW - Knowledge representation KW - Answer Set Programming KW - Wireless Sensor Networks KW - Localization KW - Tracking Y1 - 2011 U6 - https://doi.org/10.1007/s10472-011-9241-2 SN - 1012-2443 SN - 1573-7470 VL - 62 IS - 3-4 SP - 345 EP - 370 PB - Springer CY - Dordrecht ER - TY - JOUR A1 - Gebser, Martin A1 - Kaminski, Roland A1 - Schaub, Torsten H. T1 - Complex optimization in answer set programming JF - Theory and practice of logic programming N2 - Preference handling and optimization are indispensable means for addressing nontrivial applications in Answer Set Programming (ASP). However, their implementation becomes difficult whenever they bring about a significant increase in computational complexity. As a consequence, existing ASP systems do not offer complex optimization capacities, supporting, for instance, inclusion-based minimization or Pareto efficiency. Rather, such complex criteria are typically addressed by resorting to dedicated modeling techniques, like saturation. Unlike the ease of common ASP modeling, however, these techniques are rather involved and hardly usable by ASP laymen. We address this problem by developing a general implementation technique by means of meta-prpogramming, thus reusing existing ASP systems to capture various forms of qualitative preferences among answer sets. In this way, complex preferences and optimization capacities become readily available for ASP applications. KW - Answer Set Programming KW - Preference Handling KW - Complex optimization KW - Meta-Programming Y1 - 2011 U6 - https://doi.org/10.1017/S1471068411000329 SN - 1471-0684 VL - 11 IS - 3 SP - 821 EP - 839 PB - Cambridge Univ. Press CY - New York ER - TY - JOUR A1 - Gebser, Martin A1 - Maratea, Marco A1 - Ricca, Francesco T1 - The Seventh Answer Set Programming Competition BT - design and results JF - Theory and practice of logic programming N2 - Answer Set Programming (ASP) is a prominent knowledge representation language with roots in logic programming and non-monotonic reasoning. Biennial ASP competitions are organized in order to furnish challenging benchmark collections and assess the advancement of the state of the art in ASP solving. In this paper, we report on the design and results of the Seventh ASP Competition, jointly organized by the University of Calabria (Italy), the University of Genova (Italy), and the University of Potsdam (Germany), in affiliation with the 14th International Conference on Logic Programming and Non-Monotonic Reasoning (LPNMR 2017). KW - Answer Set Programming KW - competition Y1 - 2019 U6 - https://doi.org/10.1017/S1471068419000061 SN - 1471-0684 SN - 1475-3081 VL - 20 IS - 2 SP - 176 EP - 204 PB - Cambridge Univ. Press CY - Cambridge [u.a.] ER - TY - JOUR A1 - Cabalar, Pedro A1 - Fandinno, Jorge A1 - Garea, Javier A1 - Romero, Javier A1 - Schaub, Torsten H. T1 - Eclingo BT - a solver for epistemic logic programs JF - Theory and practice of logic programming N2 - We describe eclingo, a solver for epistemic logic programs under Gelfond 1991 semantics built upon the Answer Set Programming system clingo. The input language of eclingo uses the syntax extension capabilities of clingo to define subjective literals that, as usual in epistemic logic programs, allow for checking the truth of a regular literal in all or in some of the answer sets of a program. The eclingo solving process follows a guess and check strategy. It first generates potential truth values for subjective literals and, in a second step, it checks the obtained result with respect to the cautious and brave consequences of the program. This process is implemented using the multi-shot functionalities of clingo. We have also implemented some optimisations, aiming at reducing the search space and, therefore, increasing eclingo 's efficiency in some scenarios. Finally, we compare the efficiency of eclingo with two state-of-the-art solvers for epistemic logic programs on a pair of benchmark scenarios and show that eclingo generally outperforms their obtained results. KW - Answer Set Programming KW - Epistemic Logic Programs KW - Non-Monotonic KW - Reasoning KW - Conformant Planning Y1 - 2020 U6 - https://doi.org/10.1017/S1471068420000228 SN - 1471-0684 SN - 1475-3081 VL - 20 IS - 6 SP - 834 EP - 847 PB - Cambridge Univ. Press CY - New York ER -