We study the derivational complexity induced by the dependency pair method, enhanced with standard refinements. We obtain upper bounds on the derivational complexity induced by the dependency pair method in terms of the derivational complexity of the base techniques employed. In particular we show that the derivational complexity induced by the dependency pair method based on some direct technique, possibly refined by argument filtering, the usable rules criterion, or dependency graphs, is primitive recursive in the derivational complexity induced by the direct method. This implies that the derivational complexity induced by a standard application of the dependency pair method based on traditional termination orders like KBO, LPO, and MPO is exactly the same as if those orders were applied as the only termination technique.

See Leuski et al. (2006) and to the same question -- for example, "What Leuski and Traum (2008) for more details. is your name?" -- depending on who the interactor The final parameter is the classification threshold is looking at. NPCEditor's user interface allows the on the KL-divergence value: only answers that designer to define arbitrary annotation classes or score above the threshold value are returned from categories and specify which of these annotation the classifier. The threshold is determined by tuning categories should be used in classification.

This article focuses on a broad framing of the DARPA Transfer Learning Program research and an assessment of its progress, limitations, and challenges, from an admittedly personal but DARPA-influenced perspective. I will focus on a broad framing of TL that that will allow us to talk about this body of work as a whole, and use this to look towards work yet to be done in this area. I will consider both indicated application areas for transfer learning, as well as indicated future research challenges. With each of these I will also venture assessment of the "ripeness" of each of these subareas for follow on work-of course this assessment will be a very personal estimation based on the effort and progress made during the TL program.

AIIDE 2010 was held October 11-13, 2010, at Stanford University ajacent to Palo Alto, California. The conference featured 17 paper presentations, 18 posters, 5 demos, 5 invited speakers, a panel on teaching game AI in academe, and the first StarCraft AI competition. Led by the conference chair, Michael Youngblood (University of North Carolina at Charlotte), and the program chair, Vadim Bulitko (University of Alberta), the three days of AIIDE contained a dense and exciting agenda highlighting new research and revealing how AI is applied in many commercial endeavors. The first day was kicked off with an invited talk from Chris Jurney, lead developer of Double Fine Productions, who detailed his work on the nonplayer character pathfinding of Dawn of War II during his time at Relic Entertainment. The morning was completed by research presentations on behavioral techniques with notable work on producing realistic behaviors through alibi generation (Ben Sunshine-Hill and Norman Badler, University of Pennsylvania), which has been widely discussed in the community since, and Ben Weber's (University of California, Santa Cruz) work applying goal-driven autonomy to playing StarCraft (awarded AIIDE 2010 Best Student Paper).

The development of intelligent environments is considered the first and primary step toward the realization of the ambient intelligence vision and requires input from research and contributions from several scientific and engineering disciplines, including computer science, software engineering, artificial intelligence, architecture, social sciences, art, and design. IE conferences create a unique blend of researchers in these disciplines and foster crossdisciplinary discussions, debate, and collaborations. The Sixth International Conference on Intelligent Environments (IE 10) was held July 19-21 at the Sunway campus of Monash University, Kuala Lumpur, Malaysia. The general chairs were Simon Egerton of Monash University and Ichiro Satoh of the Japanese National Institute of Informatics. Vic Callaghan of the University of Essex, UK, and Achilles Kameas of the Hellenic Open University and Computer Technology Institute, Greece, served as program chairs.

We are proud to continue this tradition with the presentation of five articles from the Twenty Second IAAI conference that was held in Atlanta, Georgia, from July 11-14, 2010. We were especially honored to have Jay M. (Marty) Tenenbaum accept the Robert S. Engelmore Memorial Award for his exceptional contributions to AI in computer vision and manufacturing as well as his visionary role in the birth of electronic commerce. This issue of AI Magazine includes an article based on his lecture Cancer: A Computational Disease That AI Can Cure. In this article, Jay Tenenbaum and Jeff Shrager provide a personal view of their work in the development of an AIbased system that addresses the challenge of helping to find a cure for cancer. As a cancer survivor himself, Tenenbaum has a unique insight into the shortcomings of current approaches to treating this disease.

This prize is awarded biennially to recognize and encourage outstanding artificial intelligence research advances that are made by using experimental (Max Planck Institute for Biological Nectar, as well as poster presentations methods of computer science. Cybernetics), Karrie Karahalios (University by a select number of exceptional Thrun and Whittaker, whose teams of Illinois), Michael Kearns technical papers, short papers, student won the 2005 DARPA Grand Challenge (University of Pennsylvania), and Kurt abstracts, and doctoral consortium abstracts. A special Joint will feature talks on five award-winning in particular for high-impact IAAI-11/AAAI-11 Invited Talk by deployed AI applications and 14 contributions to the field of artificial David Ferrucci (IBM T. J. Watson Research emerging applications. The week is intelligence through innovation and Center) on "Building Watson: filled with a host of other programs, achievement in autonomous vehicle An Overview of DeepQA for the ...

In this article, we discuss the problem of transferring search heuristics from one planner to another. More specifically, we demonstrate how to transfer the domain-dependent heuristics acquired by one planner into a second planner. Our motivation is to improve the efficiency and the efficacy of the second planner by allowing it to use the transferred heuristics to capture domain regularities that it would not otherwise recognize. Our experimental results show that the transferred knowledge does improve the second planner's performance on novel tasks over a set of seven benchmark planning domains.

Software defect prediction aims to reduce software testing efforts by guiding testers through the defect-prone sections of software systems. Defect predictors are widely used in organizations to predict defects in order to save time and effort as an alternative to other techniques such as manual code reviews. The usage of a defect prediction model in a real-life setting is difficult because it requires software metrics and defect data from past projects to predict the defect-proneness of new projects. It is, on the other hand, very practical because it is easy to apply, can detect defects using less time and reduces the testing effort. We have built a learning-based defect prediction model for a telecommunication company in the space of one year. In this study, we have briefly explained our model, presented its pay-off and described how we have implemented the model in the company. Furthermore, we compared the performance of our model with that of another testing strategy applied in a pilot project that implemented a new process called Team Software Process (TSP). Our results show that defect predictors can predict 87 percent of code defects, decrease inspection efforts by 72 percent and hence, reduces post-release defects by 44 percent. Furthermore, they can be used as complementary tools for a new process implementation whose effects on testing activities are limited.

This text provides a clear and systematic development of the essentials of mobile robotics. The second edition adds up-to-date material to a book that has already been adopted in robotics classes worldwide. With this guide in hand, students and readers will swiftly navigate the field toward more advanced systems.