1.1k citations · 4.8k across the 64 of their papers we have counts for
5 papers · 2 filters
Approximate Linear Programming for First-order MDPs
Scott Sanner, Craig Boutilier
We introduce a new approximate solution technique for first-order Markov decision processes (FOMDPs). Representing the value function linearly w.r.t. a set of first-order basis fun…
Practical Linear Value-approximation Techniques for First-order MDPs
Scott Sanner, Craig Boutilier
Recent work on approximate linear programming (ALP) techniques for first-order Markov Decision Processes (FOMDPs) represents the value function linearly w.r.t. a set of first-order…
Minimax regret based elicitation of generalized additive utilities
Darius Braziunas, Craig Boutilier
We describe the semantic foundations for elicitation of generalized additively independent (GAI) utilities using the minimax regret criterion, and propose several new query types a…
Toward Experiential Utility Elicitation for Interface Customization
Bowen Hui, Craig Boutilier
User preferences for automated assistance often vary widely, depending on the situation, and quality or presentation of help. Developing effectivemodels to learn individual prefere…
Regret-based Reward Elicitation for Markov Decision Processes
Kevin Regan, Craig Boutilier
The specification of aMarkov decision process (MDP) can be difficult. Reward function specification is especially problematic; in practice, it is often cognitively complex and time…