How to Organize Your Article These guideliens provide general information about the individual elements of an article: title and author names; abstract; Introduction; headings; illustrations; tables; lists; extracts; cross-references; footnotes; acknowledgments; biographical sketch and photograph; symbols, abbreviations, and mathematical equations; and references. It is the author's responsibility to obtain written permission from the copyright holder to reprint such illustrations in AI Magazine. In tables, footnotes are preferred to long explanations in the headings or the body of the table; place the footnotes under the table, and begin them with superscript lowercase letters. of Computer Science, Stanford Univ., Stanford, CA Forthcoming Book Clancey, W. J.
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.
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.
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.
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.
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.
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.
This issue of AI Magazine brings together a collection of articles on challenges, mechanisms, and research progress in turn-taking and coordination between humans and machines. The contributing authors work in interrelated fields of spoken dialog systems, intelligent virtual agents, human-computer interaction, human-robot interaction, and semiautonomous collaborative systems and explore core concepts in coordinating speech and actions with virtual agents, robots, and other autonomous systems. Several of the contributors participated in the AAAI Spring Symposium on Turn-Taking and Coordination in Human-Machine Interaction, held in March 2015, and several articles in this issue are extensions of work presented at that symposium. The articles in the collection address key modeling, methodological, and computational challenges in achieving effective coordination with machines, propose solutions that overcome these challenges under sensory, cognitive, and resource restrictions, and illustrate how such solutions can facilitate coordination across diverse and challenging domains.
The 29th International Florida Artificial Intelligence Research Society Conference (FLAIRS-29) was held May 16-18, 2016, at the Hilton Key Largo Resort in Key Largo, Florida, USA. The conference events included invited speakers, special tracks, and presentations of papers, posters, and awards. The conference chair was Bill Eberle from Tennessee Technological University. The special track were coordinated by Vasile Rus from University of Memphis.