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).

Transfer learning aims to solve new learning problems by extracting and making use of the common knowledge found in related domains. A key element of transfer learning is to identify structured knowledge to enable the knowledge transfer. Structured knowledge comes in different forms, depending on the nature of the learning problem and characteristics of the domains. In this article, we describe three of our recent works on transfer learning in a progressively more sophisticated order of the structured knowledge being transferred. We show that optimization methods, and techniques inspired by the concerns of data reuse can be applied to extract and transfer deep structural knowledge between a variety of source and target problems. In our examples, this knowledge spans explicit data labels, model parameters, relations between data clusters and relational action descriptions. 

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.

Cancer kills millions of people each year. From an AI perspective, finding effective treatments for cancer is a high-dimensional search problem characterized by many molecularly distinct cancer subtypes, many potential targets and drug combinations, and a dearth of high quality data to connect molecular subtypes and treatments to responses. The broadening availability of molecular diagnostics and electronic medical records, presents both opportunities and challenges to apply AI techniques to personalize and improve cancer treatment. We discuss these in the context of Cancer Commons, a “rapid learning” community where patients, physicians, and researchers collect and analyze the molecular and clinical data from every cancer patient, and use these results to individualize therapies. Research opportunities include: adaptively-planning and executing individual treatment experiments across the whole patient population, inferring the causal mechanisms of tumors, predicting drug response in individuals, and generalizing these findings to new cases. The goal is to treat each patient in accord with the best available knowledge, and to continually update that knowledge to benefit subsequent patients. Achieving this goal is a worthy grand challenge for AI.

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.

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 ...

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.

Automatic transfer of learned knowledge from one task or domain to another offers great potential to simplify and expedite the construction and deployment of intelligent systems. In practice however, there are many barriers to achieving this goal. In this article, we present a prototype system for the real-world context of transferring knowledge of American football from video observation to control in a game simulator. We trace an example play from the raw video through execution and adaptation in the simulator, highlighting the system's component algorithms along with issues of complexity, generality, and scale. We then conclude with a discussion of the implications of this work for other applications, along with several possible improvements.

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.

A geoscientist would be faced with the situation shown on the right of the figure; his task is to deduce the situation shown at the left, along with the processes that were at work and the timeline involved. To accomplish this, a geoscientist first dates a set of rock samples from the present surface, then reasons backward to deduce what process affected the original landform. This is a difficult deduction: geological processes take place over an extremely long period of time, and evidence remaining today is scarce and noisy. Finally, experts in geological dating, like experts in any field, are only human, and can be biased in favor of one theory over another. In the face of these problems, experts form an exhaustive list of possible hypotheses and consider the evidence for and against each one--much like the AI concept of argumentation. Our system to automate this reasoning, Calvin, uses the same argumentation process as experts, comparing the strength of the evidence for and against a set of hypotheses before coming to a conclusion. We collected knowledge about how isotope dating experts reason through interviews with several dozen geoscientists.