The complexity and tight integration of electromechanical systems often makes them "brittle" and hard to modify in response to changing requirements. We aim to remedy this by capturing expert knowledge as functional blueprints, an idea inspired by regulatory processes that occur in natural morphogenesis. We then apply this knowledge in an intelligent design variation tool. When a user modifies a design, our tool uses functional blueprints to modify other components in response, thereby maintaining integration and reducing the need for costly search or constraint solving. In this paper, we refine the functional blueprint concept and discuss practical issues in applying it to electromechanical systems. We then validate our approach with a case study applying our prototype tool to create variants of a miniDroid robot and by empirical evaluation of convergence dynamics of networks of functional blueprints.

Most approaches to semantics in computational linguistics represent meaning in terms of words or abstract symbols. Grounded-language research bases the meaning of natural language on perception and/or action in the (real or virtual) world. Machine learning has become the most effective approach to constructing natural-language systems; however, current methods require a great deal of laboriously annotated training data. Ideally, a computer would be able to acquire language like a child, by being exposed to language in the context of a relevant but ambiguous environment, thereby grounding its learning in perception and action. We will review recent research in grounded language learning and discuss future directions.

List of conference committees for AAAI-13, IAAI-13, and the EAAI-13 symposium.

Editor David Leake (Indiana University, USA) Reports Editor Robert A. Morris (NASA Ames Research Center, USA) Competition Reports Coeditors Sven Koenig (University of Southern California, USA) Robert A. Morris (NASA Ames Research Center, USA) Managing Editor David M. Hamilton (The Live Oak Press, LLC, USA)

According to Daniel Kahneman, there are two systems that drive the human decision making process: The intuitive system that performs the fast thinking, and the deliberative system that does more logical and slower thinking. Inspired by this model, we propose a framework for implementing human activity recognition on mobile devices. In this area, the mobile app is usually always-on and the general challenge is how to balance accuracy and energy consumption. However, among existing approaches, those based on cellular IDs consume little power but are less accurate; those based on GPS/WiFi sampling are accurate often at the costs of battery drainage; moreover, previous methods in general do not improve over time. To address these challenges, our framework consists of two modes: In the deliberation mode, the system learns cell ID patterns that are trained by existing GPS/WiFi based methods; in the intuition mode, only the learned cell ID patterns are used for activity recognition, which is both accurate and energy-efficient; system parameters are learned to control the transition from deliberation to intuition, when sufficient confidence is gained, and the transition from intuition to deliberation, when more training is needed. For the scope of this paper, we first elaborate our framework in a subproblem in activity recognition, trip detection, which recognizes significant places and trips between them. For evaluation, we collected real-life traces of six participants over five months. Our experiments demonstrated consistent results across different participants in terms of accuracy and energy efficiency, and, more importantly, its fast improvement on energy efficiency over time due to regularities in human daily activities.

Participation will be open to active no later than Friday, June 19, 2013 finally, two years as immediate Past participants as well as other interested (5:00 PM local hotel time (PDT)).

Since people spend a majority of their time indoors, indoor air quality (IAQ) can have a significant impact on human health, safety, productivity, and comfort. Due to the diversity and dynamics of people's indoor activities, it is important to monitor IAQ for each individual. Most existing air quality sensing systems are stationary or focus on outdoor air quality. In contrast, we propose MAQS, a user-centric mobile sensing system for IAQ monitoring. MAQS users carry portable, indoor location tracking and IAQ sensing devices that provide personalized IAQ information in real time. To improve accuracy and energy efficiency, MAQS incorporates three novel techniques: (1) an accurate temporal n-gram augmented Bayesian room localization method that requires few Wi-Fi fingerprints; (2) an air exchange rate based IAQ sensing method, which measures general IAQ using only CO$_2$ sensors; and (3) a zone-based proximity detection method for collaborative sensing, which saves energy and enables data sharing among users. MAQS has been deployed and evaluated via a real-world user study. This evaluation demonstrates that MAQS supports accurate personalized IAQ monitoring and quantitative analysis with high energy efficiency. We also found that study participants frequently experienced poor IAQ.

Ontologies provide a formal description of concepts and their relationships in a knowledge domain. The goal of ontology alignment is to identify semantically matching concepts and relationships across independently developed ontologies that purport to describe the same knowledge. In order to handle the widest possible class of ontologies, many alignment algorithms rely on terminological and structural meth- ods, but the often fuzzy nature of concepts complicates the matching process. However, one area that should provide clear matching solutions due to its mathematical nature, is units of measurement. Several on- tologies for units of measurement are available, but there has been no attempt to align them, notwithstanding the obvious importance for tech- nical interoperability. We propose a general strategy to map these (and similar) ontologies by introducing MathML to accurately capture the semantic description of concepts specified therein. We provide mapping results for three ontologies, and show that our approach improves on lexical comparisons.

We consider the problem of learning the structure of a pairwise graphical model over continuous and discrete variables. We present a new pairwise model for graphical models with both continuous and discrete variables that is amenable to structure learning. In previous work, authors have considered structure learning of Gaussian graphical models and structure learning of discrete models. Our approach is a natural generalization of these two lines of work to the mixed case. The penalization scheme involves a novel symmetric use of the group-lasso norm and follows naturally from a particular parametrization of the model.

A risk assessment method is then used to identify the flood risk in each community using the following risk factors: the area's urbanized area ratio, literacy rate, mortality rate, poverty incidence, radio/TV penetration, and state of structural and nonstructural measures. Vulnerability is defined as a weighted-sum of these components. A „penalty‟ was imposed for reduced vulnerability. Optimization comparison was done with MatLab‟s Genetic Algorithms and Simulated Annealing; Results showed „extreme‟ solutions and realistic designs, for simulated annealing and genetic algorithm, respectively. INTRODUCTION Disaster Risk Management (DRM) at the local, regional, and global scale continues to generate great research interest of a complex, multidisciplinary nature, involving the interplay of scientific, social, economic, and political dimensions. Driven by the series of disasters of increasing frequency and magnitude, DRM meaning and context has evolved into an internationally accepted definition: a systemic approach to identifying, assessing and reducing risk of all kinds associated with hazards and human activities with identified operational and practical disaster risk reduction initiatives. These initiatives have been clarified by the international community through UN‟s 2005 World Conference on Disaster Reduction in Kobe, Japan and accepted as the DRR framework, known as the Hyogo Framework of Action [1]. The ultimate objective of all DRM initiatives remains simple: reduce the loss of lives and property, and improve the capacity of communities to cope with disasters. The 2005 Hyogo Framework of Action (HFA) has been used to review UN member states‟ respective DRM initiatives.