We describe a framework for rapidly prototyping applications which require intelligent visual processing, but for which reliable algorithms do not yet exist, or for which engineering those algorithms is too costly. The framework, CrowdSight, leverages the power of crowdsourcing to offload intelligent processing to humans, and enables new applications to be built quickly and cheaply, affording system builders the opportunity to validate a concept before committing significant time or capital. Our service accepts requests from users either via email or simple mobile applications, and handles all the communication with a backend human computation platform. We build redundant requests and data aggregation into the system freeing the user from managing these requirements. We validate our framework by building several test applications and verifying that prototypes can be built more easily and quickly than would be the case without the framework.

We present techniques for gathering data that expose errors of automatic predictive models. In certain common settings, traditional methods for evaluating predictive models tend to miss rare-but-important errors---most importantly, rare cases for which the model is confident of its prediction (but wrong). In this paper we present a system that, in a game-like setting, asks humans to identify cases what will cause the predictive-model-based system to fail. Such techniques are valuable in discovering problematic cases that do not reveal themselves during the normal operation of the system, and may include cases that are rare but catastrophic. We describe the design of the system, including design iterations that did not quite work. In particular, the system incentivizes humans to provide examples that are difficult for the model to handle, by providing a reward proportional to the magnitude of the predictive model's error. The humans are asked to ``\emph{Beat the Machine}'' and find cases where the automatic model (``\emph{the Machine}'') is wrong. Experiments show that the humans using Beat the Machine identify more errors than traditional techniques for discovering errors in from predictive models, and indeed, they identify many more errors where the machine is confident it is correct. Further, the cases the humans identify seem to be not simply outliers, butcoherent areas missed completely by the model. Beat the machine identifies the ``unknown unknowns.''

The strategies seem designed to mitigate guilt over the parents' role in their The GenIE Assistant is an implemented proof-of-concept child's inheritance of a genetic condition. The names used computational model of normative biomedical argument to refer to the strategies in this paper and examples of generation informed by study of a corpus of letters each are listed below. All four apply to cases of written by genetic counselors to their clients (Green et al. autosomal recessive inheritance, while only the first two 2011). The goal of the model is to generate transparent apply to cases of autosomal dominant inheritance.

This paper describes the development of a corpus of blog posts that are annotated for the presence of attempts to persuade and corresponding tactics employed in persuasive messages. We investigate the feasibility of classifying blog posts as persuasive or non-persuasive on the basis of lexical features in the text and the tactics (as provided by human annotators). Annotated tactics provide substantial assistance in classifying persuasion, particularly tactics indicating formal reasoning, deontic obligation, and discussions of possible outcomes, suggesting that learning to identify tactics may be an excellent first step to detecting attempts to persuade.

In this paper, a practical example of implemented high abstraction-level control of mobile robot is presented. A method to represent abstract plans is shown along with a mechanism to schedule the actions within the plans for concurrent execution. Furthermore, a mechanism to consider contingencies and dynamic environment is explained.

This paper shows an example application of the LTLMoP mission planning toolkit, in which an Aldebaran Nao and an iRobot Create each play a very basic game of hide-and-seek.

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 an original integration of high level planning and execution with incoming perceptual information from vision, SLAM, topological map segmentation and dialogue. The task of the robot system, implementing the integrated model, is to explore unknown areas and report detected objects to an operator, by speaking loudly. The knowledge base of the planner maintains a graph-based representation of the metric map that is dynamically constructed via an unsupervised topological segmentation method, and augmented with information about the type and position of detected objects, within the map, such as cars or containers. According to this knowledge the cognitive robot can infer strategies in so generating parametric plans that are instantiated from the perceptual processes. Finally, a model-based approach for the execution and control of the robot system is proposed to monitor, concurrently, the low level status of the system and the execution of the activities, in order to achieve the goal, instructed by the operator.

The task of coordinating hundreds of mobile robots in one of Kiva System's warehouses presents many challenging multi-agent resource allocation problems. The resources include things like inventory, open orders, small shelving units, and the robots themselves. The types of resources can be classified by whether they are consumable, recycled, or scheduled. Further, the global optimization problem can be broken down into more manageable sub-problems, some of which map to (hard) versions of well known computational problems, but with a dynamic, temporal twist.

Mainly, we need kinds of virtualization technologies to offer on-demand to identify what parts of the model are really important and computing resources. There is widespread consensus that what other parts are secondary. Then, we would like to study the Future Internet will be heavily based on some kind of the scalability of the current models and identify the current successful Cloud technology. However, to master the deployment limits. Therefore, we propose to consider all existing of Cloud-based infrastructures, some hard scientific CP models in order to answer to these questions.