Browsing by Subject "Multi-agent systems"
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Item Open Access A Distributed Optimal Control Approach for Multi-agent Trajectory Optimization(2013) Foderaro, GregThis dissertation presents a novel distributed optimal control (DOC) problem formulation that is applicable to multiscale dynamical systems comprised of numerous interacting systems, or agents, that together give rise to coherent macroscopic behaviors, or coarse dynamics, that can be modeled by partial differential equations (PDEs) on larger spatial and time scales. The DOC methodology seeks to obtain optimal agent state and control trajectories by representing the system's performance as an integral cost function of the macroscopic state, which is optimized subject to the agents' dynamics. The macroscopic state is identified as a time-varying probability density function to which the states of the individual agents can be mapped via a restriction operator. Optimality conditions for the DOC problem are derived analytically, and the optimal trajectories of the macroscopic state and control are computed using direct and indirect optimization algorithms. Feedback microscopic control laws are then derived from the optimal macroscopic description using a potential function approach.
The DOC approach is demonstrated numerically through benchmark multi-agent trajectory optimization problems, where large systems of agents were given the objectives of traveling to goal state distributions, avoiding obstacles, maintaining formations, and minimizing energy consumption through control. Comparisons are provided between the direct and indirect optimization techniques, as well as existing methods from the literature, and a computational complexity analysis is presented. The methodology is also applied to a track coverage optimization problem for the control of distributed networks of mobile omnidirectional sensors, where the sensors move to maximize the probability of track detection of a known distribution of mobile targets traversing a region of interest (ROI). Through extensive simulations, DOC is shown to outperform several existing sensor deployment and control strategies. Furthermore, the computation required by the DOC algorithm is proven to be far reduced compared to that of classical, direct optimal control algorithms.
Item Open Access Eliciting and Aggregating Information for Better Decision Making(2018) Freeman, RupertIn this thesis, we consider two classes of problems where algorithms are increasingly used to make, or assist in making, a wide range of decisions. The first class of problems we consider is the allocation of jointly owned resources among a group of agents, and the second is the elicitation and aggregation of probabilistic forecasts from agents regarding future events. Solutions to these problems must trade off between many competing objectives including economic efficiency, fairness between participants, and strategic concerns.
In the first part of the thesis, we consider shared resource allocation, where we relax two common assumptions in the fair divison literature. Firstly, we relax the assumption that goods are private, meaning that they must be allocated to only a single agent, and introduce a more general public decision making model. This allows us to incorporate ideas and techniques from fair division to define novel fairness notions in the public decisions setting. Second, we relax the assumption that decisions are made offline, and instead consider online decisions. In this setting, we are forced to make decisions based on limited information, while seeking to retain fairness and game-theoretic desiderata.
In the second part of the thesis, we consider the design of mechanisms for forecasting. We first consider a tradeoff between several desirable properties for wagering mechanisms, showing that the properties of Pareto efficiency, incentive compatibility, budget balance, and individual rationality are incompatible with one another. We propose two compromise solutions by relaxing either Pareto efficiency or incentive compatibility. Next, we consider the design of decentralized prediction markets, which are defined by the lack of any single trusted authority. As a consequence, markets must be closed by popular vote amongst a group of anonymous, untrusted arbiters. We design a mechanism that incentivizes arbiters to truthfully report their information even when they have a (possibly conflicting) stake in the market themselves.