83,525 research outputs found
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
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: extending
state-of-the-art techniques and ASP solvers, or 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
An Investigation into Game Based Learning Using High Level Programming Languages
Game-related education within mobile learning spheres is a matter of great debate for university students across the globe. It is the case that programming languages often pose a sizeable challenge for university students. This research paper aims to develop a game based learning platform “iPlayCode”, designed to offer a new and exciting method of learning programming language. Xcode 5.0.2 was used to develop the game by using the cocos2d-x development tool and the Adobe Photoshop graphic design tool. In addition, iOS 7.0.3 (11B508) Simulator was used to test the application and the application was deployed in different models of mobile devices such as the iPhone and iPad. The application outcomes are presented by a mobile game that teaches programming languages in an easy, attractive and effective way
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