List of organizers of the Artificial Intelligence and Robotics Methods in Computational BiologyAAAI 2013 workshop.

In pervasive environments, users are situated in rich context and can interact with their surroundings through various services. To improve user experience in such environments, it is essential to find the services that satisfies user preferences in certain context. Thus the suitability of discovered services is highly dependent on how much the context-aware system can understand users' current context and preferred activities. In this paper, we propose an unsupervised learning solution for mining user preferences from the user's past context. To cope with the high dimensionality and heterogeneity of context data, we propose a subspace clustering approach that is able to find user preferences identified by different feature sets. The results of our approach are validated by a series of experiments.

the AAAI-13 Activity Context-Aware System Architectures workshop builds on the groundwork laid at the first and second workshops at AAAI-11 and AAAI-12, for developing systems that capture, transfer, and recall activity context across multiple devices and platforms, used by people individually and collectively. The scope of the workshop includes finding techniques for creating context, activity-driven systems pro- viding end-user value through monitoring, exchange and support on activities which can be performed better with help of computational devices than otherwise. The consortium and workshop interest is focused on using techniques in artificial intelligence to improve the human-computer interface for better human performance of knowledge work.

This work describes a student-friendly, pedagogy-oriented event-handling system for managing multiagent AI simulations in a classroom environment.

Norvig’s (1992) Paradigms of AI Programming is an important book for learning about AI programming. However, the book uses Common Lisp as the programming language, which is less popular now than in 1992. Thus, we have translated many classical AI programs described in the book into Python, a more commonly used language. We have also documented the programs and offered them as a resource in a course on knowledge-based AI.

Many AI courses include design and programming projects that provide students with opportunities for experiential learning. Design and programming projects in courses on knowledge-based AI typically explore topics in knowledge, memory, reasoning, and learning. Traditional AI curricula, however, seldom highlight issues of modality of representations, often focusing solely on propositional representations. In this paper, we report on an investigation into learning about representational modality through a series of projects based around geometric analogy problems similar to the Raven’s Progressive Matrices test of intelligence. We conducted this experiment over three years, from Fall 2010 through Fall 2012, in a class on knowledge-based AI. We used the methodology of action research in which the teacher is also the researcher. We discovered that students found these projects motivating, engaging, and challenging, in several cases investing significant time and posting their work online. From our perspective, the projects accomplished the goal of learning about representational modality in addition to knowledge representation and reasoning.

Accurate and detailed measurement of an individual's physical activity is a key requirement for helping researchers understand the relationship between physical activity and health. Accelerometers have become the method of choice for measuring physical activity due to their small size, low cost, convenience and their ability to provide objective information about physical activity. However, interpreting accelerometer data once it has been collected can be challenging. In this work, we applied machine learning algorithms to the task of physical activity recognition from triaxial accelerometer data. We employed a simple but effective approach of dividing the accelerometer data into short non-overlapping windows, converting each window into a feature vector, and treating each feature vector as an i.i.d training instance for a supervised learning algorithm. In addition, we improved on this simple approach with a multi-scale ensemble method that did not need to commit to a single window size and was able to leverage the fact that physical activities produced time series with repetitive patterns and discriminative features for physical activity occurred at different temporal scales.

Before laboratory testing, bioinformatics problems often require a machine-learned predictor to identify the most likely choices among a wealth of possibilities. Researchers may advocate different predictors for the same problem, none of which is best in all situations. This paper introduces a case-based meta-predictor that combines a set of elaborate, pre-existing predictors to improve their accuracy on a difficult and important problem: protein-ligand docking. The method focuses on the reliability of its component predictors, and has broad potential applications in biology and chemistry. Despite noisy and biased input, the method outperforms its individual components on benchmark data. It provides a promising solution for the performance improvement of compound virtual screening, which would thereby reduce the time and cost of drug discovery.

Coronary heart disease (CHD) is a major cause of death worldwide.In the U.S. CHD is responsible for approximated 1 in every 6 deaths with a coronary event occurring every 25 seconds and about 1 death every minute based on data current to 2007.Although a multitude of cardiovascular risks factors have been identified, CHD actually reflects complexinteractions of these factors over time. Today's datasets from longitudinal studies offer great promise to uncover these interactions but also pose enormous analytical problems due to typically large amount of both discrete and continuous measurements and risk factors with potential long-range interactions over time.Our investigation demonstrates that a statistical relational analysis of longitudinal data can easily uncover complex interactions of risks factors and actually predict future coronary artery calcification (CAC) levels --- an indicator of the risk of CHD present subclinically in an individual --- significantly better than traditional non-relational approaches.The uncovered long-range interactions between risk factors conform to existing clinical knowledgeand are successful in identifying risk factors at the early adult stage. This may contribute to monitoring young adults via smartphones and to designing patient-specific treatments in young adults to mitigate their risk later.

Distributed denial of service (DDoS) attacks constitute a rapidly evolving threat in the current Internet. In this paper we introduce Multiagent Router Throttling, a decentralized DDoS response mechanism in which a set of upstream routers independently learn to throttle traffic towards a victim server. We compare our approach against a baseline and a popular throttling technique from the literature, and we show that our proposed approach is more secure, reliable and cost-effective. Furthermore, our approach outperforms the baseline technique and either outperforms or has the same performance as the popular one.