Solving games of imperfect information with linear programming took a significant leap forward when Koller, Megiddo, and Stengel (KMS) proposed an exponentially more compact way to represent two-player games of imperfect information as linear programs. Despite this substantial advancement many recent works on solving these games rely on Counter Factual Regret Minimization (CFR) as opposed to linear programming. One reason CFR became a standard approach is that CFR is easily parallelizable whereas the linear program defined by KMS's technique is difficult to solve in parallel. Convenient parallelism made CFR more amenable to multi-core computing environments and large games. This paper presents a method to parallelize the linear programing techniques of KMS. The proposed iterative method divides a potentially intractable linear program representing a large game of imperfect information into many smaller linear programs. Each of the smaller LPs can be processed independently and in parallel. It is shown that the solutions to the smaller LPs interact together over multiple iterations of this algorithm to produce a strategy pair that converges to the Nash Equilibrium solution to the original undivided problem. This is the first work to propose a Dantzig-Wolfe style decomposition for solving two-player games of imperfect information.

A unique behavior of humans is modifying one’s unobservable behavior based on the reaction of others for cooperation. We used a card game called Hanabi as an evaluation task of imitating human reflective intelligence with artificial intelligence. Hanabi is a cooperative card game with incomplete information. A player cooperates with an opponent in building several card sets constructed with the same color and ordered numbers. However, like a blind man's bluff, each player sees the cards of all other players except his/her own. Also, communication between players is restricted to information about the same numbers and colors, and the player is required to read his/his opponent's intention with the opponent's hand, estimate his/her cards with incomplete information, and play one of them for building a set. We compared human play with several simulated strategies. The results indicate that the strategy with feedbacks from simulated opponent's viewpoints achieves more score than other strategies.

Clustering agents by their behaviour can be crucial for building effective agent models. Traditional clustering typically aims to group entities together based on a distance metric, where a desirable clustering is one where the entities in a cluster are spatially close together. Instead, one may desire to cluster based on actionability, or the capacity for the clusters to suggest how an agent should respond to maximize their utility with respect to the entities. Segmentation problems examine this decision-theoretic clustering task. Although finding optimal solutions to these problems is computationally hard, greedy-based approximation algorithms exist. However, in settings where the agent has a combinatorially large number of candidate responses whose utilities must be considered, these algorithms are often intractable. In this work, we show that in many cases the utility function can be factored to allow for an efficient greedy algorithm even when there are exponentially large response spaces. We evaluate our technique theoretically, proving approximation bounds, and empirically using extensive-form games by clustering opponent strategies in toy poker games. Our results demonstrate that these techniques yield dramatically improved clusterings compared to a traditional distance-based clustering approach in terms of both subjective quality and utility obtained by responding to the clusters.

Generalized Additive Models (GAM) are a widely popular class of regression models to forecast electricity demand, due to their high accuracy, flexibility and interpretability. However, the residuals of the fitted GAM are typically heteroscedastic and leptokurtic caused by the nature of energy data. In this paper we propose a novel approach to estimate the time-varying conditional variance of the GAM residuals, which we call the GAM2 algorithm. It allows utility companies and network operators to assess the uncertainty of future electricity demand and incorporate it into their planning processes. The basic idea of our algorithm is to apply another GAM to the squared residuals to explain the dependence of uncertainty on exogenous variables. Empirical evidence shows that the residuals rescaled by the estimated conditional variance are approximately normal. We combine our modeling approach with online learning algorithms that adjust for dynamic changes in the distributions of demand. We illustrate our method by a case study on data from RTE, the operator of the French transmission grid.

Bike sharing systems consist of a fleet of bikes placed in a network of docking stations. These bikes can then be rented and returned to any of the docking stations after usage. Predicting unrealized bike demand at locations currently without bike stations is important for effectively designing and expanding bike sharing systems. We predict pairwise bike demand for New York City’s Citi Bike system. Since the system is driven by daily commuters we focus only on the morning rush hours between 7:00 AM to 11:00 AM during weekdays. We use taxi usage, weather and spatial variables as covariates to predict bike demand, and further analyze the influence of precipitation and day of week. We show that aggregating stations in neighborhoods can substantially improve predictions. The presented model can assist planners by predicting bike demand at a macroscopic level, between pairs of neighborhoods.

The problem with this type of method is that it does not really take advantage of Land use and land cover classification (LUCC) maps from high resolution images. We believe that pixel based spectral remote sensor data are of great interest since they allow to information is not enough to characterize land use and track issues like deforestation/reforestation, water sources land cover classes. For this reason, our goal is to design a reduction, urban growth, or to calculate indicators like a methodology that models classes as areas of correlated pixels.

While human behavior models based on repeated Stackelberg games have been proposed for domains such as "wildlife crime" where there is repeated interaction between the defender and the adversary, there has been no empirical study with human subjects to show the effectiveness of such models. This paper presents an initial study based on extensive human subject experiments with participants on Amazon Mechanical Turk (AMT). Our findings include: (i) attackers may view the defender’s coverage probability in a non-linear fashion; specifically it follows an S-shaped curve, and (ii) there are significant losses in defender utility when strategies generated by existing models are deployed in repeated Stackelberg game settings against human subjects.

The DNR is able to purchase lands and engage in conservation easements, but there is considerable uncertainty (for the Conservation reserve design is the problem of selecting reasons enumerated above) about which lands to target, and parcels of land such that the assembled set maximizes when. Furthermore, for any parcel that is protected through some criterion pertaining to the conservation of species or purchase or easement, DNR encumbers a responsibility to natural communities (Williams, ReVelle, and Levin 2005).

For a sustainable integration of wind power into the electricity grid, a precise prediction method is required. In this work, we investigate the use of heterogeneous machine learning ensembles for wind power prediction. We first analyze homogeneous ensemble regressors that make use of a single base algorithm and compare decision trees to k-nearest neighbors and support vector regression. As next step, we construct heterogeneous ensembles that make use of multiple base algorithms and benefit from a gain of diversity of the weak predictors. In the experimental evaluation, we show that a combination of decision trees and support vector regression outperforms state-of-the-art predictors (improvements of up to 37% compared to support vector regression) as well as homogeneous ensembles while requiring a shorter runtime (speed-ups from 1.60x to 8.78x). The experiments are based on large wind time series data from simulations and real measurements.

We propose to introduce social media opinion mining research into the field of computational sustainability. Opinion mining from social media can be a faster and less expensive alternative to traditional survey and polling, on which many sustainability research are based. We describe a framework for such analysis, examine the challenges in our proposed framework and current status of research on those challenges. We also propose some possible research directions for tackling these challenges.