Publications

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Agent-based modeling is commonly used for studying complex system properties emergent from interactions among many agents. We present a novel data-driven agent-based modeling framework applied to forecasting individual and aggregate residential rooftop solar adoption in San Diego county. Our first step is to learn a model of individual agent behavior from combined data of individual adoption characteristics and property assessment. We then construct an agent-based simulation with the learned model embedded in artificial agents, and proceed to validate it using a holdout sequence of collective adoption decisions. We demonstrate that the resulting agent-based model successfully forecasts solar adoption trends and provides a meaningful quantification of uncertainty about its predictions. We utilize our model to optimize two classes of policies aimed at spurring solar adoption: one that subsidizes the cost of adoption, and another that gives away free systems to low-income house-holds. We find that the optimal policies derived for the latter class are significantly more efficacious, whereas the policies similar to the current California Solar Initiative incentive scheme appear to have a limited impact on overall adoption trends.

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We present a Stackelberg game model of security in which the defender chooses a mitigation strategy that interdicts potential attack actions, and the attacker responds by computing an optimal attack plan that circumvents the deployed mitigations. First, we offer a general formulation for deterministic plan interdiction as a mixed-integer program, and use constraint generation to compute optimal solutions, leveraging state-of-the-art partial satisfaction planning techniques. We also present a greedy heuristic for this problem, and compare its performance with the optimal MILP-based approach. We then extend our framework to incorporate uncertainty about attacker's capabilities, costs, goals, and action execution uncertainty, and show that these extensions retain the basic structure of the deterministic plan interdiction problem. Introduction of more general models of planning uncertainty require us to model the attacker's problem as a general MDP, and demonstrate that the MDP interdiction problem can still be solved using the basic constraint generation framework. Copyright © 2013, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.

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Stackelberg games have been used in several deployed applications to allocate limited resources for critical infrastructure protection. These resource allocation strategies are randomized to prevent a strategic attacker from using surveillance to learn and exploit patterns in the allocation. Past work has typically assumed that the attacker has perfect knowledge of the defender's randomized strategy or can learn the defender's strategy after conducting a fixed period of surveillance. In consideration of surveillance cost, these assumptions are clearly simplistic since attackers may act with partial knowledge of the defender's strategies and may dynamically decide whether to attack or conduct more surveillance. In this paper, we propose a natural model of limited surveillance in which the attacker dynamically determine a place to stop surveillance in consideration of his updated belief based on observed actions and surveillance cost. We show an upper bound on the maximum number of observations the attacker can make and show that the attacker's optimal stopping problem can be formulated as a finite state space MDP. We give mathematical programs to compute optimal attacker and defender strategies. We compare our approaches with the best known previous solutions and experimental results show that the defender can achieve significant improvement in expected utility by taking the attacker's optimal stopping decision into account, validating the motivation of our work. Copyright © 2013, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.

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Stackelberg games increasingly influence security policies deployed in real-world settings. Much of the work to date focuses on devising a fixed randomized strategy for the defender, accounting for an attacker who optimally responds to it. In practice, defense policies are often subject to constraints and vary over time, allowing an attacker to infer characteristics of future policies based on current observations. A defender must therefore account for an attacker's observation capabilities in devising a security policy. We show that this general modeling framework can be captured using stochastic Stackelberg games (SSGs), where a defender commits to a dynamic policy to which the attacker devises an optimal dynamic response. We then offer the following contributions. 1) We show that Markov stationary policies suffice in SSGs, 2) present a finite-time mixed-integer non-linear program for computing a Stackelberg equilibrium in SSGs, and 3) present a mixed-integer linear program to approximate it. 4) We illustrate our algorithms on a simple SSG representing an adversarial patrolling scenario, where we study the impact of attacker patience and risk aversion on optimal defense policies. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

