This Fall issue marks the first time we have devoted the AI Magazine to a single theme. The idea originated a couple of years ago, and I'm pleased to see the actual implementation. Mark Fox, Special Editor for this issue, is to be congratulated for a fine job of selecting some of the best authorities in the field and working with them to produce an excellent survey of the current state of the art in AI for manufacturing. In fact, Mark exceeded our expectations and solicited more articles than we could reasonably fit in one issue. The quality of all the articles was so high that we didn't want to exclude any of them.

In this issue of AI Magazine, we continue our presentation of extended versions of papers presented at IAAI-12 (held in Toronto, Ontario, Canada) that were selected for their description of AI technologies that are in practical use. Our selections for this issue describe deployed applications. They explain the context, requirements, and constraints of the application, how the technology was adapted to satisfy those factors, and the impact that this innovation brought to the operation in terms of cost and performance. The articles also supply useful insights into use cases that we hope can also be translated to other work that the AI community is engaged in. In the first of these deployed application articles, eBird: A Human/Computer Learning Network to Improve Biodiversity Conservation and Research by Steve Kelling, Carl Lagoze, Weng-Keen Wong, Jun Yu, Theodoros Damoulas, Jeff Gerbracht, Daniel Fink, and Carla Gomes, the authors describe an intriguing application that successfully combines the best in human and artificial computing capabilities with an active feedback loop between people and machines.

An informal workshop on concurrent logic programming, metaprogramming, and open systems was held at Xerox Palo Alto Research Center (PARC) on 8-9 September 1987 with support from the American Association for Artificial Intelligence. The 50 workshop participants came from the Japanese Fifth Generation Project (ICOT), the Weizmann Institute of Science in Israel, Imperial College in London, the Swedish Institute of Computer Science, Stanford University, the Massachusetts Institute of Technology (MIT), Carnegie-Mellon University (CMU), Cal Tech, Science University of Tokyo, Melbourne University, Calgary University, University of Wisconsin, Case Western Reserve, University of Oregon, Korea Advanced Institute of Science and Technology (KAIST), Quintus, Symbolics, IBM, and Xerox PARC. No proceedings were generated; instead, participants distributed copies of drafts, slides, and recent papers. A shared vision emerged from the morning session with concurrent logic programming fulfilling the same role that C and Assembler do now. Languages such as Flat Concurrent Prolog and Guarded Horn Clauses are seen as general-purpose, parallel machine languages and interface languages between hardware and software and not, as a newcomer to this field might expect, as high-level, AI, problemsolving languages.

On October 8-10, 2009, an interdisciplinary group met in Beverley, Massachusetts, to evaluate the state of the art in the computational modeling of narrative. Three important findings emerged: (1) current work in computational modeling is described by three different levels of representation; (2) there is a paucity of studies at the highest, most abstract level aimed at inferring the meaning or message of the narrative; and (3) there is a need to establish a standard data bank of annotated narratives, analogous to the Penn Treebank. We use them to entertain, communicate, convince, and explain. One workshop participant noted that "as far as I know, every society in the world has stories, which suggests they have a psychological basis, that stories do something for you." To truly understand and explain human intelligence, reasoning, and beliefs, we need to understand why narrative is universal and explain the function it serves. Computational modeling is a natural method for investigating narrative. As a complex cognitive phenomenon, narrative touches on many areas that have traditionally been of interest to artificial intelligence researchers: its different facets draw on our capacities for natural language understanding and generation, commonsense reasoning, analogical reasoning, planning, physical perception (through imagination), and social cognition. Successful modeling will undoubtedly require researchers from these many perspectives and more, using a multitude of different techniques from the AI toolkit, ranging from, for example, detailed symbolic knowledge representation to largescale statistical analyses. The relevance of AI to narrative, and vice versa, is compelling.

