Art historians and archaeologists have long grappled with the regional classification of ancient Near Eastern ivory carvings. Based on the visual similarity of sculptures, individuals within these fields have proposed object assemblages linked to hypothesized regional production centers. Using quantitative rather than visual methods, we here approach this classification task by exploiting computational methods from machine learning currently used with success in a variety of statistical problems in science and engineering. We first construct a prediction function using 66 categorical features as inputs and regional style as output. The model assigns regional style group (RSG), with 98 percent prediction accuracy. We then rank these features by their mutual information with RSG, quantifying single-feature predictive power. Using the highest- ranking features in combination with nomographic visualization, we have found previously unknown relationships that may aid in the regional classification of these ivories and their interpretation in art historical context.

Extempo Systems, Inc. was founded in 1995 to commercialize intelligent characters. Our team built innovative software and novel applications for several markets. We had some early-adopting customers during the Internet boom, but the company was not quite able to survive the significant downturn in corporate IT spending when the bubble burst. In 2004, Extempo ceased operations and was formally liquidated. Although our commercial venture failed, we learned a lot, had fun, and are trying again with a new company. To others who aspire to commercialize their AI technology, I say: ";;Take a chance!";;

On March 27, 2006, I gave a light-hearted and occasionally bittersweet presentation on “Whatever Happened to AI?” at the Stanford Spring Symposium presentation – to a lively audience of active AI researchers and formerly-active ones (whose current inaction could be variously ascribed to their having aged, reformed, given up, redefined the problem, etc.)  This article is a brief chronicling of that talk, and I entreat the reader to take it in that spirit: a textual snapshot of a discussion with friends and colleagues, rather than a scholarly article. I begin by whining about the Turing Test, but only for a thankfully brief bit, and then get down to my top-10 list of factors that have retarded progress in our field, that have delayed the emergence of a true strong AI.

Human-Robot Interaction (HRI-2008) with robots," highlights the importance It also featured Foundation, and the European a panel on "robo-ethics" intended Network for the Advancement of Artificial to start a discussion of the ethical Cognitive Systems (EU Cognition) and societal implications of provided grants. More than 250 autonomous robots and a panel on representatives from academia, government, "what is HRI?" that examined the constitutive and industry attended HRI-components of human-robot 2008. HRI is the premier forum for the Of the 134 submissions, the program presentation and discussion of committee accepted 48 full research results in human-robot interaction. Human-robot interaction 27 submissions) were featured in a special is inherently interdisciplinary session. The workshops artificial intelligence, cognitive science, addressed metrics (an examination of ergonomics, human-computer proposed guidelines for evaluating interaction, psychology, robotics, and HRI), coding behavioral video data other fields. From 1997 to 2000, he was vice president of development for Fourth Planet, Inc., a developer of real-time visualization software. Fong has published more than 50 papers in field robotics, human-robot interaction, virtual reality user interfaces, and parallel processing, was chair of the 2006 AAAI Spring Symposium on human-robot interaction in space, and is cogeneral chair for HRI-2008. Kerstin Dautenhahn is the research professor of artificial intelligence in the School of Computer Science and coordinator of the Adaptive Systems Research Group at the University of Hertfordshire in the United Kingdom. Save the Date! -- July 11-15, 2010 AAAI comes to Atlanta, Georgia in 2010! Please mark your calendars, and visit www. She was general chair of IEEE RO-MAN06 and cogeneral chair of HRI-2008. Scheutz was the coprogram chair for HRI-Seven student teams competed to award went to "Robots in Organizations: University of Amsterdam took top Jodi Forlizzi.

This paper presents a Multi-Agent approach to the problem of recommending training courses to engineering professionals. The recommendation system is built as a proof of concept and limited to the electrical and mechanical engineering disciplines. Through user modelling and data collection from a survey, collaborative filtering recommendation is implemented using intelligent agents. The agents work together in recommending meaningful training courses and updating the course information. The system uses a users profile and keywords from courses to rank courses. A ranking accuracy for courses of 90% is achieved while flexibility is achieved using an agent that retrieves information autonomously using data mining techniques from websites. This manner of recommendation is scalable and adaptable. Further improvements can be made using clustering and recording user feedback.

The AAAI video archive is a central source of information about videotapes and films with information about AI that are stored digitally on other sites or physically in institutional archives. For each video, the archive includes a brief description of the contents and personae, one or more representative short clips for classroom or individual use, and the location of the archival copy (for example, at a university library).

AAAI-10 will be held in Atlanta, Georgia! Please mark your calendars, and visit www.aaai.org/Conferences/AAAI/ For more information on Human and Machine Cognition Chicago, please visit www.choose AAAI recently launched a series of (IHMC), is the sixth recipient of the chicago.com. The AAAI 2008 Teaching Forum aims 2006 and 2007 will continue in 2008, industry representative.

This research report introduces the generation of textual entailment within the project CSIEC (Computer Simulation in Educational Communication), an interactive web-based human-computer dialogue system with natural language for English instruction. The generation of textual entailment (GTE) is critical to the further improvement of CSIEC project. Up to now we have found few literatures related with GTE. Simulating the process that a human being learns English as a foreign language we explore our naive approach to tackle the GTE problem and its algorithm within the framework of CSIEC, i.e. rule annotation in NLML, pattern recognition (matching), and entailment transformation. The time and space complexity of our algorithm is tested with some entailment examples. Further works include the rules annotation based on the English textbooks and a GUI interface for normal users to edit the entailment rules.

We derive a cost functional for estimating the relationship between highdimensional observations and the low-dimensional process that generated them with no input-output examples. Limiting our search to invertible observation functions confers numerous benefits, including a compact representation and no suboptimal local minima. Our approximation algorithms for optimizing this cost functional are fast and give diagnostic bounds on the quality of their solution. Our method can be viewed as a manifold learning algorithm that utilizes a prior on the low-dimensional manifold coordinates. The benefits of taking advantage of such priors in manifold learning and searching for the inverse observation functions in system identification are demonstrated empirically by learning to track moving targets from raw measurements in a sensor network setting and in an RFID tracking experiment.

We present a method for binary online classification of triggered but temporally blurred events that are embedded in noisy time series in the context of online discrimination between left and right imaginary hand-movement. In particular the goal of the binary classification problem is to obtain the decision, as fast and as reliably as possible from the recorded EEG single trials. To provide a probabilistic decision at every time-point t the presented method gathers information from two distinct sequences of features across time. In order to incorporate decisions from prior time-points we suggest an appropriate weighting scheme, that emphasizes time instances, providing a higher discriminatory power between the instantaneous class distributions of each feature, where the discriminatory power is quantified in terms of the Bayes error of misclassification. The effectiveness of this procedure is verified by its successful application in the 3rd BCI competition. Disclosure of the data after the competition revealed this approach to be superior with single trial error rates as low as 10.7, 11.5 and 16.7% for the three different subjects under study.