Modeling occupant behavior in smart buildings to reduce energy usage in a more accurate fashion has garnered much recent attention in the literature. Predicting occupant comfort in buildings is a related and challenging problem. In some smart buildings, such as NASA AMES Sustainability Base, there are discrepancies between occupants' actual thermal discomfort and sensors based upon a weighted average of wet bulb, dry bulb, and mean radiant temperature intended to characterize thermal comfort. In this paper we attempt to find other contributing factors to occupant discomfort. For our experiment we use a dataset from a Building Automation System (BAS) in NASA Sustainability Base. We choose one conference room for our experiment and empirically establish the thermal discomfort level for the room's temperature sensor. We use various causality metrics and causal graphs to isolate candidate causes of the target room temperature. And we compare these feature sets according to their predictive capability of future instances of discomfort. Moreover, we establish a trade off between computational and statistical performance of adverse event prediction.

Staying up to date with the latest discoveries is a challenge in any scientific field. In planetary science, new observation targets on the surface of Mars are identified and named every day, and new publications announcing new discoveries and conclusions provide frequent updates about these targets. We are constructing a system that uses information extraction and retrieval methods to mine the steadily growing body of planetary science publications about Mars surface targets and automatically construct a concise summary of what is known about each target. The Mars Target Encyclopedia will provide a central, continually updated resource for use by planetary scientists and the interested public. We describe our use of Tika, Sundance, and AutoSlog to extract and summarize information, some of the challenges associated with this domain, and our plans for maturing the system.

Emerging smart buildings, such as the NASA Sustainability Base (SB), have a broad range of energy-related systems, including systems for heating and cooling. While the innovative technologies found in SB and similar smart buildings have the potential to increase the usage of renewable energy, they also add substantial technical complexity. Consequently, managing a smart building can be a challenge compared to managing a traditional building, sometimes leading to adverse events including unintended thermal discomfort of occupants (“too hot” or “too cold”). Fortunately, today’s smart buildings are typically equipped with thousands of sensors, controlled by Building Automation Systems (BASs). However, manually monitoring a BAS time series data stream with thousands of values may lead to information overload for the people managing a smart building. We present here a novel technique, Scalable Causal Learning (SCL), that integrates dimensionality reduction and Bayesian network structure learning techniques. SCL solves two problems associated with the naive application of dimensionality reduction and causal machine learning techniques to BAS time series data: (i) using autoregressive methods for causal learning can lead to induction of spurious causes and (ii) inducing a causal graph from BAS sensor data using existing graph structure learning algorithms may not scale to large data sets. Our novel SCL method addresses both of these problems. We test SCL using time series data from the SB BAS, comparing it with a causal graph learning technique, the PC algorithm. The causal variables identified by SCL are effective in predicting adverse events, namely abnormally low room temperatures, in a conference room in SB. Specifically, the SCL method performs better than the PC algorithm in terms of false alarm rate, missed detection rate and detection time.

Selecting homes with energy-efficient infrastructure is important for renters, because infrastructure influences energy consumption more than in-home behavior.Personalized energy estimates can guide prospective tenants toward energy-efficient homes, but this information is not readily available. Utility estimates are not typically offered to house-hunters, and existing technologies like carbon calculators require users to answer (prohibitively) many questions that may require considerable research to answer. For the task of providing personalized utility estimates to prospective tenants, we present a cost-based model for feature selection at training time, where all features are available and costs assigned to each feature reflect the difficulty of acquisition. At test time, we have immediate access to some features but others are difficult to acquire (costly). In this limited-information setting, we strategically order questions we ask each user, tailored to previous information provided, to give the most accurate predictions while minimizing the cost to users. During the critical first 10 questions that our approach selects, prediction accuracy improves equally to fixed order approaches, but prediction certainty is higher.

In order to properly handle a dangerous Artificially Intelligent (AI) system it is important to understand how the system came to be in such a state. In popular culture (science fiction movies/books) AIs/Robots became self-aware and as a result rebel against humanity and decide to destroy it. While it is one possible scenario, it is probably the least likely path to appearance of dangerous AI. In this work, we survey, classify and analyze a number of circumstances, which might lead to arrival of malicious AI. To the best of our knowledge, this is the first attempt to systematically classify types of pathways leading to malevolent AI. Previous relevant work either surveyed specific goals/meta-rules which might lead to malevolent behavior in AIs (Özkural 2014) or reviewed specific undesirable behaviors AGIs can exhibit at different stages of its development (Turchin July 10 2015a, Turchin July 10, 2015b).

In this paper we describe the unique challenges to the important problem of socio-cultural modeling of cyber-threat actors and why they necessitate further advances in artificial intelligence – particularly with regard to interdisciplinary efforts with the social sciences.

Utilities use Demand Response (DR) to balance supply and demand in the electric grid by involving customers in efforts to reduce electricity consumption during peak periods. To implement and adapt DR under dynamically changing conditions of the grid, reliable prediction of reduced consumption is critical. However, despite the wealth of research on electricity consumption prediction and DR being long in practice, the problem of reduced consumption prediction remains largely un-addressed. In this paper, we identify unique computational challenges associated with the prediction of reduced consumption and contrast this to that of normal consumption and DR baseline prediction. We propose a novel ensemble model that leverages different sequences of daily electricity consumption on DR event days as well as contextual attributes for reduced consumption prediction. We demonstrate the success of our model on a large, real-world, high resolution dataset from a university microgrid comprising of over 950 DR events across a diverse set of 32 buildings. Our model achieves an average error of 13.5%, an 8.8% improvement over the baseline. Our work is particularly relevant for buildings where electricity consumption is not tied to strict schedules. Our results and insights should prove useful to the researchers and practitioners working in the sustainable energy domain.

In the past few years various government funding organizations such as the U.S. National Institutes of Health and the U.S.\ National Science Foundation have provided access to large publicly-available online databases documenting the grants that they have funded over the past few decades. These databases provide an excellent opportunity for the application of statistical text analysis techniques to infer useful quantitative information about how funding patterns have changed over time. In this paper we analyze data from the National Cancer Institute (part of National Institutes of Health) and show how text classification techniques provide a useful starting point for analyzing how funding for cancer research has evolved over the past 20 years in the United States.

While it is empirically confirmed folklore that conjunctive normal form (CNF) is not the ideal input format for QBF solvers, most tool developers and therefore also the users focus on formulas in this restricted structure. One important factor for establishing non-CNF solving is the input format. To overcome drawbacks of available formats, the QCIR format has recently been presented. The QCIR format is a circuit-based input format for quantified Boolean formulas which supports structure sharing. In contrast to previous formats, the representation is very compact, yet still easy to parse and to read for the human user. In this paper, we analyze the QCIR format in detail and provide tools and benchmarks which, we hope, will make its usage attractive and motivate tool developers to support this format as well as users to formulate their encodings in this format.

Microsoft today announced that it is making it easier for developers to use its Computational Network Toolkit (CNTK) to build their own deep learning applications. The company first open sourced this toolkit in April 2015, but at the time, it was hosted on Microsoft's own CodePlex site and was only available under a restrictive academic license. Now, the team is moving the project to GitHub and to the MIT open source license. CNTK is an open-source deep-learning toolkit that became available back in April 2015. However, when it was still on CodePlex, it was restricted by an academic license, which means that it was virtually unused beyond scholarly use.