Traditionally focused on good old-fashioned AI and robotics, the Spanish AI community holds a vigorous computational intelligence substrate. Neuromorphic, evolutionary, or fuzzylike systems have been developed by many research groups in the Spanish computer sciences. It is no surprise, then, that these nature-grounded efforts start to emerge, enriching the AI catalogue of research projects and publications and, eventually, leading to new directions of basic or applied research. In this article, we review the contribution of Melomics in computational creativity.
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This issue features expanded versions of articles selected from the 2016 AAAI Conference on Innovative Applications of Artificial Intelligence held in Phoenix, Arizona. We present a selection of three articles that describe deployed applications, two articles that discuss work on emerging applications, and an article based on the 2016 Robert S. Engelmore Memorial Lecture.
However, the selection of test cases in regression testing is challenging as the time available for testing is limited and some selection criteria must be respected. This problem, coined as Test Suite Reduction (TSR), is usually addressed by validation engineers through manual analysis or by using approximation techniques. By associating each test case a cost-value aggregating distinct criteria, such as execution time, priority or importance due to the error-proneness of each test case, we propose several constraint optimization models to find a subset of test cases covering all the test requirements and optimizing the overall cost of selected test cases. Our overall goal is to develop a constraint-based approach of test suite reduction that can be deployed to test a complete product line of conferencing systems in continuous delivery mode.
Sadilek, Adam (University of Rochester) | Kautz, Henry (University of Rochester) | DiPrete, Lauren (Southern Nevada Health District) | Labus, Brian (Southern Nevada Health District, Las Vegas, Nevada) | Portman, Eric (University of Rochester) | Teitel, Jack (University of Rochester) | Silenzio, Vincent (University of Nevada Las Vegas,)
CDC has even identified food safety as one of seven "winnable battles"; however, progress to date has been limited. We show that adaptive inspection process is 64 percent more effective at identifying problematic venues than the current state of the art. If fully deployed, our approach could prevent over 9,000 cases of foodborne illness and 557 hospitalizations annually in Las Vegas alone. Additionally, adaptive inspections result in unexpected benefits, including the identification of venues lacking permits, contagious kitchen staff, and fewer customer complaints filed with the Las Vegas health department.
The available tools and support for building planning and scheduling systems and applications have been steadily improving for decades. At the same time, the scope, scale, and complexity of the problems to be addressed has been increasing. In this column, I discuss several different scheduling applications developed over the past 25 years, and then describe the tools and techniques used in addressing these problems, showing how improved tools simplified (and in some cases enabled) the solution of problems of increasing difficulty.
We review the 2016 International Competition on Knowledge Engineering for Planning and Scheduling (ICKEPS), the fifth in a series of competitions started in 2005. ICKEPS series focuses on promoting the importance of knowledge engineering methods and tools for automated Planning and Scheduling systems.
AI applications have been deployed and used for industrial, government, and consumer purposes for many years. Over the years, the breadth of applications has expanded many times over and AI systems have become more commonplace. Indeed, AI has recently become a focal point in the industrial and consumer consciousness. This article focuses on changes in the world of computing over the last three decades that made building AI applications more feasible.
Rychtyckyj, Nestor (AAAI) | Raman, Venkatesh (Ford Motor Company) | Sankaranarayanan, Baskaran (Indian Institute of Technology Madras) | Kuma, P. Sreenivasa (Indian Institute of Technology Madras) | Khemani, Deepak (Indian Institute of Technology Madras)
For over twenty-five years Ford Motor Company has been utilizing an AI-based system to manage process planning for vehicle assembly at its assembly plants around the world. The scope of the AI system, known originally as the Direct Labor Management System and now as the Global Study Process Allocation System (GSPAS), has increased over the years to include additional functionality on Ergonomics and Powertrain Assembly (Engines and Transmission plants). The knowledge about Ford's manufacturing processes is contained in an ontology originally developed using the KL-ONE representation language and methodology. In this article, we will discuss the process by which we re-engineered the existing GSPAS KL-ONE ontology and deployed semantic web technology in our application.