Adaptive exploration uses active learning principles to improve the efficiency of autonomous robotic surveys. This work considers an important and understudied aspect of autonomous exploration: in situ validation of remote sensing measurements. We focus on high- dimensional sensor data with a specific case study of spectroscopic mapping. A field robot refines an orbital image by measuring the surface at many wavelengths. We introduce a new objective function based on spectral unmixing that seeks pure spectral signatures to accurately model diluted remote signals. This objective reflects physical properties of the multi-wavelength data. The rover visits locations that jointly improve its model of the environment while satisfying time and energy constraints. We simulate exploration using alternative planning approaches, and show proof of concept results with the canonical spectroscopic map of a mining district in Cuprite, Nevada.

A scoring rule is a device for eliciting and assessing probabilistic forecasts from an agent. When dealing with continuous outcome spaces, and absent any prior insights into the structure of the agent's beliefs, the rule should allow for a flexible reporting interface that can accurately represent complicated, multi-modal distributions. In this paper, we provide such a scoring rule based on a nonparametric approach of eliciting a set of samples from the agent and efficiently evaluating the score using kernel methods. We prove that sampled reports of increasing size converge rapidly to the true score, and that sampled reports are approximately optimal. We also demonstrate a connection between the scoring rule and the maximum mean discrepancy divergence. Experimental results are provided that confirm rapid convergence and that the expected score correlates well with standard notions of divergence, both important considerations for ensuring that agents are incentivized to report accurate information.

Modern society is critically dependent on the services provided by engineered infrastructure networks. When natural disasters (e.g. Hurricane Sandy) occur, the ability of these networks to provide service is often degraded because of physical damage to network components. One of the most critical of these networks is the electrical distribution grid, with medium voltage circuits often suffering the most severe damage. However, well-placed upgrades to these distribution grids can greatly improve post-event network performance. We formulate an optimal electrical distribution grid design problem as a two-stage, stochastic mixed-integer program with damage scenarios from natural disasters modeled as a set of stochastic events. We develop and investigate the tractability of an exact and several heuristic algorithms based on decompositions that are hybrids of techniques developed by the AI and operations research communities. We provide computational evidence that these algorithms have significant benefits when compared with commercial, mixed-integer programming software.

Peak demand for electricity continues to surge around the world. The supply-demand imbalance manifests itself in many forms, from rolling brownouts in California to power cuts in India. It is often suggested that exposing consumers to real-time pricing, will incentivize them to change their usage and mitigate the problem - akin to increasing tolls at peak commute times. We show that risk-averse consumers of electricity react to price fluctuations by scaling back on their total demand, not just their peak demand, leading to the unintended consequence of an overall decrease in production/consumption and reduced economic efficiency. We propose a new scheme that allows homes to move their demands from peak hours in exchange for greater electricity consumption in non-peak hours - akin to how airlines incentivize a passenger to move from an over-booked flight in exchange for, say, two tickets in the future. We present a formal framework for the incentive model that is applicable to different forms of the electricity market. We show that our scheme not only enables increased consumption and consumer social welfare but also allows the distribution company to increase profits. This is achieved by allowing load to be shifted while insulating consumers from real-time price fluctuations. This win-win is important if these methods are to be embraced in practice.

The power output of photovoltaic systems (PVS) increases with the use of effective and efficient solar tracking techniques. However, current techniques suffer from several drawbacks in their tracking policy: (i) they usually do not consider the forecasted or prevailing weather conditions; even when they do, they (ii) rely on complex closed-loop controllers and sophisticated instruments; and (iii) typically, they do not take the energy consumption of the trackers into account. In this paper, we propose a policy iteration method (along with specialized variants), which is able to calculate near-optimal trajectories for effective and efficient day-ahead solar tracking, based on weather forecasts coming from on-line providers. To account for the energy needs of the tracking system, the technique employs a novel and generic consumption model. Our simulations show that the proposed methods can increase the power output of a PVS considerably, when compared to standard solar tracking techniques.

