As educators, we have high hopes for Electronic Tangibles (ETs), we expect ETs to: Interest more students in the study of computing Broaden students' views of computing Invite non-majors to learn something about the computing Attract students to computer science as a major Help students learn about particular ETs Attract students to our classes by incorporating a flashy ET in the course material Improve student understanding of some difficult topics Maintain student interest throughout the class However some important questions arise: Can we and should we extend these benefits throughout the K-20 curriculum? And if we can't, are we guilty of bait-and-switch?

The Institute of Education Sciences (IES) provides funding opportunities for researchers to bring their knowledge of learning, cognitive science, and technology to bear on education practice. This panel describes opportunities available through the National Center for Education Research and the National Center for Special Education Research.

The AAAI-07 workshop program was held Sunday and Monday, July 22-23, in Vancouver, British Columbia, Canada. The program included the following thirteen workshops: (1) Acquiring Planning Knowledge via Demonstration; (2) Configuration; (3) Evaluating Architectures for Intelligence; (4) Evaluation Methods for Machine Learning; (5) Explanation-Aware Computing; (6) Human Implications of Human-Robot Interaction; (7) Intelligent Techniques for Web Personalization; (8) Plan, Activity, and Intent Recognition; (9) Preference Handling for Artificial Intelligence; (10) Semantic e-Science; (11) Spatial and Temporal Reasoning; (12) Trading Agent Design and Analysis; and (13) Information Integration on the Web.

The Electrical Systems Division at the NASA Kennedy Space Center has developed and deployed an agent-based tool to monitor the space shuttle's ground processing telemetry stream. The application, the NASA Engineering Shuttle Telemetry Agent (NESTA), increases situational awareness for system and hardware engineers during ground processing of the shuttle's subsystems. The agent provides autonomous monitoring of the telemetry stream and automatically alerts system engineers when predefined criteria have been met. Efficiency and safety are improved through increased automation. Sandia National Labs' Java Expert System Shell is employed as the rule engine. The shell's predicate logic lends itself well to capturing the heuristics and specifying the engineering rules of this spaceport domain. The declarative paradigm of the rule- based agent yields a highly modular and scalable design spanning multiple subsystems of the shuttle. Several hundred monitoring rules have been written thus far with corresponding notifications sent to shuttle engineers. This article discusses the rule-based telemetry agent used for space shuttle ground processing and explains the problem domain, development of the agent software, benefits of AI technology, and deployment and sustaining engineering of the product.

Amino acid profiles, which capture position-specific mutation probabilities, are a richer encoding of biological sequences than the individual sequences themselves. However, profile comparisons are much more computationally expensive than discrete symbol comparisons, making profiles impractical for many large datasets. Furthermore, because they are such a rich representation, profiles can be difficult to visualize. To overcome these problems, we propose a discretization for profiles using an expanded alphabet representing not just individual amino acids, but common profiles. By using an extension of information bottleneck (IB) incorporating constraints and priors on the class distributions, we find an informationally optimal alphabet. This discretization yields a concise, informative textual representation for profile sequences. Also alignments between these sequences, while nearly as accurate as the full profile-profile alignments, can be computed almost as quickly as those between individual or consensus sequences. A full pairwise alignment of SwissProt would take years using profiles, but less than 3 days using a discrete IB encoding, illustrating how discrete encoding can expand the range of sequence problems to which profile information can be applied.

For example, Yona et al.[10] uses profiles to align distant homologues from

Is there a way for an algorithm linked to an unknown body to infer by itself information about this body and the world it is in? Taking the case of space for example, is there a way for this algorithm to realize that its body is in a three dimensional world? Is it possible for this algorithm to discover how to move in a straight line? And more basically: do these questions make any sense at all given that the algorithm only has access to the very high-dimensional data consisting of its sensory inputs and motor outputs? We demonstrate in this article how these questions can be given a positive answer. We show that it is possible to make an algorithm that, by analyzing thelaw that links its motor outputs to its sensory inputs, discovers information about the structure of the world regardless of the devices constituting the body it is linked to. We present results from simulations demonstrating a way to issue motor orders resulting in "fundamental" movements of the body as regards the structure of the physical world.

Is there a way for an algorithm linked to an unknown body to infer by itself information about this body and the world it is in? Taking the case of space for example, is there a way for this algorithm to realize that its body is in a three dimensional world? Is it possible for this algorithm to discover how to move in a straight line? And more basically: do these questions make any sense at all given that the algorithm only has access to the very high-dimensional data consisting of its sensory inputs and motor outputs? We demonstrate in this article how these questions can be given a positive answer. We show that it is possible to make an algorithm that, by analyzing the law that links its motor outputs to its sensory inputs, discovers information about the structure of the world regardless of the devices constituting the body it is linked to. We present results from simulations demonstrating a way to issue motor orders resulting in "fundamental" movements of the body as regards the structure of the physical world.

We present a neural network model that shows how the prefrontal cortex, interacting with the basal ganglia, can maintain a sequence of phonological information in activation-based working memory (i.e., the phonological loop). The primary function of this phonological loop may be to transiently encode arbitrary bindings of information necessary for tasks - the combinatorial expressive power of language enables very flexible binding of essentially arbitrary pieces of information. Our model takes advantage of the closed-class nature of phonemes, which allows different neural representations of all possible phonemes at each sequential position to be encoded. To make this work, we suggest that the basal ganglia provide a region-specific update signal that allocates phonemes to the appropriate sequential coding slot. To demonstrate that flexible, arbitrary binding of novel sequences can be supported by this mechanism, we show that the model can generalize to novel sequences after moderate amounts of training.

We present a model of binding of relationship information in a spatial domain (e.g., square above triangle) that uses low-order coarse-coded conjunctive representations instead of more popular temporal synchrony mechanisms. Supporters of temporal synchrony argue that conjunctive representations lack both efficiency (i.e., combinatorial numbers of units are required) and systematicity (i.e., the resulting representations are overly specific and thus do not support generalization to novel exemplars). To counter these claims, we show that our model: a) uses far fewer hidden units than the number of conjunctions represented, by using coarse-coded, distributed representations where each unit has a broad tuning curve through high-dimensional conjunction space, and b) is capable of considerable generalization to novel inputs.