Authors:Wafa Jguirim; Wady Naanaa; Martin C. Cooper Pages: 1 - 20 Abstract: Finding a solution to a constraint satisfaction problem (CSP) is known to be an NP-hard task. Considerable effort has been spent on identifying tractable classes of CSP, in other words, classes of constraint satisfaction problems for which there are polynomial time recognition and resolution algorithms. In this article, we present a relational tractable class of binary CSP. Our key contribution is a new ternary operation that we name mjx. We first characterize mjx-closed relations which leads to an optimal algorithm to recognize such relations. To reduce space and time complexity, we define a new storage technique for these relations which reduces the complexity of establishing a form of strong directional path consistency, the consistency level that solves all instances of the proposed class (and, indeed, of all relational classes closed under a majority polymorphism). PubDate: 2018-05-01 DOI: 10.1007/s10472-017-9566-6 Issue No:Vol. 83, No. 1 (2018)

Authors:Cynthia Maushagen; Jörg Rothe Pages: 219 - 244 Abstract: Control by partition refers to situations where an election chair seeks to influence the outcome of an election by partitioning either the candidates or the voters into two groups, thus creating two first-round subelections that determine who will take part in a final round. The model of partition-of-voters control attacks is remotely related to “gerrymandering” (maliciously resizing election districts). While the complexity of control by partition has been studied thoroughly for many voting systems, there are no such results known for the important veto voting system. We settle the complexity of control by partition for veto in a broad variety of models. In addition, by giving a counterexample we observe that a reduction from the literature (Chen et al. 2015) showing the parameterized complexity of control by adding candidates to plurality elections, parameterized by the number of voters, is technically flawed, and we show how this reduction can be adapted to make it correct. PubDate: 2018-04-01 DOI: 10.1007/s10472-017-9565-7 Issue No:Vol. 82, No. 4 (2018)

Authors:Jonathan Pieper Abstract: This paper describes the team BusyBeaver, that participated in and won the Multi-Agent Programming Contest 2017. Its strategy is based on dividing agents into three static groups modeling the work chain of buying, assembling and delivering items. The team is coordinated by a centralized agent doing most of the high-level planning, usually using greedy algorithms and specialized heuristics. There is a heavy focus on proactively buying and assembling some items, in order to quickly complete upcoming jobs. PubDate: 2018-06-09 DOI: 10.1007/s10472-018-9589-7

Authors:Z. Kovács; T. Recio; C. Sólyom-Gecse Abstract: We present an algorithm to help converting expressions having non-negative quantities (like distances) in Euclidean geometry theorems to be usable in a complex algebraic geometry prover. The algorithm helps in refining the output of an existing prover, therefore it supports immediate deployment in high level prover systems. We prove that the algorithm may take doubly exponential time to produce the output in polynomial form, but in many cases it is still computable and useful. PubDate: 2018-06-04 DOI: 10.1007/s10472-018-9590-1

Authors:Evangelos I. Sarmas Abstract: This paper presents the workings of the Flisvos-2017 multi-agent system that participated in the Multi-Agent Programming Contest MAPC 2017 of Clausthal TU. PubDate: 2018-06-02 DOI: 10.1007/s10472-018-9587-9

Authors:Jørgen Villadsen; Oliver Fleckenstein; Helge Hatteland; John Bruntse Larsen Abstract: This paper presents the overall strategy utilized by Jason-DTU to achieve a shared second place in the annual Multi-Agent Programming Contest. It provides an overview of the implementation details considering perception, task handling, agent logic and more. The paper analyzes the team’s results in each match, and is summarized by evaluating strengths and weaknesses of the proposed multi-agent system. PubDate: 2018-06-01 DOI: 10.1007/s10472-018-9588-8

Authors:Christopher-Eyk Hrabia; Patrick Marvin Lehmann; Nabil Battjbuer; Axel Hessler; Sahin Albayrak Abstract: In the Multi-Agent Programming Contest 2017 the TUBDAI team of the Technische Universität Berlin is using the complex multi-agent scenario to evaluate the application of two frameworks from the field (multi-)robot systems. In particular the task-level decision-making and planning framework ROS Hybrid Behaviour Planner (RHBP) is used to implement the execution and decision-making for single agents. The RHBP framework builds on top of the framework Robot Operating System (ROS) that is used to implement the communication and scenario specific coordination strategy of the agents. The united team for the official contest is formed by volunteering students from a project course and their supervisors. PubDate: 2018-05-07 DOI: 10.1007/s10472-018-9586-x

