Recognizing human activities from wearable sensor data is an important problem, particularly for health and eldercare applications. However, collecting sufficient labeled training data is challenging, especially since interpreting IMU traces is difficult for human annotators. Recently, crowdsourcing through services such as Amazon's Mechanical Turk has emerged as a promising alternative for annotating such data, with active learning serving as a natural method for affordably selecting an appropriate subset of instances to label. Unfortunately, since most active learning strategies are greedy methods that select the most uncertain sample, they are very sensitive to annotation errors (which corrupt a significant fraction of crowdsourced labels). This paper proposes methods for robust active learning under these conditions. Specifically, we make three contributions: 1) we obtain better initial labels by asking labelers to solve a related task; 2) we propose a new principled method for selecting instances in active learning that is more robust to annotation noise; 3) we estimate confidence scores for labels acquired from MTurk and ask workers to relabel samples that receive low scores under this metric. The proposed method is shown to significantly outperform existing techniques both under controlled noise conditions and in real active learning scenarios. The resulting method trains classifiers that are close in accuracy to those trained using ground-truth data.

Mobile robots are increasingly being used in the real-world due to the availability of high-fidelity sensors and sophisticated information processing algorithms. A key challenge to the widespread deployment of robots is the ability to accurately sense the environment and collaborate towards a common objective. Probabilistic sequential decision-making methods can be used to address this challenge because they encapsulate the partial observability and non-determinism of robot domains. However, such formulations soon become intractable for domains with complex state spaces that require real-time operation. Our prior work enabled a mobile robot to use hierarchical partially observable Markov decision processes (POMDPs) to automatically tailor visual sensing and information processing to the task at hand. This paper introduces adaptive observation functions and policy re-weighting in a three-layered POMDP hierarchy to enable reliable and efficient visual processing in dynamic domains. In addition, each robot merges its beliefs with those communicated by teammates, to enable a team of robots to collaborate robustly. All algorithms are evaluated in simulated domains and on physical robots tasked with locating target objects in indoor environments.

We describe work on representing and using a rich notion ofcontext that goes beyond current networking applications focusingmostly on location. Our context model includes locationand surroundings, the presence of people and devices,inferred activities and the roles people fill in them. A keyelement of our work is the use of collaborative informationsharing where devices share and integrate knowledge abouttheir context. This introduces a requirement that users canset appropriate levels of privacy to protect the personal informationbeing collected and the inferences that can be drawnfrom it. We use Semantic Web technologies to model contextand to specify high-level, declarative policies specifying informationsharing constraints. The policies involve attributesof the subject (i.e., information recipient), target (i.e., the information)and their dynamic context (e.g., are the parties copresent).We discuss our ongoing work on context representationand inference and present a model for protecting andcontrolling the sharing of private data in context-aware mobileapplications.

We present a framework that produces sentence-level summarizations of videos containing complex human activities that can be implemented as part of the Robot Perception Control Unit (RPCU). This is done via: 1) detection of pertinent objects in the scene: tools and direct-objects, 2) predicting actions guided by a large lexical corpus and 3) generating the most likely sentence description of the video given the detections. We pursue an active object detection approach by focusing on regions of high optical flow. Next, an iterative EM strategy, guided by language, is used to predict the possible actions. Finally, we model the sentence generation process as a HMM optimization problem, combining visual detections and a trained language model to produce a readable description of the video. Experimental results validate our approach and we discuss the implications of our approach to the RPCU in future applications.

Autonomous robots demand complex behavior to deal with unstructured environments. To meet these expectations, a robot needs to address a suite of problems associated with long term knowledge acquisition, representation, and execution in the presence of partial information. In this paper, we address these issues by the acquisition of broad, domain general skills using an intrinsically motivated reward function. We show how these skills can be represented compactly and used hierarchically to obtain complex manipulation skills. We further present a Bayesian model using the learned skills to model objects in the world, in terms of the actions they afford. We argue that our knowledge representation allows a robot to both predict the dynamics of objects in the world as well as recognize them.

Bayesian networks are a theoretically well-founded approach to represent large multi-variate probability distributions, and have proven useful in a broad range of applications. While several software tools for visualizing and editing Bayesian networks exist, they have important weaknesses when it comes to enabling users to clearly understand and compare conditional probability tables in the context of network topology, especially in large-scale networks. This paper describes a system for improving the ability for computers to work with people to develop intelligent systems through the construction of high-performing Bayesian networks. We describe NetEx, a tool developed as a Cytoscape plug-in, which allows a user to visually inspect and compare details concerning multiple nodes in a Bayesian network while maintaining awareness of their network context. It uses a "thought bubble line" to connect nodes in a graph representation and their internal information at the side of the graph. The tool seeks to improve the ability of experts to analyze and debug large Bayesian network models, and to help people to understand how alternative algorithms and Bayesian networks operate, providing insights into how to improve them.

Intelligent tutoring systems (ITSs) provide students with a one-on-one tutor, allowing them to work at their own pace, and helping them to focus on their weaker areas. The RUR1–Python Learning Environment (RUR-PLE), a game-like virtual environment to help students learn to program, provides an interface for students to write their own Python code and visualize the code execution (Roberge 2005). RUR-PLE provides a fixed sequence of learning lessons for students to explore. We are extending RUR-PLE to develop the Playing to Program (PtP) ITS, which consists of three components: (1) a Bayesian student model that tracks student competence, (2) a diagnosis module that provides tailored feedback to students, and (3) a problem selection module that guides the student’s learning process. In this paper, we summarize RUR-PLE and the PtP design, and describe an ongoing user study to evaluate the predictive accuracy of our student modeling approach.

In environmental and natural resource planning domains actions are taken at a large number of locations over multiple time periods. These problems have enormous state and action spaces, spatial correlation between actions, uncertainty and complex utility models. We present an approach for modeling these planning problems as factored Markov decision processes. The reward model can contain local and global components as well as spatial constraints between locations. The transition dynamics can be provided by existing simulators developed by domain experts. We propose a landscape policy defined as the equilibrium distribution of a Markov chain built from many locally-parameterized policies. This policy is optimized using a policy gradient algorithm. Experiments using a forestry simulator demonstrate the algorithm's ability to devise policies for sustainable harvest planning of a forest.

Redacting text documents has traditionally been a mostly manual activity, making it expensive and prone to disclosure risks. This paper describes a semi-automated system to ensure a specified level of privacy in text data sets. Recent work has attempted to quantify the likelihood of privacy breaches for text data. We build on these notions to provide a means of obstructing such breaches by framing it as a multi-class classification problem. Our system gives users fine-grained control over the level of privacy needed to obstruct sensitive concepts present in that data. Additionally, our system is designed to respect a user-defined utility metric on the data (such as disclosure of a particular concept), which our methods try to maximize while anonymizing. We describe our redaction framework, algorithms, as well as a prototype tool built in to Microsoft Word that allows enterprise users to redact documents before sharing them internally and obscure client specific information. In addition we show experimental evaluation using publicly available data sets that show the effectiveness of our approach against both automated attackers and human subjects.The results show that we are able to preserve the utility of a text corpus while reducing disclosure risk of the sensitive concept.

We propose an intelligent tutoring system that constructs a curriculum of hints and problems in order to teach a student skills with a rich dependency structure. We provide a template for building a multi-layered Dynamic Bayes Net to model this problem and describe how to learn the parameters of the model from data. Planning with the DBN then produces a teaching policy for the given domain. We test this end-to-end curriculum design system in two human-subject studies in the areas of finite field arithmetic and artificial language and show this method performs on par with hand-tuned expert policies.