The articles in this special issue of AI Magazine include those that propose specific tests and those that look at the challenges inherent in building robust, valid, and reliable tests for advancing the state of the art in AI. To people outside the field, the test -- which hinges on the ability of machines to fool people into thinking that they (the machines) are people -- is practically synonymous with the quest to create machine intelligence. Within the field, the test is widely recognized as a pioneering landmark, but also is now seen as a distraction, designed over half a century ago, and too crude to really measure intelligence. Intelligence is, after all, a multidimensional variable, and no one test could possibly ever be definitive truly to measure it. Moreover, the original test, at least in its standard implementations, has turned out to be highly gameable, arguably an exercise in deception rather than a true measure of anything especially correlated with intelligence.

ATP systems are used in a wide variety of domains: A mathematician might use the axioms of group theory to prove the conjecture that groups of order two are commutative; a management consultant might formulate axioms that describe how organizations grow and interact and, from these axioms, prove that organizational death rates decrease with age; or a frustrated teenager might formulate the jumbled faces of a Rubik's cube as a conjecture and prove, from axioms that describe legal changes to the cube's configuration, that the cube can be rearranged to the solution state. All these tasks can be performed by an ATP system, given an appropriate formulation of the problem as axioms, hypotheses, and a conjecture. Most commonly, ATP systems are embedded as components of larger, more complex software systems, and in this context, the ATP systems are required to autonomously solve subproblems that are generated by the overall system. To build a useful ATP system, several issues have to ...

Today, as the world moves into one in which everyone owns at least one mobile device, be it a smartphone, a tablet, or other handheld device, applications on the devices are increasingly more intelligent as well. We will see more and more applications of AI on the mobile devices. This special issue of AI Magazine is devoted to some exemplary works of AI on mobile devices. We include four works that range from mobile activity recognition and air-quality detection to machine translation and image compression. These works were chosen from a variety of sources, including the International Joint Conference on Artificial Intelligence 2011 Special Track on Integrated and Embedded AI Systems, held in Barcelona, Spain, in July 2011. In "User-Centric Indoor Air-Quality Monitoring on Mobile Devices," written by Yifei Jiang, Kun Li, Ricardo Piedrahita, Yun Xiang, Lei Tian, Omkar Mansata, Qin Lv, Robert P. Dick, Michael Hannigan, and Li Shang, the authors develop a novel and important technique for portable indoor air quality (IAQ) detec-

Deploying AI systems on the Web provides tangible evidence of the power and utility of AI techniques. Next time you encounter AI bashing, wouldn't it be satisfying to counter with a handful of well-chosen URLs? At the conference, Jude Shavlik asked me to edit a special issue of AI Magazine describing AI systems that have the Web as their domain. Indeed, the authors of each article included in this special issue have promised to create and maintain a URL pointing to a working prototype. Now, almost a year later, we have the fruit of this labor.

AI Game-Playing Techniques: Are They Useful for Anything Other Than Games? In conjunction with the American Association for Artificial Intelligence's Hall of Champions exhibit, the Innovative Applications of Artificial Intelligence held a panel discussion entitled "AI Game-Playing Techniques: Are They Useful for Anything Other Than Games?" This article summarizes the panelists' comments about whether ideas and techniques from AI game playing are useful elsewhere and what kinds of game might be suitable as "challenge problems" for future research. AAAI-98's Hall of Champions exhibit) is an AI games researcher at the University of Alberta and author of the checkers program The early research on the alpha-beta search algorithm was useful in establishing a foundation for AI theories of heuristic search, and these theories have been useful in many areas of AI. Several of the panelists (particularly Schaeffer, Wilkins, and Fotland) pointed out that the minimax search algorithms traditionally associated with AI have only a limited range of applicability.

This article is an editorial introduction to the research discipline of bioinformatics and to the articles in this special issue. In particular, we address the issue of how techniques from AI can be applied to many of the open and complex problems of modern-day molecular biology. Undoubtedly, bioinformatics is a truly interdisciplinary field: Although some researchers continuously affect wet labs in life science through collaborations or provision of tools, others are rooted in the theory departments of exact sciences (physics, chemistry, or engineering) or computer sciences. This wide variety creates many different perspectives and terminologies. One result of this Babel of languages is that there is no single definition for what the subject of this young field really is.