Learning Models of Sequential Decision-Making with Partial Specification of Agent Behavior

Efficient Learning of Relational Models for Sequential Decision Making

OF THE DISSERTATION Efficient Learning of Relational Models for Sequential Decision Making by Thomas J. Walsh Dissertation Director: Michael L. Littman The exploration-exploitation tradeoff is crucial to reinforcement-learning (RL) agents, and a significant number of sample complexity results have been derived for agents in propositional domains. These results guarantee, with high probability, ...

Learning and Sequential Decision Making

Comparing Agent-Based Learning Models of Land-Use Decision Making

An agent-based model, incorporating a small set of primarily agent-based variables, was designed to explain private land-use decision making. Agents are landowners, who allocate their labor and land for different uses in regular time intervals. The goal is to understand what kind of spatial patterns emerge from different agent characteristics, and decision and learning mechanisms. Landscapes pr...

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چکیده ندارد.

Fairness in Multi-Agent Sequential Decision-Making

We define a fairness solution criterion for multi-agent decision-making problems, where agents have local interests. This new criterion aims to maximize the worst performance of agents with a consideration on the overall performance. We develop a simple linear programming approach and a more scalable game-theoretic approach for computing an optimal fairness policy. This game-theoretic approach ...