At the recent AAAI conference at Stanford, it became apparent that many new AI research centers are being established around the country in industrial and governmental settings and in universities that have not paid much attention to Al in the past. What does an AI researcher want from his computing facility? What will make him most productive? In fact, the needs of the Al researcher are not very different from the needs of any other researcher in computer science, except that facility-related problems seem to become acute in AI a few years sooner than they are felt in other research areas The considerations are roughly as follows: 0 AI programs tend to be very large because they contain, in one form or another, a lot of knowledge. It follows, then, that any machine used for AI must provide a large virtual address space in order to insulate the researcher from having to think about how to chop up his task into smaller tidbits and overlays.

This editorial introduction provides an overview of artificial intelligence for computational sustainability, and introduces the special issue articles that appear in this issue and the previous issue of AI Magazine. The emerging interdisciplinary field of computational sustainability (Gomes 2009) draws techniques from computer science, information science, mathematics, statistics, operations research, and related disciplines to help balance environmental and socioeconomic needs for sustainable development. Artificial intelligence (AI) techniques play a key role in computational sustainability research, enabling the solution of sustainability problems that involve modeling or decision making in dynamic and uncertain environments. Since 2011, the main AAAI conference has included a special track on computational sustainability, encouraging AI research in this area and broader participation of sustainability researchers in the AAAI community. Sustainable solutions must balance between environmental, societal, and economic demands (United Nations General Assembly 2005).

The integration of AI and vision has been a longterm goal of both disciplines for more than three decades. This special issue illustrates some recent work on bridging the gap. Back then, the issue was anticipated relatively easily, and a full integration of the two fields was expected within a decade. Processing of images is known to result in noisy segmentation, and in general, data might not be consistent in space or time, making AI methods appealing. In early AI, the handling of noisy, partially inconsistent data was, at best, a major challenge.

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 ...

Computer Sczence Department Carnegae-Mellon University Pittsburgh, Pennsylvania 152125 Two CLOSELY RELATED ASPECTS of Artificial Intelligence t,hat have received comparatively little attention in the recent literature are research methodology and the analysis of computational techniques that span multiple application areas. We believe both issues to be increasingly significant, as Artificial Intelligence matures into a scienceand spins off major application efforts. It is imperative to analyze the repertoire of AI methods with respect to past experience, utility in new domains, extensibility, and functional equivalence with other techniques, if N is to become more effective in building upon prior results rather than continually reinverking the proverbial wheel. Similarly, awareness of research methodology issues can help plan future research by learning from past successes and failures. We view the study of research methodology to be similar to the analysis of operational AI techniques, but at a met,a-level; that is, research methodology analyzes the techniques and methods used by the researchers themselves, rather than their programs, to resolve issues of selecting interesting and tract,able problems to investigate, and of deciding how to proceed with t,heir investigations.

If the user is satisfied with the expected consequences, s/he finalizes his(her) decisions. The quality of his(her) decisions are automatically evaluated, and information about the strengths and shortcomings of his(her) decision-making strategy is fed back to him(her). We have used this environment to train and evaluate novice air traffic controllers. Figure 5 shows the two displays of the PMME system. The left-hand side displays the current world and the right-hand side the extrapolated world.

This survey was conducted for technical and historical reasons: First, I work in the commercial AI industry and was worried about missing significant intellectual contributions to my work. Second, this work was intended to test the thesis that there is a coherent body of study called cognitive science. If a new scientific discipline has emerged that is a fusion of psychology, computer science, linguistics, mathematics, philosophy, and neuroscience (as claimed by Gardner 1985), then there should be some evidence of this new discipline in the pattern of scientific publications and researcher biographies. For example, a paper about AI could cite a psychology paper, or a graduate in a mathematics department could migrate into the linguistics field. I have been informally conducting this survey for the last three years.

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.

It has become especially relevant in the last decade, with researchers increasingly investigating how to identify the most suitable existing algorithm for solving a problem instance instead of developing new algorithms. This survey presents an overview of this work focusing on the contributions made in the area of combinatorial search problems, where algorithm selection techniques have achieved significant performance improvements. We unify and organise the vast literature according to criteria that determine algorithm selection systems in practice. The comprehensive classification of approaches identifies and analyzes the different directions from which algorithm selection has been approached. This article contrasts and compares different methods for solving the problem as well as ways of using these solutions.

AI's War on Manipulation: Are We Winning? The next day was going to be a big day: Citizens of Bitotia would once and for all establish which byte order was better, big-endian (B) or little-endian (L). Little Bit Timmy was a big supporter of little endian because that would give him the best position in the word. However, the population was split quite evenly between L and B, with a small minority of Bits who still remembered the single-tape Turing machine and preferred unary encoding (U), without any of this endianness business. Nonetheless, about half of the Bits preferred big-endian (B L U), and about half were the other way round (L B U).