Key challenges to widespread deployment of mobile robots to interact with humans in real-world domains include the ability to: (a) robustly represent and revise domain knowledge; (b) autonomously adapt sensing and processing to the task at hand; and (c) learn from unreliable high-level human feedback. Partially observable Markov decision processes (POMDPs) have been used to plan sensing and navigation in different application domains. It is however a challenge to include common sense knowledge obtained from sensory or human inputs in POMDPs. In addition, information extracted from sensory and human inputs may have varying levels of relevance to current and future tasks. On the other hand, although a non-monotonic logic programming paradigm such as Answer Set Programming (ASP) is wellsuited for common sense reasoning, it is unable to model the uncertainty in real-world sensing and navigation (Gelfond 2008). This paper presents a hybrid framework that integrates ASP, hierarchical POMDPs (Zhang and Sridharan 2012) and psychophysics principles to address the challenges stated above. Experimental results in simulation and on mobile robots deployed in indoor domains show that the framework results in reliable and efficient operation.

However, this approach usually requires expert users to write individual transformations for each data source manually. Figure 2: Delete grammar A variety of work (Kandel et al. 2011; Raman and Hellerstein 2001; Liang, Jordan, and Klein 2010) tries to take advantage of user input to solve the transformation problem, cally learn transformation rules through examples. As shown but these methods either cannot learn rules from training in Figure 1, a user might want to reverse the order of the date data or need the training data to contain all the intermediate and use hyphens to replace slashes. The user would just provide steps. We have developed an approach where the user the system with an example "30/07/2010" and "2010-only needs to provide the target value as an example.

We are developing a testbed for learning by demonstration combining spoken language and sensor data in a natural real-world environment. Microsoft Kinect RGB-Depth cameras allow us to infer high-level visual features, such as the relative position of objects in space, with greater precision and less training than required by traditional systems. Speech is recognized and parsed using a “deep” parsing system, so that language features are available at the word, syntactic, and semantic levels. We collected an initial data set of 10 episodes of 7 individuals demonstrating how to “make tea”, and created a “gold standard” hand annotation of the actions performed in each. Finally, we are constructing “baseline” HMM-based activity recognition models using the visual and language features, in order to be ready to evaluate the performance of our future work on deeper and more structured models.

This work presents a new algorithm based on the Bucket Elimination framework that efficiently determines strong controllability of temporal plans formulated as Labeled Simple Temporal Networks with Uncertainty (LSTNU) with controllable and uncontrollable plan branches (choices).

The Nonnegative Least Squares (NNLS) formulation arises in many important regression problems. We present a novel coordinate descent method which differs from previous approaches in that we do not explicitly maintain complete gradient information. Empirical evidence shows that our approach outperforms a state-of-the-art NNLS solver in computation time for calculating radiation dosage for cancer treatment problems.

A person demonstrates observed, and this technique is acceptable. However, the domains how to perform a task, such as making tea, by describing of these research efforts could be expanded if new the actions he or she carries out in front of the camera objects could be identified by their mention in descriptive and Kinect. RFID tags are placed on all relevant objects text, without any prior knowledge or mapping of the object that can accept them, and the subject wears an iBracelet on instance to a concept.

In recent times, crowdsourcing over social networks has emerged as an active tool for complex task execution. In this paper, we address the problem faced by a planner to incentivize agents in the network to execute a task and also help in recruiting other agents for this purpose. We study this mechanism design problem under two natural resource optimization settings: (1) cost critical tasks, where the planner's goal is to minimize the total cost, and (2) time critical tasks, where the goal is to minimize the total time elapsed before the task is executed. We define a set of fairness properties that should be ideally satisfied by a crowdsourcing mechanism. We prove that no mechanism can satisfy all these properties simultaneously. We relax some of these properties and define their approximate counterparts. Under appropriate approximate fairness criteria, we obtain a non-trivial family of payment mechanisms. Moreover, we provide precise characterizations of cost critical and time critical mechanisms.

Human-agent interaction for learning with instruction can would involve pointing the tank in at the enemy tank be viewed on a continuum of instructor/agent control. The environment is partially observable to the instructor or imitation. The other extreme of the continuum is and the task is unknown to the agent, necessitating mixed occupied by systems where instructor interaction is limited initiative, bidirectional information transfer. Our agents are instantiated in Soar (Laird, 2008), a To be able to maintain the state of interactions with the symbolic, cognitive architecture based on the problemspace instructor while acting in the environment, and to be able to hypothesis. A Soar agent's current state is derived learn from these instructions in the context they were from its perceptions, its beliefs about the world and provided in, the agent needs a model of task-oriented knowledge in its long-term memories and is held in its interaction.

Geotagged photos of users on social media site, i.e., Flickr provide plentiful location-based data, which has been exploited for location-based services, such as mapping geotags to places and recommendation of personalized landmarks. As users’ preferences to visit a location or multiple locations in a certain sequence could be affected by their current temporal, and weather context. This paper considers the problem of mining context-aware significant semantic travel sequences from geotagged photos.

In this work, we build a large scale reasoning engine under temporal RDFS semantics using MapReduce. We identify the major challenges of applying MapReduce framework to reason over temporal information, and present our solutions to tackle them.