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.

The Smart Grid allows users to monitor power usage through the use of Smart Meter technology. In principle, this information can be used to modify usage habits in a way that reduces consumer costs as well as greenhouse emissions. However, in an urban environment, many users are restricted by the same constaints: they work during the day, and they are home at night. This creates spikes in power cost at peak usage times, and it may also lead to increased emissions in scenarios where sustainable resources are limited. An individual user can avoid these spikes by using an electric car as a storage device; it can be charged at the cheapest times, and then discharged to the home at the most expensive times. While this idea is intuitively appealing, it turns out that the benefits vary greatly depending on the storage algorithm used. In this paper, we describe the Power Storage Simulator, a tool for experimenting with storage algorithms to improve the efficiency of vehicle to grid systems. We suggest that this tool is also useful for educating power consumers about load balancing on the Smart Grid through an engaging, visual simulation.

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.

This paper focuses on a preliminary experimental protocol that aims at assessing a robot operator’s behavior when the robot is equipped with what appears as moral decision capabilities. The protocol is derived from the trolley dilemma, a well-known decision making paradigm. Indeed the participants, acting as operators of simulated aerial robots via a computer screen, are faced to impersonal moral dilemmas, i.e. decide to crash a damaged robot on one of two inhabited areas, and to non-moral choices, i.e. decide to crash a damaged robot on one of two uninhabited areas. In each situation, the robot has a default crash behavior which is displayed to the participant who will have to decide whether to follow it or not. The participants are equipped with fNIRS and eye-tracking and answer a post-experimental questionnaire. As some of the behavioral and physiological results do not match the hypotheses we had set, we give the features of the further experiments that we are planning.

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.

In this work we present a theoretical approach (not currently implemented), to the problem of predicting agent behavior. The ultimate goal of this work is to learn models that can be used to predict the future actions of intelligent agents, based on previously recorded data on those agents’ behavior. We believe that we can improve the predictive accuracy of our models by assuming that an agent reasons about the actions it takes, and trying to explicitly model that reasoning process. Here, we model an agent’s reasoning process as a form of Monte-Carlo search, and attempt to learn a state evaluation function that, when used with this planning algorithm, yields a similar distribution of actions given the current state of the world as we observe in the data. While it is simple to simulate Monte-Carlo search given an evaluation function, it is much more difficult to determine an evaluation function that will generate a certain behavior. Here we will use Expectation-Maximization to find a maximum likelihood estimate of the parameters of the evaluation function, treating the actual steps taken in planning each action as unobserved data.

While most work on trajectory mining is applied to pre- dict movements of mobile users, in this paper we consider a more general problem of building behavior models of users from their state-action trajectories. We assume that the user behavior can be compactly modeled as a Probabilistic State-Dependent Grammar (PSDG) which represents a hierarchical policy. The key problem is that while the states and actions of the user are directly observed, his intentional structure is not. We propose to learn the user’s policy from a set of selected trajectories and intention queries at selected states in the trajectory. Our main contributions are an algorithm for learning hierarchical policies from state-action trajectories, and principled heuristics for selecting suitable trajectories and intention queries. Experiments in multiple domains show that our approach is effective and more sample-efficient than learning non-hierarchical policies.