We define and propose a manifesto and an architecture for smart digital workspaces and consumer playspaces, that “know” what the user is doing (activity structure, context, goals), how are they doing it (methods), what resources are they using (allocation and discovery), when (time) and where (location, application, device) are they doing it, who are they (profile, history), what is their role (responsibility, security, privacy) and who are their collaborators (social network), all the while observing, recording this context of work and play (institutional and social tribal knowledge). These smart workspaces and playspaces to be developed in the next five years, will let the users seamlessly move between applications and devices without having to remember or copy what they did earlier (activity context transfer and exchange), proactively show them steps others took in meaningfully similar situations before (semantic task reasoning), quickly find and show them directly related information and present answers to questions based on what they mean (proactive semantic extraction and search), in the context they need it, with access to provenance, quality and derivation of information, connect them to insights of experts within the organization and beyond, helping them reason and decide faster, with greater confidence, within a framework for managing, semantically dividing, tracking and enabling distributed work. We report two examples of the application of this architecture: a patient care system in a hospital and an assisted living system.

This research proposes the use of imitation based learning to build collaborative strategies for a team of agents. Imitation based learning involves learning from an expert by observing her demonstrating a task and then replicating it. This mechanism makes it extremely easy for a knowledge engineer to transfer knowledge to a software agent via human demonstrations. This research aims to apply imitation to learn not only the strategy of an individual agent but also the collaborative strategy of a team of agents to achieve a common goal. The effectiveness of the proposed methodology is being assessed in the domain of RoboCup Soccer Simulation 3D which is a promising platform to address many of the complex real-world problems and offers a truly dynamic, stochastic, and partially-observable environment.

Much work in learning from demonstration has focused on learning simple tasks from structured demonstrations that have a well-defined beginning and end. As we attempt to scale robot learning to increasingly complex tasks, it becomes intractable to learn task policies monolithically. Furthermore, it is desirable to be able to learn from natural, unstructured demonstrations, which are unsegmented, possibly incomplete, and may come from different tasks. We propose a three-part approach to designing a natural, scalable system that allows a robot to learn tasks of increasing complexity by automatically building and refining a library of skills over time. First, we describe a Bayesian nonparametric model that can segment unstructured demonstrations into appropriate numbers of component skills and recognize repeated skills across demonstrations and tasks. These skills can then be parameterized and generalized to new situations. Second, we propose to create a system that allows the user to provide unstructured corrections and feedback to the robot, without requiring any knowledge of the robot's underlying representation of the task or its component skills. Third, we propose to infer the user's intentions for each segmented skill and autonomously improve these skills using reinforcement learning. This approach will be applied to learn and generalize complex, multi-step tasks that are beyond the reach of current LfD methods, using the PR2 mobile manipulator as a testing platform.

Group Recommendation Systems (GRS's) assist groups when trying to reach a joint decision. I use probabilistic data and apply voting theory to GRS’s in order to minimize user interaction and output an approximate or definite “winner item

My research seeks to answer the question of how any agent that is tasked with making sense of its world, by finding explanations for evidence (e.g., sensor reports) using domain-general strategies, may accurately and efficiently handle incomplete evidence, noisy evidence, and an incomplete knowledge base. I propose the following answer to the question. The agent should employ an optimal abductive reasoning algorithm (developed piece-wise and shown to be best in a class of similar algorithms) that allows it to reason from evidence to causes. For the sake of efficiency and operational concerns, the agent should establish beliefs periodically rather than waiting until it has obtained all evidence it will ever be able to obtain. If the agent commits to beliefs on the basis of incomplete or noisy evidence or an incomplete knowledge base, these beliefs may be incorrect. Future evidence obtained by the agent may result in failed predictions or anomalies. The agent is then tasked with determining whether it should retain its beliefs and therefore discount the newly-obtained evidence, revise its prior beliefs, or expand its knowledge base (what can be described as anomaly-driven or explanation-based learning). I have developed an abductive metareasoning procedure that aims to appropriately reason about these situations. Preliminary experiments in two reasoning tasks indicate that the procedure is effective.

A General Similarity Measurement (GSM), which takes into account of both non-temporal and rich temporal aspects including temporal order, temporal duration and temporal gap, is proposed for state-sequence matching. It is believed to be versatile enough to subsume representative existing measurements as its special cases.

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.

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.

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.