Answer set programming (ASP) has emerged as an approach to declarative problem solving based on the stable model semantics for logic programs. The basic idea is to represent a computational problem by a logic program, formulating constraints in terms of rules, such that its answer sets correspond to problem solutions. To this end, ASP combines an expressive language for high-level modeling with powerful low-level reasoning capacities, provided by off-the-shelf tools. Compact problem representations take advantage of genuine modeling features of ASP, including (first-order) variables, negation by default, and recursion. In this article, we demonstrate the ASP methodology on two example scenarios, illustrating basic as well as advanced modeling and solving concepts. We also discuss mechanisms to represent and implement extended kinds of preferences and optimization. An overview of further available extensions concludes the article.

At first, a problem is expressed as a logic program. ASP's success is largely due to the availability of a rich modeling language (Gebser and Schaub 2016) along with effective systems. Early ASP solvers SModels (Simons, Niemelä, and Soininen 2002) and DLV (Leone et al. 2006) were followed by SAT DLV (Faber, Leone, and Perri 2012) or GrinGo (Gebser ground rules, corresponding to the number of net al. 2011) are based on seminaive database evaluation tuples, over a set of two elements. For more details techniques (Ullman 1988) for avoiding duplicate about complexity of ASP the reader may refer to work during grounding. Grounding is seen as an iterative Dantsin et al. (2001).

What distinguishes ASP from other declarative paradigms, like satisfiability (SAT) or constraint solving (CSP), is its underlying modeling language and the semantics involved. Problems are specified using logic programminglike rules, with some convenient extensions facilitating compact and readable problem descriptions. Sets of such rules, or answer set programs, come with an intuitive, well-defined and, by now, well-accepted semantics. This semantics has its roots in research in knowledge representation, in particular nonmonotonic reasoning, and avoids the pitfalls of earlier attempts such as the procedural semantics of Prolog based on negation as finite failure. This semantics was originally called the stable-model semantics and was defined for normal logic programs only, that is, programs consisting of rules with a single atom in the head and any finite number of atoms, possibly preceded by default negation, not, in the body. Stable models were later generalized to broader classes of programs, where the semantics can no longer be defined in terms of sets of atoms, which is a natural representation of classical models. Instead, it was defined by means of some sets of literals. For this reason the term answer set was adopted as more adequate (although answer sets also have a straightforward interpretation as models, albeit three-valued ones). Over the last decade or so, ASP has evolved into a vibrant and active research area that produced not only theoretical insights, but also highly effective and useful software tools and interesting and promising applications.

Nonparametric estimation of a multivariate density estimation is tackled via a method which combines traditional local smoothing with a form of global smoothing but without imposing a rigid structure. Simulation work delivers encouraging indications on the effectiveness of the method. An application to density-based clustering illustrates a possible usage. Consider estimation of the probability density function f(·) of a continuous random variable in cases when a parametric formulation for f is not considered appropriate. Given a random sample drawn form f, a variety of nonparametric estimation methods are available.

Feature selection methods are usually evaluated by wrapping specific classifiers and datasets in the evaluation process, resulting very often in unfair comparisons between methods. In this work, we develop a theoretical framework that allows obtaining the true feature ordering of two-dimensional sequential forward feature selection methods based on mutual information, which is independent of entropy or mutual information estimation methods, classifiers, or datasets, and leads to an undoubtful comparison of the methods. Moreover, the theoretical framework unveils problems intrinsic to some methods that are otherwise difficult to detect, namely inconsistencies in the construction of the objective function used to select the candidate features, due to various types of indeterminations and to the possibility of the entropy of continuous random variables taking null and negative values.

We consider the online version of the isotonic regression problem. Given a set of linearly ordered points (e.g., on the real line), the learner must predict labels sequentially at adversarially chosen positions and is evaluated by her total squared loss compared against the best isotonic (non-decreasing) function in hindsight. We survey several standard online learning algorithms and show that none of them achieve the optimal regret exponent; in fact, most of them (including Online Gradient Descent, Follow the Leader and Exponential Weights) incur linear regret. We then prove that the Exponential Weights algorithm played over a covering net of isotonic functions has a regret bounded by $O\big(T^{1/3} \log^{2/3}(T)\big)$ and present a matching $\Omega(T^{1/3})$ lower bound on regret. We provide a computationally efficient version of this algorithm. We also analyze the noise-free case, in which the revealed labels are isotonic, and show that the bound can be improved to $O(\log T)$ or even to $O(1)$ (when the labels are revealed in isotonic order). Finally, we extend the analysis beyond squared loss and give bounds for entropic loss and absolute loss.

In some use cases, it is impossible for humans to replicate the performance of artificial intelligence. But businesses will need a lot of data for AI systems to be effective. Maybe you've seen an artificial intelligence (AI) system like Watson at work on "Jeopardy!" or have heard of its successes in medical diagnoses or other fields. Maybe you've only heard about other similar systems working through incredibly complex and large sets of data to produce results that even non-experts can understand, through visualizations or natural language. Either way, AI systems are impressing many on their march toward becoming essential business processes.

Alan Turing was well ahead of his time in predicting the capabilities of AI. Computers are writing novels -- and getting better at it. It probably won't help your "robots are stealing our jobs" fear. And it casts doubt on the idea that creative professions are safer than the administrative or processing professions. Right now, in a play on a human literary contest, around a hundred people are writing computer programs that will write texts for them, the Verge says.

Edward Snowden appears from Russia to people in Stuttgart, Germany, in 2014. SAN FRANCISCO -- The Edward Snowden effect just made an encore, thanks to a report Yahoo has been scanning incoming emails on behalf of U.S. intelligence officials. And it's likely to take a few more bows. U.S. technology companies, already in defensive mode thanks to the former NSA contractor's revelations of a mass government surveillance program in 2013, are even more data-hungry today and therefore more on edge. From big data to the cloud to artificial intelligence you can talk to in your kitchen, the tech world is busy spinning the straw of information it gathers about users into gold.

In a new initiative, UK-based PC systems maker and retailer Scan 3XS is providing remote access to Nvidia DGX-1 Deep Learning Supercomputers. To allow customers to decide whether the significant investment involved in acquiring a DGX-1 is for them, Scan has begun a DGX-1 Proof of Concept program to allow end users to run custom data processing tests on one of its own deep learning machines. As a reminder, the Nvidia DGX-1 is headlined as'The World's First AI Supercomputer' by Nvidia. This compact 3U 19in rackmount purpose-built system, made for deep learning and AI accelerated analytics, is said to provide performance equivalent to 250 conventional servers. If you have access to a DGX-1 you get fully integrated hardware, deep learning software, development tools, and the ability to run popular accelerated analytics applications.