We consider the problem of belief tracking in a planning setting where states are valuations over a set of variables that are partially observable, and beliefs stand for the sets of states that are possible. While the problem is intractable in the worst case, it has been recently shown that in deterministic conformant and contingent problems, belief tracking is exponential in a width parameter that is often bounded and small. In this work, we extend these results in two ways. First, we introduce a width notion that applies to non-deterministic problems as well, develop a factored belief tracking algorithm that is exponential in the problem width, and show how it applies to existing benchmarks. Second, we introduce a meaningful, powerful, and sound approximation scheme, beam tracking, that is exponential in a smaller parameter, the problem causal width, and has much broader applicability. We illustrate the value of this algorithm over large instances of problems such as Battleship, Minesweeper, and Wumpus, where it yields state-of-the-art performance in real-time.

A key challenge in creating a sustainable and energy-efficient society is to make consumer demand adaptive to the supply of energy, especially to the renewable supply. In this article, we propose a partially-centralized organization of consumers (or agents), namely, a consumer cooperative that purchases electricity from the market. In the cooperative, a central coordinator buys the electricity for the whole group. The technical challenge is that consumers make their own demand decisions, based on their private demand constraints and preferences, which they do not share with the coordinator or other agents. We propose a novel multiagent coordination algorithm, to shape the energy demand of the cooperative. To coordinate individual consumers under incomplete information, the coordinator determines virtual price signals that it sends to the consumers to induce them to shift their demands when required. We prove that this algorithm converges to the central optimal solution and minimizes the electric energy cost of the cooperative. Additionally, we present results on the time complexity of the iterative algorithm and its implications for agents' incentive compatibility. Furthermore, we perform simulations based on real world consumption data to (a) characterize the convergence properties of our algorithm and (b) understand the effect of differing demand characteristics of participants as well as of different price functions on the cost reduction. The results show that the convergence time scales linearly with the agent population size and length of the optimization horizon. Finally, we observe that as participants' flexibility of shifting their demands increases, cost reduction increases and that the cost reduction is not sensitive to variation in consumption patterns of the consumers.

Abstract--This paper presents a new classification methods for Event Related Potentials (ERP) based on an Information geometry framework. Through a new estimation of covariance matrices, this work extend the use of Riemannian geometry, which was previously limited to SMR-based BCI, to the problem of classification of ERPs. As compared to the state-of-the-art, this new method increases performance, reduces the number of data needed for the calibration and features good generalisation across sessions and subjects. This method is illustrated on data recorded with the P300-based game brain invaders. Finally, an online and adaptive implementation is described, where the BCI is initialized with generic parameters derived from a database and continiously adapt to the individual, allowing the user to play the game without any calibration while keeping a high accuracy. So far we have conceived a Brain-Computer Interface (BCI) as a learning machine where the classifier is trained in a calibration phase preceding immediately the actual BCI use [1]. Depending on the BCI paradigm and on the efficiency of the classifier, the calibration phase may last from a few to several minutes. Regardless the duration, the very necessity of a calibration session reduces drastically the usability and appealing of a BCI. This is true both for clinically-oriented BCI, where the cognitive skills of patients are often limited and are wasted in the calibration phase, and for healthy users where the plug&play operation is nowadays considered as a minimum requirement for any consumer interfaces and devices. Besides the essential considerations from the user perspective, it appears evident that training the BCI at the beginning of each session and discarding the calibration data at the end is a very inefficient way to proceed. The problem we pose here is: can we design a "plug&play" BCI? Of course, such a goal does not imply that the BCI is not calibrated.

Overlapping Coalition Formation (OCF) games, introduced by Chalkiadakis, Elkind, Markakis, Polukarov and Jennings in 2010, are cooperative games where players can simultaneously participate in several coalitions. Capturing the notion of stability in OCF games is a difficult task:deviating players may continue to contribute resources to joint projects with non-deviators, and the crucial question is what payoffs the deviators expect to receive from such projects. Chalkiadakis et al. introduce three stability concepts for OCF games---the conservative core, the refined core, and the optimistic core---that are based on different answers to this question. In this paper, we propose a unified framework for the study of stability in the OCF setting, which encompasses the stability concepts considered by Chalkiadakis et al. as well as a wide variety of alternative stability concepts. Our approach is based on the notion of arbitration functions, which determine the payoff obtained by the deviators, given their deviation and the current allocation of resources. We provide a characterization of stable outcomes under arbitration. We then conduct an in-depth study of four types of arbitration functions, which correspond to four notions of the core; these include the three notions of the core considered by Chalkiadakis et al. Our results complement those of Chalkiadakis et al. and answer questions left open by their work. In particular, we show that OCF games with the conservative arbitration function are essentially equivalent to non-OCF games, by relating the conservative core of an OCF game to the core of a non-overlapping cooperative game, and use this result to obtain a strictly weaker sufficient condition for conservative core non-emptiness than the one given by Chalkiadakis et al.

