In this paper, we focus on the problem of learning a Bayesian network over distributed data stored in a commodity cluster. Specifically, we address the challenge of computing the scoring function over distributed data in a scalable manner, which is a fundamental task during learning. We propose a novel approach designed to achieve: (a) scalable score computation using the principle of gossiping; (b) lower resource consumption via a probabilistic approach for maintaining scores using the properties of a Markov chain; and (c) effective distribution of tasks during score computation (on large datasets) by synergistically combining well-known hashing techniques. Through theoretical analysis, we show that our approach is superior to a MapReduce-style computation in terms of communication bandwidth. Further, it is superior to the batch-style processing of MapReduce for recomputing scores when new data are available.

Mobile and location-based social media applications provide platforms for users to share brief opinions about products, venues, and services. These quickly typed opinions, or microreviews, are a valuable source of current sentiment on a wide variety of subjects. However, there is currently little research on how to mine this information to present it back to users in easily consumable way. In this paper, we introduce the task of microsummarization, which combines sentiment analysis, summarization, and entity recognition in order to surface key content to users. We explore unsupervised and supervised methods for this task, and find we can reliably extract relevant entities and the sentiment targeted towards them using crowdsourced labels as supervision. In an end-to-end evaluation, we find our best-performing system is vastly preferred by judges over a traditional extractive summarization approach. This work motivates an entirely new approach to summarization, incorporating both sentiment analysis and item extraction for modernized, at-a-glance presentation of public opinion.

We present a probabilistic generative model for inferring a description of coordinated, recursively structured group activities at multiple levels of temporal granularity based on observations of individuals’ trajectories. The model accommodates: (1) hierarchically structured groups, (2) activities that are temporally and compositionally recursive, (3) component roles assigning different subactivity dynamics to subgroups of participants, and (4) a nonparametric Gaussian Process model of trajectories. We present an MCMC sampling framework for performing joint inference over recursive activity descriptions and assignment of trajectories to groups, integrating out continuous parameters. We demonstrate the model’s expressive power in several simulated and complex real-world scenarios from the VIRAT and UCLA Aerial Event video data sets.

Stock movements are essentially driven by new information. Market data, financial news, and social sentiment are believed to have impacts on stock markets. To study the correlation between information and stock movements, previous works typically concatenate the features of different information sources into one super feature vector. However, such concatenated vector approaches treat each information source separately and ignore their interactions. In this article, we model the multi-faceted investors’ information and their intrinsic links with tensors. To identify the nonlinear patterns between stock movements and new information, we propose a supervised tensor regression learning approach to investigate the joint impact of different information sources on stock markets. Experiments on CSI 100 stocks in the year 2011 show that our approach outperforms the state-of-the-art trading strategies.

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2011 Spring Symposium Series Monday through Wednesday, March 21–23, 2011 at Stanford University. The titles of the eight symposia were AI and Health Communication, Artificial Intelligence and Sustainable Design, AI for Business Agility, Computational Physiology, Help Me Help You: Bridging the Gaps in Human-Agent Collaboration, Logical Formalizations of Commonsense Reasoning, Multirobot Systems and Physical Data Structures, and Modeling Complex Adaptive Systems As If They Were Voting Processes.

The titles of the eight symposia were Artificial Intelligence and Health Communication, Artificial Intelligence and Sustainable Design, Artificial Intelligence for Business Agility, Computational Physiology, Help Me Help You: Bridging the Gaps in Human-Agent Collaboration, Logical Formalizations of Commonsense Reasoning, Multirobot Systems and Physical Data Structures, and Modeling Complex Adaptive Systems As If They Were Voting Processes. The goal of the Artificial Intelligence and Health Communication symposium was to advance the conceptual design of automated systems that provide health services to patients and consumers through interdisciplinary insight from artificial intelligence, health communication and related areas of communication studies, discourse studies, public health, and psychology. There is a large and growing interest in the development of automated systems to provide health services to patients and consumers. In the last two decades, applications informed by research in health communication have been developed, for example, for promoting healthy behavior and for managing chronic diseases. While the value that these types of applications can offer to the community in terms of cost, access, and convenience is clear, there are still major challenges facing design of effective health communication systems. Overall, the participants found the format of the symposium engaging and constructive, and they The symposium was organized around five main expressed the desire to continue this initiative in concepts: (1) Patient empowerment and education further events.

This paper describes a framework for an agent to learn verb-phrase meanings from human teachers and combine these models with environmental dynamics so the agent can enact verb commands from the human teacher. This style of human/agent interaction allows the human teacher to issue natural-language commands and demonstrate ground actions, thereby alleviating the need for advanced teaching interfaces or difficult goal encodings. The framework extends prior work in apprenticeship learning and builds off of recent advancements in learning to recognize activities and modeling domains with multiple objects. In our studies, we show how to both learn a verb model and turn it into reward and heuristic functions that can then be composed with a dynamics model. The resulting "combined model" can then be efficiently searched by a sample-based planner which determines a policy for enacting a verb command in a given environment. Our experiments with a simulated robot domain show this framework can be used to quickly teach verb commands that the agent can then enact in new environments.

Voting systems have complex and dynamic properties that are useful in modeling behavior in physical systems. The goal of this symposium is to bring together diverse perspectives on applications of voting analysis to share ideas about gaining theoretical insight and conducting experiments to increase our knowledge. Topics will include the study of social insects, risk analysis, voting theory, combining simulations of group behavior, personal genome analysis, business classification, robots, and man-machine interaction. Joint sessions on multirobot systems and human-agent collaboration are planned.

The challenge of using communications infrastructure to stabilize other infrastructures is related to research on the collective communications systems in social animals, robots, and human-non-human interaction. In these systems, voting models can explicate patterns of observed behavior or predict collective outcomes. Developing more theoretical deductive explanatory power can increase our knowledge about the interplay of voters and communication that produces collective inferences. This paper suggests that many analyses of voting patterns have not integrated what is known about the predictive properties of voting processes into their analyses. Taking a more deductive approach enables us to think about the strengths and weaknesses of existing explanations and imagine new types of analysis that have implications for engineering communications systems to stabilize other infrastructures.

Humans naturally use multiple modes of instruction while teaching one another. We would like our robots and artificial agents to be instructed in the same way, rather than programmed. In this paper, we review prior work on human instruction of autonomous agents and present observations from two exploratory pilot studies and the results of a full study investigating how multiple instruction modes are used by humans. We describe our Bootstrapped Learning User Interface, a prototype multiinstruction interface informed by our human-user studies.