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.

Despite a dramatic growth of power consumption inhouseholds, less attention has been paid to monitoring,analyzing and predicting energy usage. In this paper,we propose a framework to mine raw energy data bytransforming time series energy data into a symbol se-quence, and then extend a suffix tree data structure asan efficient representation to analyze global structuralpatterns. Then, we use a clustering algorithm to detectenergy pattern outliers which are far from their clustercentroids. To validate our approach, we use real powerdata collected from a smart apartment testbed duringtwo months.

Automatic detection of persuasion is essential for machine interaction on the social web. To facilitate automated persuasion detection, we present a novel microtext corpus derived from hostage negotiation transcripts as well as a detailed manual (codebook) for persuasion annotation. Our corpus, called the NPS Persuasion Corpus, consists of 37 transcripts from four sets of hostage negotiation transcriptions. Each utterance in the corpus is hand annotated for one of nine categories of persuasion based on Cialdini’s model: reciprocity, commitment, consistency, liking, authority, social proof, scarcity, other, and not persuasive. Initial results using three supervised learning algorithms (Na ̈ve Bayes, Maximum Entropy, and Support Vector Machines) combined with gappy and orthogonal sparse bigram feature expansion techniques show that the annotation process did capture machine learnable features of persuasion with F-scores better than baseline.

Users are constantly involved in a multitude of activities in ever-changing context. Analyzing activities in context-rich environments has become a great challenge in context-awareness research. Traditional methods for activity recognition, such as classification, cannot cope with the variety and dynamicity of context and activities. In this paper, we propose an activity recognition approach that incorporates unsupervised learning. We analyze the feasibility of applying subspace clustering---a specific type of unsupervised learning — to high-dimensional, heterogeneous sensory input. Then we present the correspondence between clustering output and classification input. This approach has the potential to discover implicit, evolving activities, and can provide valuable assistance to traditional classification based methods.

Being able to detect and recognize human activities is important for making personal assistant robots useful in performing assistive tasks. The challenge is to develop a system that is low-cost, reliable in unstructured home settings, and also straightforward to use. In this paper, we use a RGBD sensor (Microsoft Kinect) as the input sensor, and present learning algorithms to infer the activities. Our algorithm is based on a hierarchical maximum entropy Markov model (MEMM). It considers a person's activity as composed of a set of sub-activities, and infers the two-layered graph structure using a dynamic programming approach. We test our algorithm on detecting and recognizing twelve different activities performed by four people in different environments, such as a kitchen, a living room, an office, etc., and achieve an average performance of 84.3% when the person was seen before in the training set (and 64.2% when the person was not seen before).

Recently, information gain has been proposed as a candidate intrinsic motivation for lifelong learning agents that may not always have a specific task.  In the InfoMax control framework, reinforcement learning is used to find a control policy for a POMDP in which movement and sensing actions are selected to reduce Shannon entropy as quickly as possible. In this study, we implement InfoMax control on a robot which can move between objects and perform sound-producing manipulations on them.  We formulate a novel latent variable mixture model for acoustic similarities and learn InfoMax polices that allow the robot to rapidly reduce uncertainty about the categories of the objects in a room. We find that InfoMax with our improved acoustic model leads to policies which lead to high classification accuracy.  Interestingly, we also find that with an insufficient model, the InfoMax policy eventually learns to "bury its head in the sand" to avoid getting additional evidence that might increase uncertainty.  We discuss the implications of this finding for InfoMax as a principle of intrinsic motivation in lifelong learning agents.

Skill discovery algorithms in reinforcement learning typically identify single states or regions in state space that correspond to potential task-specific subgoals. However, such methods do not directly address the question of how many distinct skills are appropriate for solving the tasks that the agent faces. This can be highly inefficient when many identified subgoals correspond to the same underlying skill, but are all used in- dividually as skill goals. Furthermore, skills created in this manner are often only transferable to tasks that share iden- tical state spaces, since corresponding subgoals across tasks are not merged into a single skill goal. We show that these problems can be overcome by clustering subgoal data defined in an agent-space and using the resulting clusters as templates for skill termination conditions. Clustering via a Dirichlet process mixture model is used to discover a minimal, suffi- cient collection of portable skills.

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.

Rather, the goal is to Microtext media (Ellen, 2011), such as SMS, IM, Twitter, gather relevant messages, organize them, and extract some and text chat, have in common that they use short strings other kind of useful information from them, such as how for immediate communication or broadcast. Microtext can well a team is performing or what people are talking about be construed as one form of micro-messaging (e.g., and when. However, micro-messages do not exist in a Milstein, et al., 2008) which we extend here to include any vacuum; they are contextually oriented and may be part of of a number of other modalities (e.g., telephone calls, a larger network of communications which includes email, face-to-face interaction) used for short, immediate and telephone and other media, including "macro-text." Given (potentially) persistent message passing among this, we have found that natural language processing of the coordinating agents. In this paper, we describe several microtext must be paired with temporal or network recent attempts to study micro-messaging military and analysis of the context. To demonstrate this process, we related organizational contexts.

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.