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Autonomous Agents and Multi-Agent Systems
Zhang, Haifeng; Vorobeychik, Yevgeniy V. ; Letchford, Joshua L. ; Lakkaraju, Kiran L.
Agent-based modeling is commonly used for studying complex system properties emergent from interactions among agents. However, agent-based models are often not developed explicitly for prediction, and are generally not validated as such. We therefore present a novel data-driven agent-based modeling framework, in which individual behavior model is learned by machine learning techniques, deployed in multi-agent systems and validated using a holdout sequence of collective adoption decisions. We apply the framework to forecasting individual and aggregate residential rooftop solar adoption in San Diego county and demonstrate that the resulting agent-based model successfully forecasts solar adoption trends and provides a meaningful quantification of uncertainty about its predictions. Meanwhile, we construct a second agent-based model, with its parameters calibrated based on mean square error of its fitted aggregate adoption to the ground truth. Our result suggests that our data-driven agent-based approach based on maximum likelihood estimation substantially outperforms the calibrated agent-based model. Seeing advantage over the state-of-the-art modeling methodology, we utilize our agent-based model to aid search for potentially better incentive structures aimed at spurring more solar adoption. Although the impact of solar subsidies is rather limited in our case, our study still reveals that a simple heuristic search algorithm can lead to more effective incentive plans than the current solar subsidies in San Diego County and a previously explored structure. Finally, we examine an exclusive class of policies that gives away free systems to low-income households, which are shown significantly more efficacious than any incentive-based policies we have analyzed to date.
Lakkaraju, Kiran L. ; Letchford, Joshua L. ; Vorobeychik, Yevgeniy V. ; Zhang, Haifeng
Lakkaraju, Kiran L. ; Vorobeychik, Yevgeniy V. ; Letchford, Joshua L.
Vorobeychik, Yevgeniy V.
Vorobeychik, Yevgeniy V.
Vorobeychik, Yevgeniy V.
Vorobeychik, Yevgeniy V.
Vorobeychik, Yevgeniy V.
Lloyd, Levi L. ; Fritz, David J. ; Erickson, Jeremy L. ; Choe, Yung R. ; Dirks, David H. ; Hulette, Geoffrey C. ; Armstrong, Robert C. ; Vorobeychik, Yevgeniy V. ; Deccio, Casey T.
Slattengren, Nicole S. ; Bennett, Janine C. ; Vorobeychik, Yevgeniy V. ; Floren, John F.
Vorobeychik, Yevgeniy V.
12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013
Letchford, Joshua; Vorobeychik, Yevgeniy V.
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.
12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013
An, Bo; Brown, Matthew; Vorobeychik, Yevgeniy V. ; Tambe, Milind
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.
Vorobeychik, Yevgeniy V.
Vorobeychik, Yevgeniy V.
Bennett, Janine C. ; Vorobeychik, Yevgeniy V. ; Slattengren, Nicole S. ; Floren, John F.
Vorobeychik, Yevgeniy V.
Vorobeychik, Yevgeniy V. ; Smith, Andrew M.
Vorobeychik, Yevgeniy V.
Proceedings of the National Conference on Artificial Intelligence
Vorobeychik, Yevgeniy V. ; Singh, Satinder
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.
Vorobeychik, Yevgeniy V. ; Erickson, Jeremy L. ; Hurd, Steven A. ; Cook, Benjamin K. ; Atkins, William D.
Vorobeychik, Yevgeniy V.
Vorobeychik, Yevgeniy V.
Vorobeychik, Yevgeniy V.
Vorobeychik, Yevgeniy V.
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