In addition, we illustrate the potential of ASP including molecular biology (Gebser et computational hardness of our application problem al. 2010a, 2010b), decision support system for space by explaining its connection to the NPcomplete shuttle controllers (Balduccini, Gelfond, and decision problem Exact-3-SAT.

Its main ideas are described in the article by Janhunen and Niemelä (2016) and in other contributions to this special issue. In this introductory article my goal is to discuss the concept of an answer set, or stable model, which defines the semantics of ASP languages. The answer sets of a logic program are sets of atomic formulas without variables ("ground atoms"), and they were introduced in the course of research on the semantics of negation in Prolog. For this reason, I will start with examples illustrating the relationship between answer sets and Prolog and the relationship between answer set solvers and Prolog systems. Then I will review the mathematical definition of an answer set and discuss some extensions of the basic language of ASP.

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.

Arthur C. Clarke famously stated that "any sufficiently advanced technology is indistinguishable from magic." No current technology embodies this statement more than neural networks and deep learning. And like any good magic it not only dazzles and inspires but also puts fear into people's hearts. One known property of artificial neural networks (ANNs) is that they are universal function approximators. This means that any mathematical function can be represented by a neural network.

Although technology seems to be everywhere, we continue to fill in the voids. Existing technologies evolve and change at a higher pace every year, making it challenging for some professionals to adjust.We have reached a point where some are having difficulty coping with game-changing technologies that are presumably contributing to progress. Such progress can be difficult to perceive if you are not directly benefiting from it. In my previous article, I discussed the ability of social robotics to make a positive impact on our society. This article provides an optimistic vision of the opportunity for this technology to include the general public in its development.

Artificial Intelligence (AI) is fast becoming a buzzword in the technology industry. If you are following up technology news, you will get to read eye catching AI news almost every day. Large companies are realising that they will be at a loss if they remain behind in acquiring competence in AI. Prominent technology companies like Google, Facebook, Microsoft and IBM are investing billions of dollars in developing AI teams and technologies. Venture capitalists are investing in a number of start-ups promising to make AI products.

This paper examines the performance of a number of AI agents on the games included in the General Video Game Playing Competition. Through analyzing these results, the paper seeks to provide insight into the strengths and weaknesses of the current generation of video game playing algorithms. The paper also provides an analysis of the given games in terms of inherent features which define the different games. Finally, the game features are matched with AI agents, based on performance, in order to demonstrate a plausible case for algorithm portfolios as a general video game playing technique.

The Terminator isn't arriving anytime soon, but concern is growing that artificial intelligence is already so pervasive in society--and getting more so all the time--that there needs to be more focus on how it's being used and potentially misused (even if by accident). Aside from futuristic killer robots, there are already real dangers ranging from faulty autonomous cars to algorithms used in hiring or recruiting that have an inadvertent bias against women or ethnic groups. The giants of artificial intelligence, especially as it affects consumers and businesses, have just joined together to form a nonprofit called the Partnership on AI, with founding members Amazon, DeepMind/Google, Facebook, IBM, and Microsoft. It's the latest effort to keep a collective eye on how AI is developed and used.OpenAI, founded in December 2015, has a similar goal of conducing research and conferences to promote responsible use of AI. "This partnership will provide consumer and industrial users of cognitive systems a vital voice in the advancement of the defining technology of this century," reads part of a statement from IBM's AI ethics researcher, Francesca Rossi. The Partnership on AI announcementlays out an ambitious agenda for research to be conducted or funded by members, in partnership with academics, user group advocates, and industry experts.

Convergent TechFocus is a blog look at technology developments: IT, Audio Visual, Unified Communications and Collaboration (UCC), video conferencing, cloud and software-defined, mobility/BYOD, Internet of Things, cybersecurity and more. This blog focus is on Microsoft Ignite, which took place this week in Atlanta, GA. Two major technology categories discussed here: Skype for Business UC and videoconferencing solutions, and Artificial Intelligence (AI) as an emerging trend in enterprise business. Microsoft Ignite kicked off on Monday with exceptional keynotes throughout the day. In his opening Keynote, Scott Guthrie, Executive Vice President, Microsoft focused on how technology enables digital transformation and demonstrated new technologies.

Summary: What comes next after Deep Learning? How do we get to Artificial General Intelligence? Adversarial Machine Learning is an emerging space that points to that direction and shows that AGI is closer than we think. Deep Learning, Convolutional Neural Nets (CNNs) have given us dramatic improvements in image, speech, and text recognition over the last two years. They suffer from the flaw however that they can be easily fooled by the introduction of even small amounts of noise, random or intentional.