This article introduces the challenge of digital preservation in the area of engineering design and manufacturing and presents a methodology to apply knowledge representation and semantic techniques to develop Digital Engineering Archives. This work is part of an ongoing, multiuniversity, effort to create cyber infrastructure-based engineering repositories for undergraduates (CIBER-U) to support engineering design education. The technical approach is to use knowledge representation techniques to create formal models of engineering data elements, workflows and processes. With these formal engineering knowledge and processes can be captured and preserved with some guarantee of long-term interpretability. The article presents examples of how the techniques can be used to encode specific engineering information packages and workflows. These techniques are being integrated into a semantic wiki that supports the CIBER-U engineering education activities across nine universities and involving over 3500 students since 2006.

This paper presents a brief summary of a number of different approaches to the semantic representation and automated interpretation of engineering data. In this context, engineering data is represented as Computer-Aided Design (CAD) files, 3D models or assemblies. Representing and reasoning about these objects is a highly interdisciplinary problem, requiring techniques that can handle the complex interactions and data types that occur in the engineering domain. This paper presents several examples, taken from different problem areas that have occupied engineering and computer science researchers over the past 15 years. Many of the issues raised by these problems remain open, and the experience of past efforts can serve to identify fertile opportunities for investigation today.

The use of statistical AI techniques in authorship recognition (or stylometry) has contributed to literary and historical breakthroughs. These successes have led to the use of these techniques in criminal investigations and prosecutions.  However, few have studied adversarial attacks and their devastating effect on the robustness of existing classification methods. This paper presents a framework for adversarial attacks including obfuscation attacks, where a subject attempts to hide their identity imitation attacks, where a subject attempts to frame another subject by imitating their writing style.  The major contribution of this research is that it demonstrates that both attacks work very well.  The obfuscation attack reduces the effectiveness of the techniques to the level of random guessing and the imitation attack succeeds with 68-91% probability depending on the stylometric technique used.  These results are made more significant by the fact that the experimental subjects were unfamiliar with stylometric techniques, without specialized knowledge in linguistics, and spent little time on the attacks. This paper also provides another significant contribution to the field in using human subjects to empirically validate the claim of high accuracy for current techniques (without attacks) by reproducing results for three representative stylometric methods.

This paper introduces the challenge of digital preservation in the   area of engineering design and manufacturing and presents a   methodology to apply knowledge representation and semantic   techniques to develop Digital Engineering Archives.  This work   is part of an ongoing, multi-university, effort to create   Cyber-Infrastructure-Based Engineering Repositories for   Undergraduates (CIBER-U) to support engineering design education.   The technical approach is to use knowledge representation techniques   to create formal models of engineering data elements, workflows and   processes.  With these formal engineering knowledge and processes   can be captured and preserved with some guarantee of long-term   interpretability.  The paper presents examples of how the techniques   can be used to encode specific engineering information     packages and workflows.  These techniques are being integrated   into a semantic Wiki that supports the CIBER-U engineering education   activities across nine universities and involving over 3,500   students since 2006.

Collaboration between research scientists, particularly those with diverse backgrounds, is a driver of scientific innovation. However, finding the right collaborator is often an unscientific process that is subject to chance. This paper explores recommending collaborators based on repeating patterns of previous successful collaboration experiences, what we term prototypical collaborations. We investigate a method for discovering such prototypes to use them as a basis to guide the recommendation of new collaborations. To this end, we also examine two methods for matching collaboration seekers to these prototypical collaborations. Our initial studies reveal that though promising, improving collaborations through recommendation is a complex goal.

The AAAI 2008 Workshop on Mobility and Manipulation (held during the Twenty-Third AAAI Conference on Artificial Intelligence) showcased advances in mobility and manipulation through a half-day workshop and an exhibition. The workshop focused on possible solutions to both technical and organizational challenges to mobility and manipulation research. This article presents the highlights of that discussion along with the content of the accompanying exhibits.

Moving toward true robot autonomy may require new paradigms, hardware, and ways of thinking. The goal of the AAAI 2008 Workshop on Mobility and Manipulation was not only to demonstrate current research successes to the AAAI community but also to road-map future mobility and manipulation challenges that create synergies between artificial intelligence and robotics. The half-day workshop included both a session on the exhibits and a panel discussion. The panel consisted of five prominent researchers who led a discussion of future directions for mobility and manipulation research. Andrew Ng of Stanford University (along with students Ashutosh Saxena and Ellen Klingbeil) focuses on opening arbitrary doors through learning a few visual keypoints, such as the location and type of door handle.

Developments in mechanical control and complex motion planning have enabled robots to become almost commonplace in situations requiring precise but menial, tedious, and repetitive tasks. Recent robotics research has targeted the mechanical and computational challenges inherent in performing a much broader range of tasks autonomously. These problems are less well-defined, requiring greater intelligence, commonsense reasoning, and oftentimes novel solutions. By most definitions, creativity (the generation of novel and useful ideas) is necessary for intelligence; thus research efforts focusing on robotics and creativity are also efforts toward artificial intelligence. As robots and computer physical systems become more capable, they are increasingly useful in the study of creativity itself.