Figure 1a first illustrates by the oval that a Cradle-to-cradle (C2C) design (McDonough & Braungart, critical problem in traditional design is that a product is designed 2002) recognizes that nothing short of full recycling of materials in isolation. In contrast, the products shown in the with no degradation in material quality is necessary square box of Figure 1b illustrate the concept of a product for long-term planet sustainability. C2C advocates looking family, where multiple products are designed within a system to the natural world as an ideal model of recycling, where of material use and reuse, which flows between product organic materials are continually recycled through processes lines. While there may still be materials that come from of decay and growth. They propose design methodology outside the family and there are materials that are byproducts that separates biological cycles and syntheticmaterial of the family production, a family design would seek cycles, enabling biological material to be reclaimed to minimize these and to exploit them in a still larger context.

This article presents our work on building a virtual coach agent, called Dr. Vicky, and training environment (called the Virtual BNI Trainer, or VBT) for learning how to correctly talk with medical patients who have substance abuse issues. This work focuses on how to effectively design menu-based dialogue interactions for conversing with a virtual patient within the context of learning how to properly engage in such conversations according to the brief negotiated interview techniques we desire to train. Dr. Vicky also employs a model of student knowledge to influence the mediation strategies used in personalizing the training experience and guidance offered. The VBT is a prototype training application that will be used by medical students and practitioners within the Yale medical community in the future.

Commonsense is a challenge not only for representation and reasoning but also for large scale knowledge engineering required to capture the breadth of our "everyday" world. One approach to knowledge engineering is to "outsource" the effort to the public through games that generate structured commonsense knowledge from user play. To date, such games have focused on symbolic and textual knowledge. However, an effective commonsense reasoning system will require spatial and physical reasoning capabilities. In this paper, I propose a tool for gathering commonsense information from ordinary people. It is a user-friendly 3D sculpting tool for modeling and annotating models of physical objects and spaces.

List of organizers of the Logical Formalizations of Commonsense Reasoning AAAI symposium.

Developments in sophisticated mobile physiological sensors have presented many novel opportunities for monitoring coaching of individuals. In this work, we investigate the ability to utilize physiological data to recognize the state ofa user while exercising. We discuss recognition of user state using data suchas heart rate, respiration rate, and activity level. We also discuss the development of a motivational agent which utilizes the physiological data to help encourage a user during an exercise routine.

Thus, design knowledge and a description of the desired product existing product environmental impact assessment to automatically synthesize potential solutions. This work approaches are most beneficial to implementing changes focuses on a morphological matrix based approach that during the detailed design phase. In addition, impacts due operates on information stored in a design repository to to materials choices, manufacturing processes utilized, and output high-level descriptions of possible solutions. The transportation of an existing product can be evaluated and following section describes the data source and concept reduced. It has been recognized, however, that generation algorithm.

In multi-label learning, each sample is associated with several labels. Existing works indicate that exploring correlations between labels improve the prediction performance. However, embedding the label correlations into the training process significantly increases the problem size. Moreover, the mapping of the label structure in the feature space is not clear. In this paper, we propose a novel multi-label learning method "Structured Decomposition + Group Sparsity (SDGS)". In SDGS, we learn a feature subspace for each label from the structured decomposition of the training data, and predict the labels of a new sample from its group sparse representation on the multi-subspace obtained from the structured decomposition. In particular, in the training stage, we decompose the data matrix $X\in R^{n\times p}$ as $X=\sum_{i=1}^kL^i+S$, wherein the rows of $L^i$ associated with samples that belong to label $i$ are nonzero and consist a low-rank matrix, while the other rows are all-zeros, the residual $S$ is a sparse matrix. The row space of $L_i$ is the feature subspace corresponding to label $i$. This decomposition can be efficiently obtained via randomized optimization. In the prediction stage, we estimate the group sparse representation of a new sample on the multi-subspace via group \emph{lasso}. The nonzero representation coefficients tend to concentrate on the subspaces of labels that the sample belongs to, and thus an effective prediction can be obtained. We evaluate SDGS on several real datasets and compare it with popular methods. Results verify the effectiveness and efficiency of SDGS.

Temporal information has been the focus of recent attention in information extraction, leading to some standardization effort, in particular for the task of relating events in a text. This task raises the problem of comparing two annotations of a given text, because relations between events in a story are intrinsically interdependent and cannot be evaluated separately. A proper evaluation measure is also crucial in the context of a machine learning approach to the problem. Finding a common comparison referent at the text level is not obvious, and we argue here in favor of a shift from event-based measures to measures on a unique textual object, a minimal underlying temporal graph, or more formally the transitive reduction of the graph of relations between event boundaries. We support it by an investigation of its properties on synthetic data and on a well-know temporal corpus.

Hybrid learning methods use theoretical knowledge of a domain and a set of classified examples to develop a method for classification. Methods that use domain knowledge have been shown to perform better than inductive learners. However, there is no general method to include domain knowledge into all inductive learning algorithms as all hybrid methods are highly specialized for a particular algorithm. We present an algorithm that will take domain knowledge in the form of propositional rules, generate artificial examples from the rules and also remove instances likely to be flawed. This enriched dataset then can be used by any learning algorithm. Experimental results of different scenarios are shown that demonstrate this method to be more effective than simple inductive learning.

This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. Our approach is based on a direct approximation of AIXI, a Bayesian optimality notion for general reinforcement learning agents. Previously, it has been unclear whether the theory of AIXI could motivate the design of practical algorithms. We answer this hitherto open question in the affirmative, by providing the first computationally feasible approximation to the AIXI agent. To develop our approximation, we introduce a new Monte-Carlo Tree Search algorithm along with an agent-specific extension to the Context Tree Weighting algorithm. Empirically, we present a set of encouraging results on a variety of stochastic and partially observable domains. We conclude by proposing a number of directions for future research.