Tekkotsu is an open source application development framework for intelligent mobile robots. Originally designed for undergraduate computer science majors, recent refinements to the framework have led us to explore its use with high school students. We developed a pilot course curriculum to introduce high level robotics to students with little or no programming experience in a way that provides improved feedback and error detection on multiple levels. The use of visualization tools and pair programming techniques scaffolds the learning process and provides a systematic way to introduce robotics as a fun and worthwhile endeavor to novices, and helps instructors efficiently address students’ concerns in a real-time manner.

In this paper we present Myro-C++, developed at the University of Tennessee. Myro-C++ is a C++ port ofthe Python Myro library that was written by the Institute for Personal Robots in Education (IPRE) at Georgia Tech and Bryn Mawr College. Myro-C++ is publicly available, open source software, released under the GPLv3 open source license. At the time of writing, the library has been used six semesters for the CS1 courseat the University of Tennessee, Knoxville. The library contains functions for control of the robot and access to sensor information, and provides the ability to display the live camera image from the robot into a video window. This library is used as a teaching tool in our CS1 course where students learn basic programming fundamentals using multiple artificial intelligence based labs. In addition to the software, the IPRE book, Learning Computing with Robots, has been edited to use C++ examples and explanations, and is freely available. We also present example programs that we use as laboratory assignments in our Introduction to Computer Science course, which are also freely available.

Challenges for Computing include Learning for Life (Taylor et al, 2008). Grand Research Challenges in Information Systems identifies the need to "provide a teacher for These are amongst the key challenges that AIED responds to. What will next generation AIED learning environments be like? GROE report (Woolf, 2010), in order to highlight the expected role of AIED research.

This book presents a methodology and philosophy of empirical science based on large scale lossless data compression. In this view a theory is scientific if it can be used to build a data compression program, and it is valuable if it can compress a standard benchmark database to a small size, taking into account the length of the compressor itself. This methodology therefore includes an Occam principle as well as a solution to the problem of demarcation. Because of the fundamental difficulty of lossless compression, this type of research must be empirical in nature: compression can only be achieved by discovering and characterizing empirical regularities in the data. Because of this, the philosophy provides a way to reformulate fields such as computer vision and computational linguistics as empirical sciences: the former by attempting to compress databases of natural images, the latter by attempting to compress large text databases. The book argues that the rigor and objectivity of the compression principle should set the stage for systematic progress in these fields. The argument is especially strong in the context of computer vision, which is plagued by chronic problems of evaluation. The book also considers the field of machine learning. Here the traditional approach requires that the models proposed to solve learning problems be extremely simple, in order to avoid overfitting. However, the world may contain intrinsically complex phenomena, which would require complex models to understand. The compression philosophy can justify complex models because of the large quantity of data being modeled (if the target database is 100 Gb, it is easy to justify a 10 Mb model). The complex models and abstractions learned on the basis of the raw data (images, language, etc) can then be reused to solve any specific learning problem, such as face recognition or machine translation.

The visual recognition problem is central to computer vision research. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and image classification. We introduce primary representations and learning approaches, with an emphasis on recent advances in the field. The target audience consists of researchers or students working in AI, robotics, or vision who would like to understand what methods and representations are available for these problems.

New this year: Attendance at the tutorials is May 20, 2011.

The Association for the Advancement of Artificial Intelligence was pleased to present the 2010 Fall Symposium Series, held Thursday through Saturday, November 11-13, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the eight symposia are as follows: (1) Cognitive and Metacognitive Educational Systems; (2) Commonsense Knowledge; (3) Complex Adaptive Systems: Resilience, Robustness, and Evolvability; (4) Computational Models of Narrative; (5) Dialog with Robots; (6) Manifold Learning and Its Applications; (7) Proactive Assistant Agents ; and (8) Quantum Informatics for Cognitive, Social, and Semantic Processes. The highlights of each symposium are presented in this report.

For example, baby boomers are aging. Researchers are actively pursuing interdisciplinary research that enables robots to function autnomously within arbitrary environments alongside people. The goal of the AAAI 2010 Workshop on Enabling Intelligence through Middleware was to examine both the successes and opportunities to provide tools that enable a larger pool of researchers to experiment with embodied, intelligent algorithms. The half-day workshop, attended by over 80 people, was held as part of the Twenty-Fourth AAAI Conference on Artificial Intelligence in Atlanta Georgia on July 12, 2010. The workshop consisted of two parts: (1) invited talks and (2) middleware presentations.

Sustainable design is as an important movement in design. Biologically inspired design is a major paradigm for sustainable design. In this paper, we analyze a corpus of biologically inspired design projects in terms of sustainability. We then describe a case study of analogical design of a fog harvesting net, and abstract from it the patterns of Hydrophobia and Hydrophilia. We indicate how these two function-mechanism design patterns occur in several design projects in our corpus. This analysis indicates how biologically inspired sustainable design can be analyzed in terms of cross-domain analogical transfer of design patterns.

This paper presents the formalization of a biological process, cell division, in modular action language ALM. We show how the features of ALM — modularity, separation between an uninterpreted theory and its interpretation — lead to a simple and elegant solution that can be used in answering questions from biology textbooks.