Authors:Imen Zghidi; Brahim Hnich; Abdelwaheb Rebai Abstract: The original version of this article unfortunately contained a mistake. The name “Abdelwahad Rebaii” should be corrected to “Abdelwaheb Rebai”. PubDate: 2018-05-05 DOI: 10.1007/s10472-018-9583-0

Authors:Lorenz Demey; Hans Smessaert Abstract: Aristotelian diagrams are used extensively in contemporary research in artificial intelligence. The present paper investigates the geometric and cognitive differences between two types of Aristotelian diagrams for the Boolean algebra \(\mathbb {B}_{4}\) . Within the class of 3D visualizations, the main geometric distinction is that between the cube-based diagrams (such as the rhombic dodecahedron) and the tetrahedron-based diagrams. Geometric properties such as collinearity, central symmetry and distance are examined from a cognitive perspective, focusing on diagram design principles such as congruence/isomorphism and apprehension. The cognitive effectiveness of the different visualizations is compared for the representation of implication versus opposition relations, and for subdiagram embeddings. PubDate: 2018-05-02 DOI: 10.1007/s10472-018-9585-y

Authors:Rafael C. Cardoso; Tabajara Krausburg; Túlio Baségio; Débora C. Engelmann; Jomi F. Hübner; Rafael H. Bordini Abstract: The Multi-Agent Programming Contest in 2017 expanded upon the Agents in the City scenario used in the 2016 edition of the contest. In the Agents in the City contest, two teams compete to accomplish logistic tasks in simulations using realistic city maps from OpenStreetMap. The new version of the scenario shifted emphasis to include jobs that require assembled items, new types of facilities, and larger teams, resulting in a significantly more complex scenario than before. In this paper, we describe the strategies used by our team, highlighting our adaptations and any new additions from our participation in the previous year. One such new addition, is that now we have fully explored the use of all three programming dimensions (agent, environment, and organization) found in JaCaMo, the multi-agent system development platform that we used to implement our team. We also provide some discussion and analysis on what we believe were some of our most influential matches. PubDate: 2018-04-30 DOI: 10.1007/s10472-018-9584-z

Authors:Daniel Berend; Amnon Meisels; Or Peri Abstract: Distributed Constraint Optimization Problems (DCOPs) are widely used in Multi-Agent Systems for coordination and scheduling. The present paper proposes a heuristic algorithm that uses probabilistic assessment of the optimal solution in order to quickly find a solution that is not far from the optimal one. The heuristic assessment uses two passes by the agents to produce a high-quality solution. Extensive performance evaluation demonstrates that the solution of the proposed probabilistic assessment algorithm is indeed very close to the optimum, on smaller problems where this could be measured. In larger set-ups, the quality of the solution is demonstrated relatively to standard incomplete search algorithms. PubDate: 2018-04-26 DOI: 10.1007/s10472-018-9582-1

Authors:Philipp Czerner; Jonathan Pieper Abstract: In this paper we describe our participation in the 2017 edition of the Multi-Agent Programming Contest as team ‘lampe’. Our strategy was implemented in C++; it uses a centralised organisation of agents and evaluates different strategies based on an internal simulation of the future game state. Strategies are generated using handwritten heuristics in a randomised fashion, also relying on the internal simulation. PubDate: 2018-04-10 DOI: 10.1007/s10472-018-9581-2

Authors:Régis Riveret; Pietro Baroni; Yang Gao; Guido Governatori; Antonino Rotolo; Giovanni Sartor Abstract: The combination of argumentation and probability paves the way to new accounts of qualitative and quantitative uncertainty, thereby offering new theoretical and applicative opportunities. Due to a variety of interests, probabilistic argumentation is approached in the literature with different frameworks, pertaining to structured and abstract argumentation, and with respect to diverse types of uncertainty, in particular the uncertainty on the credibility of the premises, the uncertainty about which arguments to consider, and the uncertainty on the acceptance status of arguments or statements. Towards a general framework for probabilistic argumentation, we investigate a labelling-oriented framework encompassing a basic setting for rule-based argumentation and its (semi-) abstract account, along with diverse types of uncertainty. Our framework provides a systematic treatment of various kinds of uncertainty and of their relationships and allows us to back or question assertions from the literature. PubDate: 2018-03-20 DOI: 10.1007/s10472-018-9574-1