Energy disaggregation, the task of taking a whole home electricity signal and decomposing it into its component appliances, has been proved to be essential in energy conservation research. One powerful cue for breaking down the entire household's energy consumption is user's daily energy usage behavior, which has so far received little attention: existing works on energy disaggregation mostly ignored the relationship between the energy usages of various appliances across different time slots. To model such relationship, we combine topic models with Hawkes processes, and propose a novel probabilistic model based on marked Hawkes process that enables the modeling of marked event data. The proposed model seeks to capture the influence from the occurrence and the marks of one usage event to the occurrence and the marks of subsequent usage events in the future. We also develop an inference algorithm based on variational inference for model parameter estimation. Experimental results on both synthetic data and three real world data sets demonstrate the effectiveness of our model, which outperforms state-of-the-art approaches in decomposing the entire consumed energy to each appliance. Analyzing the influence captured by the proposed model provides further insights into numerous interesting energy usage behavior patterns.

We address the problem of power system restoration after a significant blackout. Prior work focus on optimization methods for finding high-quality restoration plans. Optimal solutions consist in a sequence of grid repairs and corresponding steady states. However, such approaches lack formal guarantees on the transient stability of restoration actions, a key property to avoid additional grid damage and cascading failures. In this paper, we show how to integrate transient stability in the optimization procedure by capturing the rotor dynamics of power generators. Our approach reasons about the differential equations describing the dynamics and their underlying transient states. The key contribution lies in modeling and solving optimization problems that return stable generators dispatch minimizing the difference with respect to steady states solutions. Computational efficiency is increased using preprocessing procedures along with traditional reduction techniques. Experimental results on existing benchmarks confirm the feasibility of the new approach.

Identifying important components or factors in large amounts of noisy data is a key problem in machine learning and data mining. Motivated by a pattern decomposition problem in materials discovery, aimed at discovering new materials for renewable energy, e.g. for fuel and solar cells, we introduce CombiFD, a framework for factor based pattern decomposition that allows the incorporation of a-priori knowledge as constraints, including complex combinatorial constraints. In addition, we propose a new pattern decomposition algorithm, called AMIQO, based on solving a sequence of (mixed-integer) quadratic programs. Our approach considerably outperforms the state of the art on the materials discovery problem, scaling to larger datasets and recovering more precise and physically meaningful decompositions. We also show the effectiveness of our approach for enforcing background knowledge on other application domains.

Renewable power sources such as wind and solar are inflexible in their energy production, which requires demand to rapidly follow supply in order to maintain energy balance. Promising controllable demands are air-conditioners and heat pumps which use electric energy to maintain a temperature at a setpoint. Such Thermostatically Controlled Loads (TCLs) have been shown to be able to follow a power curve using reactive control. In this paper we investigate the use of planning under uncertainty to pro-actively control an aggregation of TCLs to overcome temporary grid imbalance. We present a formal definition of the planning problem under consideration, which we model using the Multi-Agent Markov Decision Process (MMDP) framework. Since we are dealing with hundreds of agents, solving the resulting MMDPs directly is intractable. Instead, we propose to decompose the problem by decoupling the interactions through arbitrage. Decomposition of the problem means relaxing the joint power consumption constraint, which means that joining the plans together can cause overconsumption. Arbitrage acts as a conflict resolution mechanism during policy execution, using the future expected value of policies to determine which TCLs should receive the available energy. We experimentally compare several methods to plan with arbitrage, and conclude that a best response-like mechanism is a scalable approach that returns near-optimal solutions.

Bayesian optimization is a sample-efficient method for black-box global optimization. How- ever, the performance of a Bayesian optimization method very much depends on its exploration strategy, i.e. the choice of acquisition function, and it is not clear a priori which choice will result in superior performance. While portfolio methods provide an effective, principled way of combining a collection of acquisition functions, they are often based on measures of past performance which can be misleading. To address this issue, we introduce the Entropy Search Portfolio (ESP): a novel approach to portfolio construction which is motivated by information theoretic considerations. We show that ESP outperforms existing portfolio methods on several real and synthetic problems, including geostatistical datasets and simulated control tasks. We not only show that ESP is able to offer performance as good as the best, but unknown, acquisition function, but surprisingly it often gives better performance. Finally, over a wide range of conditions we find that ESP is robust to the inclusion of poor acquisition functions.