If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The concatenation of these reports forms the body of this article. Abstract Based on the experience in manufacturing production scheduling problems which the AI community has amassed over the last ten years, a workshop was held to provide a forum for discussion of the issues encountered in the design of AIbased scheduling systems. Several topics were addressed including: the relative virtues of expert system, deep method, and interactive approaches, the balance between predictive and reactive components in a scheduling system, the maintenance of convenient scheduling descriptions, the application of the ideas of chaos theory to scheduling, the state of the art in schedulers which learn, and the practicality and desirability of a set of benchmark scheduling problems. This article expands on these issues, abstracts the papers which were presented, and summarizes the lengthy discussions that took place. Since its first formal business meeting in August of 1988, the American Association for Artificial Intelligence Special Interest Group in Manufacturing (SIGMAN) has held a number of workshops, three of which have been concerned with the application of AI techniques to the problem of manufacturing scheduling.
Intelligent Scheduling is a system-oriented book on scheduling systems. Each chapter describes a scheduling system in terms of the particular scheduling problems being addressed, design assumptions, and the overall paradigm being used. The book is divided into two sections: (1) scheduling methodologies and (2) application case studies. The methodology chapters focus on research systems and scheduling techniques. The application chapters focus on fielded embedded scheduling systems and describe difficulties and lessons learned.
The symposium took place in July 2009 in Lake Arrowhead, California. Consequently, ARA techniques have been studied in various subfields in AI and related disciplines and have been used in various settings including automated reasoning, cognitive modeling, constraint programming, design, diagnosis, machine learning, model-based reasoning, planning, reasoning, scheduling, search, theorem proving, and intelligent tutoring. The considerable interest in ARA techniques and the great diversity of the researchers involved had led to work on ARA being presented at many different venues. Consequently, there was a need to have a single forum where researchers of different backgrounds and disciplines could discuss their work on ARA. As a result, the Symposium on Abstraction, Reformulation, and Approximation (SARA) was established in 1994 after a series of workshops in 1988, 1990, and 1992.
We report on the staging of the third competition on knowledge engineering for AI planning and scheduling systems, held during ICAPS-09 at Thessaloniki, Greece, in September 2009. We give an overview of how the competition has developed since its first run in 2005 and its relationship with the AI planning field. This run of the competition focused on translators that, when input with some formal description in an application-area-specific language, output solver-ready domain models. Despite a fairly narrow focus within knowledge engineering, seven teams took part in what turned out to be a very interesting and successful competition. The International Competition on Knowledge Engineering for Planning and Scheduling (ICKEPS) has been running since 2005 as a biannual event promoting the development and importance of the use of knowledge engineering methods and techniques within planning and scheduling.
The system ensures a high standard of quality in customer service, airport safety, and use of stand resources. This article describes our experience in developing an AI system using standard off-the-shelf software components. Although there were some initial hitches when the new airport opened on 6 July 1998, operations quickly returned to normal within a week's time. Within a month, operational statistics surpassed those of the old airport--80 percent of all flights were on time or within 15 minutes of schedule, all passengers cleared immigration within 15 minutes, and average baggage waiting time was only 10 minutes. During the 1998 Christmas holiday, HKIA serviced about 100,000 passengers daily and maintained equally high service standards.
The staff scheduling problem is a critical problem in the call center (or, more generally, customer contact center) industry. Even the simplest variations of this problem are known to be NPcomplete (Garey and Johnson 1978). Although staff scheduling has long been an important operations research problem, scheduling has recently become an important component of an emerging class of business software applications known as workforce management software. The need for effective workforce management systems has been driven primarily by the recent, rapid growth of the call center--customer contact center industry, in which efficient deployment of human resources is of crucial, strategic importance. Traditionally, in this industry, staff scheduling has been performed using ad hoc methods and operations research techniques (Cleveland and Mayben 1997).
