ion is a well-known approach to simplify a complex problem by over-approximating it with a deliberate loss of information. It was not considered so far in Answer Set programming (ASP), a convenient tool for problem so...
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Search-optimization problems are plentiful in scientific and engineering domains. Artificial intelligence has long contributed to the development of search algorithms and declarative programming languages geared towar...
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Parameter learning is a crucial task in the field of Statistical Relational Artificial Intelligence: given a probabilistic logic program and a set of observations in the form of interpretations, the goal is to learn t...
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programming environments have evolved from purely text based to using graphical user interfaces, and now we see a move towards web based interfaces, such as Jupyter. Web based interfaces allow for the creation of inte...
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This paper describes how XSB combines top-down and bottom-up computation through the mechanisms of variant tabling and subsumptive tabling with abstraction, respectively. It is well known that top-down evaluation of l...
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The repeated execution of reasoning tasks is desirable in many applicative scenarios, such as stream reasoning and event processing. When using answer set programming in such contexts, one can avoid the iterative gene...
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In this paper, operating data of lathes is analyzed with regard to the specifications of the ISO 13849-1. The Weibull analysis is based on machine running times of 1519 multi spindle automatic lathes. The analyzed saf...
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作者:
Bistarelli, S.Gennari, R.Rossi, F.Università di Pisa
Dipartimento di Informatica Corso Italia 40 Pisa56125 Italy ILLC
Institute of Logic Language and Computation University of Amsterdam N. Doelenstraat 15 Amsterdam1012 CP Netherlands Università di Padova
Dipartimento di Matematica Pura ed Applicata Via Belzoni 7 Padova35131 Italy
Soft constraints based on semirings are a generalization of classical constraints, where tuples of variables’ values in each soft constraint are uniquely associated to elements from an algebraic structure called semi...
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The airline industry has evolved significantly in recent years, and the emphasis on minimizing the cost and risk of operational disruptions has been shifted, in part, to satisfaction and fairness in the assignment of ...
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The airline industry has evolved significantly in recent years, and the emphasis on minimizing the cost and risk of operational disruptions has been shifted, in part, to satisfaction and fairness in the assignment of crew members. The crew rostering is the phase of flight schedules in which crew members are assigned to rosters based on their preferences and fairness in terms of balanced flight times between crew members. The satisfaction of the crew members depends on how much their preferences are met in the rosters. In this study, the crew rostering problem with cabin members' preferences is formulated as a mixed-integer goal programming model with three objective functions: total satisfaction score, deviation from average satisfaction score, and deviation from average flight hours. The importance of these objectives is imprecise from the flight management system perspective, so this study proposes a fuzzy goal programming model to solve the crew rostering problem with the preferences. The paper is organized based on a case study gathered from an airline in Kuwait. The results showed that incorporating pilot preferences leads to better rosters in terms of pilot satisfaction. However, increasing the number of pilots in consideration or the flight evaluation percentage does not lead to better rosters. The case study also sheds light on the links between the crew roster and sustainable operations.
Probability features increasingly often in software and hardware systems: it is used in distributed coordination and routing problems, to model fault-tolerances and performance, and to provide adaptive resource manage...
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Probability features increasingly often in software and hardware systems: it is used in distributed coordination and routing problems, to model fault-tolerances and performance, and to provide adaptive resource management strategies. Probabilistic model checking is an automatic procedure for establishing if a desired property holds in a probabilistic specifications such as "leader election is eventually resolved with probability 1", "the chance of shutdown occurring is at most 0.01%", and "the probability that a message will be delivered within 30ms is at least 0.75". A probabilistic model checker calculates the probability of a given temporal logic property being satisfied, as opposed to validity. In contrast to conventional model checkers, which rely on reachability analysis of the underlying transition system graph, probabilistic model checking additionally involves numerical solutions of linear equations and linear programming problems. This paper reports our experience with implementing PRISM (***//spl sim/dxp/prism), a probabilistic symbolic model checker, demonstrates its usefulness in analyzing real-world probabilistic protocols, and outlines future challenges for this research direction.
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