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Defender-Attacker Stackelberg games are the foundations of tools deployed for computing optimal patrolling strategies in adversarial domains such as the United states Federal Air Marshals Service and the United States Coast Guard, among others. In Stackelberg game models of these systems the attacker knows only the probability that each target is covered by the defender, but is oblivious to the detailed timing of the coverage schedule. In many real-world situations, however, the attacker can observe the current location of the defender and can exploit this knowledge to reason about the defender's future moves. We study Stackelberg security games in which the defender sequentially moves between targets, with moves constrained by an exogenously specified graph, while the attacker can observe the defender's current location and his (stochastic) policy concerning future moves. We offer five contributions: (1) We model this adversarial patrolling game (APG) as a stochastic game with special structure and present several alternative formulations that leverage the general nonlinear programming (NLP) approach for computing equilibria in zero-sum stochastic games. We show that our formulations yield significantly better solutions than previous approaches. (2) We extend the NLP formulation for APG allow for attacks that may take multiple time steps to unfold. (3) We provide an approximate MILP formulation that uses discrete defender move probabilities. (4) We experimentally demonstrate the efficacy of an NLP-based approach, and systematically study the impact of network topology on the results. (5) We extend our model to allow the defender to construct the graph constraining his moves, at some cost, and offer novel algorithms for this setting, finding that a MILP approximation is much more effective than the exact NLP in this setting. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

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The goal of this research was to investigate the potential for employing dynamic, decentralized software architectures to achieve reliability in future high-performance computing platforms. These architectures, inspired by peer-to-peer networks such as botnets that already scale to millions of unreliable nodes, hold promise for enabling scientific applications to run usefully on next-generation exascale platforms ({approx} 10{sup 18} operations per second). Traditional parallel programming techniques suffer rapid deterioration of performance scaling with growing platform size, as the work of coping with increasingly frequent failures dominates over useful computation. Our studies suggest that new architectures, in which failures are treated as ubiquitous and their effects are considered as simply another controllable source of error in a scientific computation, can remove such obstacles to exascale computing for certain applications. We have developed a simulation framework, as well as a preliminary implementation in a large-scale emulation environment, for exploration of these 'fault-oblivious computing' approaches. High-performance computing (HPC) faces a fundamental problem of increasing total component failure rates due to increasing system sizes, which threaten to degrade system reliability to an unusable level by the time the exascale range is reached ({approx} 10{sup 18} operations per second, requiring of order millions of processors). As computer scientists seek a way to scale system software for next-generation exascale machines, it is worth considering peer-to-peer (P2P) architectures that are already capable of supporting 10{sup 6}-10{sup 7} unreliable nodes. Exascale platforms will require a different way of looking at systems and software because the machine will likely not be available in its entirety for a meaningful execution time. Realistic estimates of failure rates range from a few times per day to more than once per hour for these platforms. P2P architectures give us a starting point for crafting applications and system software for exascale. In the context of the Internet, P2P applications (e.g., file sharing, botnets) have already solved this problem for 10{sup 6}-10{sup 7} nodes. Usually based on a fractal distributed hash table structure, these systems have proven robust in practice to constant and unpredictable outages, failures, and even subversion. For example, a recent estimate of botnet turnover (i.e., the number of machines leaving and joining) is about 11% per week. Nonetheless, P2P networks remain effective despite these failures: The Conficker botnet has grown to {approx} 5 x 10{sup 6} peers. Unlike today's system software and applications, those for next-generation exascale machines cannot assume a static structure and, to be scalable over millions of nodes, must be decentralized. P2P architectures achieve both, and provide a promising model for 'fault-oblivious computing'. This project aimed to study the dynamics of P2P networks in the context of a design for exascale systems and applications. Having no single point of failure, the most successful P2P architectures are adaptive and self-organizing. While there has been some previous work applying P2P to message passing, little attention has been previously paid to the tightly coupled exascale domain. Typically, the per-node footprint of P2P systems is small, making them ideal for HPC use. The implementation on each peer node cooperates en masse to 'heal' disruptions rather than relying on a controlling 'master' node. Understanding this cooperative behavior from a complex systems viewpoint is essential to predicting useful environments for the inextricably unreliable exascale platforms of the future. We sought to obtain theoretical insight into the stability and large-scale behavior of candidate architectures, and to work toward leveraging Sandia's Emulytics platform to test promising candidates in a realistic (ultimately {ge} 10{sup 7} nodes) setting. Our primary example applications are drawn from linear algebra: a Jacobi relaxation solver for the heat equation, and the closely related technique of value iteration in optimization. We aimed to apply P2P concepts in designing implementations capable of surviving an unreliable machine of 10{sup 6} nodes.