An expert system used in the control room of this blast furnace controls fluctuations in furnace temperature, thereby saving significant amounts of energy and costs. Representatives of universities and businesses were chosen by the Japan Technology Evaluation Center to investigate the state of the technology in Japan relative to the United States. The panel's report focused on applications, tools, and research and development in universities and industry and on major national projects. JTEC formed a panel of individuals from the academic and business communities to conduct this study. The primary objectives of the JTEC panel were to investigate Japanese knowledge-based systems development from both technological and business perspectives and compare progress and trends with similar developments in the United States. The panel focused on (1) applications in the business sector, (2) infrastructure and tools, (3) advanced knowledge systems development in industry, (4) advanced knowledge systems research in universities, and (5) national projects. The JTEC panel visited 19 sites during its 1-week visit to Japan in March 1992 and conferred with Japanese computer scientists and business executives both before and after the official visits. The panel visited four major computer manufacturers; eight companies that are applying expert systems to their operations; three universities; three national projects; and Nikkei AI, a publication that conducts an annual survey of expert system applications in Japan. This article summarizes the findings of the panel in each of the five areas listed. The panel members were Edward Feigenbaum, Stanford University (chair); Peter Friedland, National Aeronautics and Space Administration; Bruce B. Johnson, Andersen Consulting; H. Penny Nii, Stanford; Herbert Schorr, University of Southern California; and Howard Shrobe, Massachusetts Institute of Technology and Symbolics, Inc.). Robert Engelmore served as an ex officio member of the panel with the responsibility of producing the final report. Also present on the site visits were Y. T. Chien, National Science Foundation, and R. D. Shelton, JTEC. The sponsors of the JTEC study defined the dimensions of the study to include the following areas: (1) business-sector applications of expert systems; (2) advanced knowledgebased systems in industry; (3) advanced knowledge-based systems research in universities; (4) work at government laboratories, especially the laboratory of the Japanese Fifth-Generation Computer Project; and (5) the electronic dictionary research knowledge base building effort. The panel was also asked to observe the fuzzy system work being done in Japan, any neural network applications that affect expert system development, and the new national project known as Real-World Computing.
Complex electromechanical products, such as high-end printers and photocopiers, are designed as families, with reusable modules put together in different manufacturable configurations, and the ability to add new modules in the field. The modules are controlled locally by software that must take into account the entire configuration. This poses two problems for the manufacturer. The first is how to make the overall control architecture adapt to, and use productively, the inclusion of particular modules. The second is to decide, at design time, whether a proposed module is a worthwhile addition to the system: will the resulting system perform enough better to outweigh the costs of including the module?
The AAAI-05 workshops were held on Saturday and Sunday, July 9-10, in Pittsburgh, Pennsylvania. The thirteen workshops were Contexts and Ontologies: Theory, Practice and Applications, Educational Data Mining, Exploring Planning and Scheduling for Web Services, Grid and Autonomic Computing, Human Comprehensible Machine Learning, Inference for Textual Question Answering, Integrating Planning into Scheduling, Learning in Computer Vision, Link Analysis, Mobile Robot Workshop, Modular Construction of Humanlike Intelligence, Multiagent Learning, Question Answering in Restricted Domains, and Spoken Language Understanding. The cochairs of the AAAI-05 Workshop Program were Adele Howe, Colorado State University and Peter Stone, The University of Texas at Austin. During the last decade, there was a series of successful workshops and conferences on the development and application of contexts and ontologies. Early workshops focused mostly on identifying what contexts and ontologies are and how they can be formalized and exploited.
"Solving Large-Scale Constraint Satisfaction and Scheduling Problems Using a Heuristic Repair Method," by Steve Minton, Mark Johnston, Andy Phillips, and Phil Laird clearly achieved both. It proved that local search and repair was applicable to a wide class of constraint-satisfaction problems and clearly explicated the theory behind that proof. The work epitomizes the guiding philosophy of that laboratory: AI research can simultaneously advance the state of the art and provide practical solutions to key problems faced by the Space Agency and its collaborators. Minton and colleagues developed a heuristic repair method, called "min-conflicts" for solving large-scale constraint-satisfaction problems (CSP), with a particular focus on massive scheduling tasks. Mark Johnston, an astronomer and computer scientist from the Space Telescope Science Institute at Johns Hopkins, served simultaneously as domain expert and codeveloper.