In our work, a robot can proactively ask for help when necessary, based on its awareness of its sensing and actuation limitations. Approaches in which humans provide help to robots do not necessarily reason about the human availability and accuracy. Instead, we model the availability of humans in the robot's environment and present a planning approach that uses such model to generate the robot navigational plans. In particular, we contribute two separate planners that allow a robot to distinguish actions that it cannot complete autonomously from ones that it can. In the first planner, the robot plans autonomous actions when possible and requests help to complete actions that it could not otherwise complete. Then for actions that it can perform autonomously, we use a POMDP policy that incorporates the human availability model to plan actions that reduce uncertainty or that increase the likelihood of the robot finding an available human to help it reduce its uncertainty. We have shown in prior work that asking people in the environment for help during tasks can reduce task completion time and increase the robot's ability to perform tasks.

Representing computational activities is still an open problem in the field of Activity-Aware Computing. In this paper, drawn from our experiences in developing activity-aware applications in support of two populations: nurses working in hospitals and elders living independently; we defined the Context Aware Representational (CARe) model. CARe is an ontology that enables the representation and management of computational activities. We illustrate, through application scenarios, that the CARe ontology is flexible enough to enable developers to c

In this paper we present a full architecture to deploy efficiently a grid in a private cloud approach. We first give details about the resources constraints acquisition. We use Rich Internet Application (RIA) to access and/or modify the resources in a very user-friendly interface. Then, using the previous information, we explain how we can compute a dynamic deployment plan, that can be used either to build an optimal grid of computers or to give information to its scheduler. This plan is computed using pddl solver with various logical constraints obtained from the IT users through the RIA.

The use of automated action planning techniques is essential for efficient mission execution of mobile robots. However, a tremendous effort is needed to represent planning problem domains realistically to meet the real-world constraints. Therefore, there is another source of uncertainty for mobile robot systems due to the impossibility of perfectly representing action representations (e.g., preconditions and effects) in all circumstances. When domain representations are not complete, a planner may not be capable of constructing a valid plan for dynamic events even when it is possible. This research focuses on a generic domain update method to construct alternative plans against real-time execution failures which are detected either during runtime or earlier by a plan simulation process. Based on the updated domain representations, a new executable plan is constructed even when the outcomes of existing operators are not completely known in advance or valid plans are not possible with the existing representation of the domain. A failure resolution scenario is given in the realistic Webots simulator with mobile robots. Since TLPlan is used as the base temporal planner, makespan optimization is achieved with the available knowledge of the robots.

We present a framework for semantic visual scene interpretation in a system with vision and language. In this framework the system consists of two modules, a language module and a vision module that communicate with each other in a form of a dialogue to actively interpret the scene. The language module is responsible for obtaining domain knowledge from linguistic resources and reasoning on the basis of this knowledge and the visual input. It iteratively creates questions that amount to an attention mechanism for the vision module which in turn shifts its focus to selected parts of the scene and applies selective segmentation and feature extraction. As a formalism for optimizing this dialogue we use information theory. We demonstrate the framework on the problem of recognizing a static scene from its objects and show preliminary results for the problem of human activity recognition from video. Experiments demonstrate the effectiveness of the active paradigm in introducing attention and additional constraints into the sensing process.

We study the theoretical advantages of active learning over passive learning. Specifically, we prove that, in noise-free classifier learning for VC classes, any passive learning algorithm can be transformed into an active learning algorithm with asymptotically strictly superior label complexity for all nontrivial target functions and distributions. We further provide a general characterization of the magnitudes of these improvements in terms of a novel generalization of the disagreement coefficient. We also extend these results to active learning in the presence of label noise, and find that even under broad classes of noise distributions, we can typically guarantee strict improvements over the known results for passive learning.

This paper introduces 'just enough' principles and 'systems engineering' approach to the practice of ontology development to provide a minimal yet complete, lightweight, agile and integrated development process, supportive of stakeholder management and implementation independence.

In this paper, a methodology for the automated detection and classification of Tuberculosis(TB) is presented. Tuberculosis is a disease caused by mycobacterium which spreads through the air and attacks low immune bodies easily. Our methodology is based on clustering and classification that classifies TB into two categories, Pulmonary Tuberculosis(PTB) and retroviral PTB(RPTB) that is those with Human Immunodeficiency Virus (HIV) infection. Initially K-means clustering is used to group the TB data into two clusters and assigns classes to clusters. Subsequently multiple different classification algorithms are trained on the result set to build the final classifier model based on K-fold cross validation method. This methodology is evaluated using 700 raw TB data obtained from a city hospital. The best obtained accuracy was 98.7% from support vector machine (SVM) compared to other classifiers. The proposed approach helps doctors in their diagnosis decisions and also in their treatment planning procedures for different categories.

In order to address complex systems, apply pattern recongnition on their evolution could play an key role to understand their dynamics. Global patterns are required to detect emergent concepts and trends, some of them with qualitative nature. Formal Concept Analysis (FCA) is a theory whose goal is to discover and to extract Knowledge from qualitative data. It provides tools for reasoning with implication basis (and association rules). Implications and association rules are usefull to reasoning on previously selected attributes, providing a formal foundation for logical reasoning. In this paper we analyse how to apply FCA reasoning to increase confidence in sports betting, by means of detecting temporal regularities from data. It is applied to build a Knowledge-Based system for confidence reasoning.

We consider the general problem of finding the minimum weight $\bm$-matching on arbitrary graphs. We prove that, whenever the linear programming (LP) relaxation of the problem has no fractional solutions, then the belief propagation (BP) algorithm converges to the correct solution. We also show that when the LP relaxation has a fractional solution then the BP algorithm can be used to solve the LP relaxation. Our proof is based on the notion of graph covers and extends the analysis of (Bayati-Shah-Sharma 2005 and Huang-Jebara 2007}. These results are notable in the following regards: (1) It is one of a very small number of proofs showing correctness of BP without any constraint on the graph structure. (2) Variants of the proof work for both synchronous and asynchronous BP; it is the first proof of convergence and correctness of an asynchronous BP algorithm for a combinatorial optimization problem.