Information Technology


Toys and Tools: Accessible Robotics via Laptop Computers

AAAI Conferences

The ubiquity and capability of off-the-shelf laptop computers offer robotics and AI researchers a remarkable opportunity to reach into the broader computer science curriculum. At Harvey Mudd College we have developed two "lines" of laptop-controlled robots. The first, based on iRobot's vacuums, provides an inexpensive and autonomous platform suitable for indoor, human-scale environments. The second, based on PowerWheels toys, offers an inexpensive and capable platform for large, outdoor navigation and planning tasks. Both of these platforms enable cost-and time-effective undergraduate engagement in the ongoing community of robot-themed venues, exhibitions, contests, and conferences.


A Mixed Reality Approach to Undergraduate Robotics Education

AAAI Conferences

Teaching robotics to undergraduate students requires a course framework that allows students to learn about robotics in stages, without being overwhelmed with details. Such a framework must also provide the students with a motivating application environment that challenges them to apply what they have learned. Robotics competitions have proven to be an excellent method for motivating students, so the framework should be portable and robust enough to be used for competitions, and flexible enough to provide a range of environments that can become more challenging as students become more adept. Finally, the framework should provide repeatability and control for evaluating the student's work, as well as for performing research. In this paper, we overview a mixed reality approach that meets these criteria, and describe its use in an advanced undergraduate course.


Preface

AAAI Conferences

Knowledge representation and reasoning (KR&R) lies at the heart of artificial intelligence research and modern information technology. The highly ambitious goal of the field is to provide computational methods for effectively storing, retrieving, and exploiting human knowledge. It includes representing and reasoning about beliefs, goals, preferences, and actions; it includes models of argumentation, belief change, merging, inconsistency handling, and nonmonotonic reasoning; it also covers topics such as time, space, motion, ontologies, and building useful knowledge-based systems. Applications of KR&R have high potential. A convincing example that has significantly driven KR&R research over the last years is the vision of the semantic web.



Modeling belief systems with scale-free networks

arXiv.org Artificial Intelligence

Evolution of belief systems has always been in focus of cognitive research. In this paper we delineate a new model describing belief systems as a network of statements considered true. Testing the model a small number of parameters enabled us to reproduce a variety of well-known mechanisms ranging from opinion changes to development of psychological problems. The self-organizing opinion structure showed a scale-free degree distribution. The novelty of our work lies in applying a convenient set of definitions allowing us to depict opinion network dynamics in a highly favorable way, which resulted in a scale-free belief network. As an additional benefit, we listed several conjectural consequences in a number of areas related to thinking and reasoning.


Sparse Online Learning via Truncated Gradient

arXiv.org Artificial Intelligence

We propose a general method called truncated gradient to induce sparsity in the weights of online learning algorithms with convex loss functions. This method has several essential properties: The degree of sparsity is continuous -- a parameter controls the rate of sparsification from no sparsification to total sparsification. The approach is theoretically motivated, and an instance of it can be regarded as an online counterpart of the popular $L_1$-regularization method in the batch setting. We prove that small rates of sparsification result in only small additional regret with respect to typical online learning guarantees. The approach works well empirically. We apply the approach to several datasets and find that for datasets with large numbers of features, substantial sparsity is discoverable.


Belief decision support and reject for textured images characterization

arXiv.org Artificial Intelligence

The textured images' classification assumes to consider the images in terms of area with the same texture. In uncertain environment, it could be better to take an imprecise decision or to reject the area corresponding to an unlearning class. Moreover, on the areas that are the classification units, we can have more than one texture. These considerations allows us to develop a belief decision model permitting to reject an area as unlearning and to decide on unions and intersections of learning classes. The proposed approach finds all its justification in an application of seabed characterization from sonar images, which contributes to an illustration.


Unveiling the mystery of visual information processing in human brain

arXiv.org Artificial Intelligence

It is generally accepted that human vision is an extremely powerful information processing system that facilitates our interaction with the surrounding world. However, despite extended and extensive research efforts, which encompass many exploration fields, the underlying fundamentals and operational principles of visual information processing in human brain remain unknown. We still are unable to figure out where and how along the path from eyes to the cortex the sensory input perceived by the retina is converted into a meaningful object representation, which can be consciously manipulated by the brain. Studying the vast literature considering the various aspects of brain information processing, I was surprised to learn that the respected scholarly discussion is totally indifferent to the basic keynote question: "What is information?" in general or "What is visual information?" in particular. In the old days, it was assumed that any scientific research approach has first to define its basic departure points. Why was it overlooked in brain information processing research remains a conundrum. In this paper, I am trying to find a remedy for this bizarre situation. I propose an uncommon definition of "information", which can be derived from Kolmogorov's Complexity Theory and Chaitin's notion of Algorithmic Information. Embracing this new definition leads to an inevitable revision of traditional dogmas that shape the state of the art of brain information processing research. I hope this revision would better serve the challenging goal of human visual information processing modeling.


A new Hedging algorithm and its application to inferring latent random variables

arXiv.org Artificial Intelligence

We present a new online learning algorithm for cumulative discounted gain. This learning algorithm does not use exponential weights on the experts. Instead, it uses a weighting scheme that depends on the regret of the master algorithm relative to the experts. In particular, experts whose discounted cumulative gain is smaller (worse) than that of the master algorithm receive zero weight. We also sketch how a regret-based algorithm can be used as an alternative to Bayesian averaging in the context of inferring latent random variables.


Predicting Regional Classification of Levantine Ivory Sculptures: A Machine Learning Approach

arXiv.org Machine Learning

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.