But if Google's latest report on self-driving car accidents is any indicator, we have a long way to go before our robot overlords will save us. "The Google AV sustained substantial damage to its front and rear passenger doors. The other vehicle sustained significant damage to its front end. You can see the damage Google's car took in the tweet below:

In this post, I'll introduce you to the Julia programming language and a couple long-term projects of mine: Plots for easily building complex data visualizations, and JuliaML for machine learning and AI. Easily create strongly-typed custom data manipulators. "User recipes" and "type recipes" can be defined on custom types to enable them to be "plotted" just like anything else. We believe that Julia has the potential to change the way researchers approach science, enabling algorithm designers to truly "think outside the box" (because of the difficulty of implementing non-conventional approaches in other languages).

MIT's Computer Science and Artificial Intelligence Lab (CSAIL) has led one of the most notable efforts in this regard, developing a system called AI2, an adaptive cybersecurity platform that uses machine learning and the assistance of expert analysts to adapt and improve over time. The system uses near-real-time analytics to identify known security threats, stored data analytics to compare samples against historical data and big data analytics to identify evolving threats through anonymized datasets gathered from a vast number of clients. Combining this capability with the data already being gathered by IBM's threat intelligence platform, X-Force Exchange, the company wants to address the shortage of talent in the industry by raising Watson's level of efficiency to that of an expert assistant and help reduce the rate of false positives. This technique gives the cybersecurity firm the unique ability to monitor billions of results on a daily basis, identify and alert about the publication of potentially brand-damaging information and proactively detect and prevent attacks and data loss before they happen.

In this article, we give an overview of the answer set programming paradigm, explain its strengths, and illustrate its main features in terms of examples and an application problem. In this article, we give an overview of the answer set programming paradigm, explain its strengths, and illustrate its main features in terms of examples and an application problem.

The Workshop Program of the Association for the Advancement of Artificial Intelligence's Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) was held at the beginning of the conference, February 12-13, 2016. Workshop participants met and discussed issues with a selected focus -- providing an informal setting for active exchange among researchers, developers and users on topics of current interest. To foster interaction and exchange of ideas, the workshops were kept small, with 25-65 participants. Attendance was sometimes limited to active participants only, but most workshops also allowed general registration by other interested individuals.

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. 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.

Answer set programming is a declarative programming paradigm based on the answer set semantics of logic programs. This introductory article provides the mathematical background for the discussion of answer set programming in other contributions to this special issue.

Answer set programming is a declarative problem solving paradigm that rests upon a workflow involving modeling, grounding, and solving. While the former is described by Gebser and Schaub (2016), we focus here on key issues in grounding, or how to systematically replace object variables by ground terms in a effective way, and solving, or how to compute the answer sets of a propositional logic program obtained by grounding.

The goal of this article is threefold. First, we trace the history of the development of answer set solvers, by accounting for more than a dozen of them. Second, we discuss development tools and environments that facilitate the use of answer set programming technology in practical applications. Last, we present the evolution of the answer set programming competitions, prime venues for tracking advances in answer set solving technology.

This article reports on the first international Competition of Distributed and Multiagent Planners (CoDMAP). The competition focused on cooperative domain-independent planners compatible with a minimal multiagent extension of the classical planning model. The motivations for the competition were manifold: to standardize the problem description language with a common set of benchmarks, to promote development of multiagent planners both inside and outside of the multiagent research community, and to serve as a prototype for future multiagent planning competitions. The article provides an overview of cooperative multiagent planning, describes a novel variant of standardized input language for encoding mutliagent planning problems and summarizes the key points of organization, competing planners and results of the competition.