Operations Research
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Solving convex programs by random walks
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Policy teaching through reward function learning
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Value-based policy teaching with active indirect elicitation
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Policy teaching through reward function learning
Proceedings of the 10th ACM conference on Electronic commerce
Toward automatic task design: a progress report
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To teach or not to teach?: decision making under uncertainty in ad hoc teams
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Incentive design for adaptive agents
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Multiagent environment design in human computation
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Teaching and leading an ad hoc teammate: Collaboration without pre-coordination
Artificial Intelligence
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The problem of environment design considers a setting in which an interested party aims to influence an agent's decisions by making limited changes to the agent's environment. Zhang and Parkes [2008] first introduced the environment design concept for a specific problem in the Markov Decision Process setting. In this paper, we present a general framework for the formulation and solution of environment design problems with one agent. We consider both the case in which the agent's local decision model is known and partially unknown to the interested party, and illustrate the framework and results on a linear programming setting. For the latter problem, we formulate an active, indirect elicitation method and provide conditions for convergence and logarithmic convergence. We relate to the problem of inverse optimization and also offer a game-theoretic interpretation of our methods.