Computer Science Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2756
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Item Model-Predictive Strategy Generation for Multi-Agent Pursuit-Evasion Games(2015) Raboin, Eric James; Nau, Dana S; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Multi-agent pursuit-evasion games can be used to model a variety of different real world problems including surveillance, search-and-rescue, and defense-related scenarios. However, many pursuit-evasion problems are computationally difficult, which can be problematic for domains with complex geometry or large numbers of agents. To compound matters further, practical applications often require planning methods to operate under high levels of uncertainty or meet strict running-time requirements. These challenges strongly suggest that heuristic methods are needed to address pursuit-evasion problems in the real world. In this dissertation I present heuristic planning techniques for three related problem domains: visibility-based pursuit-evasion, target following with differential motion constraints, and distributed asset guarding with unmanned sea-surface vehicles. For these domains, I demonstrate that heuristic techniques based on problem relaxation and model-predictive simulation can be used to efficiently perform low-level control action selection, motion goal selection, and high-level task allocation. In particular, I introduce a polynomial-time algorithm for control action selection in visibility-based pursuit-evasion games, where a team of pursuers must minimize uncertainty about the location of an evader. The algorithm uses problem relaxation to estimate future states of the game. I also show how to incorporate a probabilistic opponent model learned from interaction traces of prior games into the algorithm. I verify experimentally that by performing Monte Carlo sampling over the learned model to estimate the location of the evader, the algorithm performs better than existing planning approaches based on worst-case analysis. Next, I introduce an algorithm for motion goal selection in pursuit-evasion scenarios with unmanned boats. I show how a probabilistic model accounting for differential motion constraints can be used to project the future positions of the target boat. Motion goals for the pursuer boat can then be selected based on those projections. I verify experimentally that motion goals selected with this technique are better optimized for travel time and proximity to the target boat when compared to motion goals selected based on the current position of the target boat. Finally, I introduce a task-allocation technique for a team of unmanned sea-surface vehicles (USVs) responsible for guarding a high-valued asset. The team of USVs must intercept and block a set of hostile intruder boats before they reach the asset. The algorithm uses model-predictive simulation to estimate the value of high-level task assignments, which are then realized by a set of learned low-level behaviors. I show experimentally that using model-predictive simulations based on Monte-Carlo sampling is more effective than hand-coded evaluation heuristics.Item Synthesis of Strategies for Non-Zero-Sum Repeated Games(2008-08-21) Au, Tsz-Chiu; Nau, Dana; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)There are numerous applications that involve two or more self-interested autonomous agents that repeatedly interact with each other in order to achieve a goal or maximize their utilities. This dissertation focuses on the problem of how to identify and exploit useful structures in agents' behavior for the construction of good strategies for agents in multi-agent environments, particularly non-zero-sum repeated games. This dissertation makes four contributions to the study of this problem. First, this thesis describes a way to take a set of interaction traces produced by different pairs of players in a two-player repeated game, and then find the best way to combine them into a strategy. The strategy can then be incorporated into an existing agent, as an enhancement of the agent's original strategy. In cross-validated experiments involving 126 agents for the Iterated Prisoner's Dilemma, Iterated Chicken Game, and Iterated Battle of the Sexes, my technique was able to make improvement to the performance of nearly all of the agents. Second, this thesis investigates the issue of uncertainty about goals when a goal-based agent situated in a nondeterministic environment. The results of this investigation include the necessary and sufficiency conditions for such guarantee, and an algorithm for synthesizing a strategy from interaction traces that maximizes the probability of success of an agent even when no strategy can assure the success of the agent. Third, this thesis introduces a technique, Symbolic Noise Detection (SND), for detecting noise (i.e., mistakes or miscommunications) among agents in repeated games. The idea is that if we can build a model of the other agent's behavior, we can use this model to detect and correct actions that have been affected by noise. In the 20th Anniversary Iterated Prisoner's Dilemma competition, the SND agent placed third in the "noise" category, and was the best performer among programs that had no "slave" programs feeding points to them. Fourth, the thesis presents a generalization of SND that can be wrapped around any existing strategy. Finally, the thesis includes a general framework for synthesizing strategies from experience for repeated games in both noisy and noisy-free environments.