Subjective Well-being(SWB), which refers to how people experience the quality of their lives, is of great use to public policy-makers as well as economic, sociological research, etc. Traditionally, the measurement of SWB relies on time-consuming and costly self-report questionnaires. Nowadays, people are motivated to share their experiences and feelings on social media, so we propose to sense SWB from the vast user generated data on social media. By utilizing 1785 users' social media data with SWB labels, we train machine learning models that are able to "sense" individual SWB from users' social media. Our model, which attains the state-by-art prediction accuracy, can then be used to identify SWB of large population of social media users in time with very low cost.

Social intelligence in natural and artificial systems is usually measured by the evaluation of associated traits or tasks that are deemed to represent some facets of social behaviour. The amalgamation of these traits is then used to configure the intuitive notion of social intelligence. Instead, in this paper we start from a parametrised definition of social intelligence as the expected performance in a set of environments with several agents, and we assess and derive tests from it. This definition makes several dependencies explicit: (1) the definition depends on the choice (and weight) of environments and agents, (2) the definition may include both competitive and cooperative behaviours depending on how agents and rewards are arranged into teams, (3) the definition mostly depends on the abilities of other agents, and (4) the actual difference between social intelligence and general intelligence (or other abilities) depends on these choices. As a result, we address the problem of converting this definition into a more precise one where some fundamental properties ensuring social behaviour (such as action and reward dependency and anticipation on competitive/cooperative behaviours) are met as well as some other more instrumental properties (such as secernment, boundedness, symmetry, validity, reliability, efficiency), which are convenient to convert the definition into a practical test. From the definition and the formalised properties, we take a look at several representative multi-agent environments, tests and games to see whether they meet these properties.

Many applications in translational medicine require the understanding of how diseases progress through the accumulation of persistent events. Specialized Bayesian networks called monotonic progression networks offer a statistical framework for modeling this sort of phenomenon. Current machine learning tools to reconstruct Bayesian networks from data are powerful but not suited to progression models. We combine the technological advances in machine learning with a rigorous philosophical theory of causation to produce Polaris, a scalable algorithm for learning progression networks that accounts for causal or biological noise as well as logical relations among genetic events, making the resulting models easy to interpret qualitatively. We tested Polaris on synthetically generated data and showed that it outperforms a widely used machine learning algorithm and approaches the performance of the competing special-purpose, albeit clairvoyant algorithm that is given a priori information about the model parameters. We also prove that under certain rather mild conditions, Polaris is guaranteed to converge for sufficiently large sample sizes. Finally, we applied Polaris to point mutation and copy number variation data in Prostate cancer from The Cancer Genome Atlas (TCGA) and found that there are likely three distinct progressions, one major androgen driven progression, one major non-androgen driven progression, and one novel minor androgen driven progression.

Aircraft engine manufacturers collect large amount of engine related data during flights. These data are used to detect anomalies in the engines in order to help companies optimize their maintenance costs. This article introduces and studies a generic methodology that allows one to build automatic early signs of anomaly detection in a way that is understandable by human operators who make the final maintenance decision. The main idea of the method is to generate a very large number of binary indicators based on parametric anomaly scores designed by experts, complemented by simple aggregations of those scores. The best indicators are selected via a classical forward scheme, leading to a much reduced number of indicators that are tuned to a data set. We illustrate the interest of the method on simulated data which contain realistic early signs of anomalies.

Extreme learning machine (ELM) as a neural network algorithm has shown its good performance, such as fast speed, simple structure etc, but also, weak robustness is an unavoidable defect in original ELM for blended data. We present a new machine learning framework called LARSEN-ELM for overcoming this problem. In our paper, we would like to show two key steps in LARSEN-ELM. In the first step, preprocessing, we select the input variables highly related to the output using least angle regression (LARS). In the second step, training, we employ Genetic Algorithm (GA) based selective ensemble and original ELM. In the experiments, we apply a sum of two sines and four datasets from UCI repository to verify the robustness of our approach. The experimental results show that compared with original ELM and other methods such as OP-ELM, GASEN-ELM and LSBoost, LARSEN-ELM significantly improve robustness performance while keeping a relatively high speed.

Operating vehicles in adversarial environments between a recurring origin-destination pair requires new planning techniques. A two players zero-sum game is introduced. The goal of the first player is to minimize the expected casualties undergone by a convoy. The goal of the second player is to maximize this damage. The outcome of the game is obtained via a linear program that solves the corresponding minmax optimization problem over this outcome. Different environment models are defined in order to compute routing strategies over unstructured environments. To compare these methods for increasingly accurate representations of the environment, a grid-based model is chosen to represent the environment and the existence of a sufficient network size is highlighted. A global framework for the generation of realistic routing strategies between any two points is described. This framework requires a good assessment of the potential casualties at any location, therefore the most important parameters are identified. Finally the framework is tested on real world environments.