AI-powered drones soon will be everywhere, monitoring crowds at major events, checking out traffic patterns on busy roads, surveying disaster sites, and inspecting airplanes. IBM is edging into this airborne safety and maintenance market early, with a deal to bring its Watson internet of things technology to unmanned aircraft systems built by Netherlands-based Aerialtronics. Data captured by high-resolution drone camera lenses will be fed into IBM's visual recognition application programming interfaces (APIs) and services on its Watson cognitive computing and Bluemix cloud-based analytics platforms. The first market for the Aerialtronics drones is expected to be for cell tower maintenance. Instead of sending humans to laboriously climb towers and report back, inspection teams can deploy drones, which quickly gain a 360-degree overview, according to IBM. The visual recognition APIs can then analyze the images captured by the drone to detect problems like damaged cabling or equipment defects.

When writing the screenplay for 1968's 2001, Arthur C. Clarke and Stanley Kubrick were confident that something resembling the sentient, humanlike HAL 9000 computer would be possible by the film's namesake year. That's because the leading AI experts of the time were equally confident. Clarke and Kubrick took the scientific community's predictions to their logical conclusion, that an AI could have not only human charm but human frailty as well: HAL goes mad and starts offing the crew. But HAL was put in an impossible situation, forced to hide critical information from its (his?) coworkers and ordered to complete the mission to Jupiter no matter what. "I'm afraid, Dave," says the robot as it's being dismantled by the surviving astronaut.

Salesforce founder and CEO Marc Benioff is practically a folk hero in the global tech community. He has earned his reputation innovating new technology, questioning the status quo and lobbying for societal change. This week, his company's user conference, Dreamforce, is forecasted to bring as many as 170,000 of his followers to town. Benioff will, in turn, deliver first time experiences to many attendees. Many of the public restrooms at the conference, for example, are gender neutral.

Most small businesses are focused on the nuts and bolts of the IT needed to run their companies, including networks, cloud services and PCs. However, there are many emerging technologies that will likely have large impacts on how businesses are run in the years ahead. The consultancy PwC recently released a report highlighting what it calls eight tech "megatrends" that it thinks will have a large impact on businesses in the next three to seven years. The trends include artificial intelligence, augmented reality, blockchain technology, drones, Internet of Things, robotics, virtual reality and 3D printing. Some of them, like IoT, have been around for years and are already widely used, while others, like virtual reality or the use of blockchain for ledgers and transactions, are rapidly maturing.

A large portion of today's industrial manufacturing relies on At Palo Alto Research Center (PARC), researchers recognized the potential business value to designers as well as manufacturers, and this value proposition was validated during project execution by presenting early prototypes of the software to potential users. The objective of PARC's uFab project hence was to create a software tool that, given just a CAD file and a representation of available machines and tools, generates a process plan in real time. While work in this area had been done in the 1980s under the name computer-aided process planning (CAPP) (Alting and Zhang 1989), none of the approaches that were pursued then resulted in a fully automated solution. A major shortcoming of these systems was their reliance on features, recognizable configurations of faces on a part such as pockets, slots, and holes, in order to represent states and actions. Any advances that This reliance on feature-based representations to these domain-specific needs, implementing are specific to domain-independent hindered their broad applicability the actual search used for planning in PDDL, such as the powerful to parts that could not be easily planning was the easy part.

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. The AAAI-16 Workshops were an excellent forum for exploring emerging approaches and task areas, for bridging the gaps between AI and other fields or between subfields of AI, for elucidating the results of exploratory research, or for critiquing existing approaches. The fifteen workshops held at AAAI-16 were Artificial Intelligence Applied to Assistive Technologies and Smart Environments (WS-16-01), AI, Ethics, and Society (WS-16-02), Artificial Intelligence for Cyber Security (WS-16-03), Artificial Intelligence for Smart Grids and Smart Buildings (WS-16-04), Beyond NP (WS-16-05), Computer Poker and Imperfect Information Games (WS-16-06), Declarative Learning Based Programming (WS-16-07), Expanding the Boundaries of Health Informatics Using AI (WS-16-08), Incentives and Trust in Electronic Communities (WS-16-09), Knowledge Extraction from Text (WS-16-10), Multiagent Interaction without Prior Coordination (WS-16-11), Planning for Hybrid Systems (WS-16-12), Scholarly Big Data: AI Perspectives, Challenges, and Ideas (WS-16-13), Symbiotic Cognitive Systems (WS-16-14), and World Wide Web and Population Health Intelligence (WS-16-15).

IBM Research is engaged in a research program in symbiotic cognitive computing to investigate how to embed cognitive computing in physical spaces. This article proposes 5 key principles of symbiotic cognitive computing.  We describe how these principles are applied in a particular symbiotic cognitive computing environment and in an illustrative application.  

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.

The answer sets for the given program can then be computed by special software systems called answer set solvers, such as DLV, Smodels, or clasp. It is especially relevant to language processing and understanding, learning, reasoning with so called defaults -- statements of the theory update/revision, preferences, diagnosis, form "Normally (typically, as a rule) elements of class description logics, semantic web, multicontext systems, C have property P." We all learn rather early in life and argumentation. Other areas that include that parents normally love their children, citizens are applications of ASP are, for instance, computational normally required to pay taxes, and so forth. We also biology, systems biology, bioinformatics, automatic learn, however, that these rules are not absolute and music composition, assisted living, software engineering, allow various types of exceptions. It is natural to bounded model checking, and robotics. Learning correct ways to decision support systems (Nogueira et al. 2001) (used reason with defaults and their exceptions is necessary by United Space Alliance), automated product configuration for building an agent capable of using such a KB. One (Tiihonen, Soininen, and Sulonen 2003) of the best available solutions to this problem uses (used by Variantum Oy), intelligent call routing the knowledge representation language CR-Prolog (Leone and Ricca 2015) (used by Italia Telecom) and (Balduccini and Gelfond 2003) -- a simple extension configuration and reconfiguration of railway safety of the original ASP language of logic programs with systems (Aschinger et al. 2011) (used by Siemens).

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.