Authors:S. D. Prestwich; S. A. Tarim; I. Ozkan Abstract: Probabilistic methods for causal discovery are based on the detection of patterns of correlation between variables. They are based on statistical theory and have revolutionised the study of causality. However, when correlation itself is unreliable, so are probabilistic methods: unusual data can lead to spurious causal links, while nonmonotonic functional relationships between variables can prevent the detection of causal links. We describe a new heuristic method for inferring causality between two continuous variables, based on randomness and unimodality tests and making few assumptions about the data. We evaluate the method against probabilistic and additive noise algorithms on real and artificial datasets, and show that it performs competitively. PubDate: 2018-03-14 DOI: 10.1007/s10472-018-9575-0

Authors:Luís Cruz-Filipe; Graça Gaspar; Isabel Nunes; Peter Schneider-Kamp Abstract: In the database world, integrity constraints are essential to guarantee database integrity. The related problem of database repair deals with finding the best way to change a database so that it satisfies its integrity constraints. These two topics have been studied intensively since the 1980s. The formalism of active integrity constraints, proposed in 2004, aims at addressing them jointly, by providing a syntax whereby a particular subclass of integrity constraints can be specified together with preferred ways to repair inconsistency. In the last decade, several authors have proposed adaptations of the notion of integrity constraints to other reasoning frameworks than relational databases. In this article, we extend this line of work in two ways. First, we target multi-context systems, a general-purpose framework for combining heterogeneous reasoning systems, able to model most other reasoning frameworks, as we demonstrate. Second, we extend the notions of active integrity constraints and grounded repairs to this generalized setting. This way of including repair suggestions inside integrity constraints, subject to a validity check, enables us to define simple iterative algorithms to find all possible grounded repairs for an inconsistent multi-context system, avoiding the need to solve complex or undecidable problems. PubDate: 2018-03-14 DOI: 10.1007/s10472-018-9577-y

Authors:Ryan Berryhill; Andreas Veneris Abstract: In the modern hardware design cycle, correcting the design when verification reveals a state to be erroneously unreachable can be a time-consuming manual process. Recently-developed algorithms aid the engineer in finding the root cause of the failure in these cases. However, they exhaustively examine every design location to determine a set of possible root causes, potentially requiring substantial runtime. This work develops a novel approach that is applicable to practical diagnosis problems. In contrast to previous approaches, it considers only a portion of the design locations but still finds the complete solution set to the problem. The presented approach proceeds through a series of iterations, each considering a strategically-chosen subset of the design locations (a suspect set) to determine if they are root causes. The results of each iteration inform the choice of suspect set for the next iteration. By choosing the first iteration’s suspect set appropriately, the algorithm is able to find the complete solution set to the problem. Empirical results on industrial designs and standard benchmark designs demonstrate a 15x speedup compared to the previous approach, while considering only 18.7% of the design locations as suspects. PubDate: 2018-03-13 DOI: 10.1007/s10472-018-9578-x

Authors:Jan Triska; Vilem Vychodil Abstract: We deal with dependencies in object-attribute data which is recorded at separate points in time. The data is formalized by finitely many tables encoding the relationship between objects and attributes and each table can be seen as single formal context observed at separate point in time. Given such data, we are interested in concise ways of characterizing all if-then dependencies between attributes that hold in the data and are preserved in all time points. In order to formalize the dependencies, we use particular if-then rules called attribute implications annotated by time points which can be seen as particular formulas of linear temporal logic. We introduce non-redundant bases of dependencies from data as non-redundant sets entailing exactly all dependencies that hold in the data. In addition, we investigate minimality of bases as stronger form of non-redundancy. For given data, we present description of minimal bases using the notion of pseudo-intents generalized in the temporal setting. The present paper is a continuation of our previous paper on entailment of attribute implications annotated by time points. PubDate: 2018-03-06 DOI: 10.1007/s10472-018-9576-z

Authors:Mehrnoosh Sadrzadeh; Dimitri Kartsaklis; Esma Balkır Abstract: Distributional semantic models provide vector representations for words by gathering co-occurrence frequencies from corpora of text. Compositional distributional models extend these from words to phrases and sentences. In categorical compositional distributional semantics, phrase and sentence representations are functions of their grammatical structure and representations of the words therein. In this setting, grammatical structures are formalised by morphisms of a compact closed category and meanings of words are formalised by objects of the same category. These can be instantiated in the form of vectors or density matrices. This paper concerns the applications of this model to phrase and sentence level entailment. We argue that entropy-based distances of vectors and density matrices provide a good candidate to measure word-level entailment, show the advantage of density matrices over vectors for word level entailments, and prove that these distances extend compositionally from words to phrases and sentences. We exemplify our theoretical constructions on real data and a toy entailment dataset and provide preliminary experimental evidence. PubDate: 2018-02-15 DOI: 10.1007/s10472-017-9570-x