Residential and commercial buildings are responsible for about 40% of primary energy consumption in the United States, hence improving their energy efficiency could have important sustainability benefits. The design of a building has tremendous effect on its energy profile, and recently there has been an increased interest in developing optimization methods that support the design of high performance buildings. Previous approaches are either based on simulation optimization or on training an accurate predictive model that is queried during the optimization. We propose a method that more tightly integrates the machine learning and optimization components, by employing active learning during optimization. In particular, we use a Gaussian Process (GP) model for the prediction and active learning and multi-objective genetic algorithm NSGA-II for the optimization. We develop a comprehensive and publicly available benchmark for building design optimization. We evaluate 5 machine learning approaches on our dataset, and show that the GP model is competitive, in addition to being well-suited for the active learning setting. We compare our optimization approach against the 2-stage approach and simulation optimization. Our results show that our approach produces solutions at the Pareto frontier compared to the other two approaches, while using only a fraction of the simulations and time.

Many experts believe that in the coming decades, artificial intelligence will change, and perhaps significantly reduce, the demand for human labor in the economy, but there remains much uncertainty about the accuracy of this claim and what to do about it. This paper identifies several ways in which the artificial intelligence community can help society to anticipate and shape such outcomes in a socially beneficial direction. First, different technical aspirations for the field of AI may be associated with different social outcomes, increasing the stakes of decisions made in the AI community. Second, the extent of researchers' efforts to apply AI to different social and economic domains will influence the distribution of cognition between humans and machines in those domains. Third, the AI community can play a key role in initiating a more nuanced and inclusive public discussion of the social and economic possibilities afforded by AI technologies. To pave the way for such dialogue, we suggest a line of research aimed at better understanding the nature, pace, and drivers of progress in AI in order to more effectively anticipate and shape AI's role in society.

The cultural and political implications of modern AI research are not some far off concern, they are things that affect the world in the here and now. From advanced control systems with advanced visualizations and image processing techniques that drive the machines of the modern military to the slow creep of a mechanized workforce, ethical questions surround us. Part of dealing with these ethical questions is not just speculating on what could be but teaching our students how to engage with these ethical questions. We explore the use of science fiction as an appropriate tool to enable AI researchers to help engage students and the public on the current state and potential impacts of AI.

We analyze the results of assistance to individuals with autism in reasoning about mental world. This assistance is provided by a natural language multiagent simulator of mental states, NL_MAMS (Galitsky 2013b). It assists in the tasks which are the hardest for autistic reasoning: operating with mental states and actions. Autistic patients are trained to perform a number of reasoning exercises. We conduct both short term and long term evaluations including the behavior in real world and confirm that the system has a positive effect on their rehabilitation.

One approach to conserving endangered species is to purchase and protect a set of land parcels in a way that maximizes the expected future population spread. Unfortunately, an ideal set of parcels may have a cost that is beyond the immediate budget constraints and must thus be purchased incrementally. This raises the challenge of deciding how to schedule the parcel purchases in a way that maximizes the flexibility of budget usage while keeping population spread loss in control. In this paper, we introduce a formulation of this scheduling problem that does not rely on knowing the future budgets of an organization. In particular, we consider scheduling purchases in a way that achieves a population spread no less than desired but delays purchases as long as possible. Such schedules offer conservation planners maximum flexibility and use available budgets in the most efficient way. We develop the problem formally as a stochastic optimization problem over a network cascade model describing a commonly used model of population spread. Our solution approach is based on reducing the stochastic problem to a novel variant of the directed Steiner tree problem, which we call the set-weighted directed Steiner graph problem. We show that this problem is computationally hard, motivating the development of a primal-dual algorithm for the problem that computes both a feasible solution and a bound on the quality of an optimal solution. We evaluate the approach on both real and synthetic conservation data with a standard population spread model. The algorithm is shown to produce near optimal results and is much more scalable than more generic off-the-shelf optimizers. Finally, we evaluate a variant of the algorithm to explore the trade-offs between budget savings and population growth.

Maintaining an accurate set of beliefs in a partially observable scenario, particularly with respect to other agents operating in the same space, is a vital aspect of multiagent planning. We analyze how the beliefs of an agent can be updated for fast adaptivity to changes in the behavior of an unknown teammate. The main contribution of this paper is the empirical evaluation of an agent cooperating with a teammate whose goals change periodically. We test our approach in a collaborative multiagent domain where identification of goals is necessary for successful completion. The belief revision technique we propose outperforms the traditional approach in a majority of test cases. Additionally, our results suggest the ability to approximate a higher level model by utilizing a belief distribution over a set of lower level behaviors, particularly when the belief update strategy identifies changes in the behavior in a responsive manner.

The leading approach for computing strong game-theoretic strategies in large imperfect-information games is to first solve an abstracted version of the game offline, then perform a table lookup during game play. We consider a modification to this approach where we solve the portion of the game that we have actually reached in real time to a greater degree of accuracy than in the initial computation. We call this approach endgame solving. Theoretically, we show that endgame solving can produce highly exploitable strategies in some games; however, we show that it can guarantee a low exploitability in certain games where the opponent is given sufficient exploitative power within the endgame. Furthermore, despite the lack of a general worst-case guarantee, we describe many benefits of endgame solving. We present an efficient algorithm for performing endgame solving in large imperfect-information games, and present a new variance-reduction technique for evaluating the performance of an agent that uses endgame solving. Experiments on no-limit Texas Hold'em show that our algorithm leads to significantly stronger performance against the strongest agents from the 2013 AAAI Annual Computer Poker Competition.

An important aspect of health science is communicating research findings to the public. The media is a critical instrument in disseminating research. Yet the process by which a scientific article becomes “newsworthy” is not well understood. In this study, we use large-scale text analysis to characterize the content features of articles that are predictive of newsworthiness. We experiment with two novel corpora: (i) 28,910 articles from a di- verse range of biomedical and health journals, of which 1,343 were covered by the news agency Reuters, and (ii) 10,760 articles from the JAMA journals, of which 846 were given press releases by the journal editors. We show that media coverage can be predicted reasonably well: logistic regression achieves mean AUCs of 0.783 and 0.882 on the Reuters and JAMA datasets, respec- tively. We present and discuss interesting findings con- cerning the most predictive content features.

How can knowing the location of my friends be used to more accurately predict my location? This paper explores socially-aware location prediction under a particularly challenging setting where the underlying interactions and social network are unknown and must be inferred over continuous spatiotemporal data. Our method samples inferred network topology using a linear regression model to predict future individual locations. We present an in-depth empirical study comparing different network models and network sampling regimes under a bootstrapped sampling baseline. Furthermore, our qualitative analysis demonstrates the value of social information in population mobility modeling under our application’s challenges.

Systems biology, the study of the intricate, ramified, com-plex and interacting mechanisms underlying life, often proves too complex for unaided human understanding, even by groups of people working together. This difficulty is ex-acerbated by the high volume of publications in molecular biology. The Big C (‘C’ for Cyc) is a system designed to (semi-)automatically acquire, integrate, and use complex mechanism models, specifically related to cancer biology, via automated reading and a hyper-detailed refinement pro-cess resting on Cyc’s logical representations and powerful inference mechanisms. We aim to assist cancer research and treatment by achieving elements of biologist-level reason-ing, but with the scale and attention to detail that only com-puter implementations can provide.