Darius Braziunas

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Structured utility models are essential for the effective representation and elicitation of complex multiattribute utility functions. Generalized additive independence (GAI) models provide an attractive structural model of user preferences, offering a balanced tradeoff between simplicity and applicability. While representation and inference with such models(More)
We propose a new system for vision-based mobile robot navigation in an unmodeled environment. Simple , unobtrusive artiicial landmarks are used as navigation and localization aids. The landmark patterns are designed so that they can be reliably detected in real-time in images taken with the robot's camera over a wide range of viewing conngurations. The code(More)
We describe the semantic foundations for elicitation of generalized additively independent (GAI) utilities using the minimax regret criterion, and propose several new query types and strategies for this purpose. Computational feasibility is obtained by exploiting the local GAI structure in the model. Our results provide a practical approach for implementing(More)
Product recommendation and decision support systems must generally develop a model of user preferences by querying or otherwise interacting with a user. Recent approaches to elicitation using minimax regret have proven to be very powerful in simulation. In this work, we test both the effectiveness of regret-based elicitation, and user comprehension and(More)
wide variety of AI applications, addressing the preference bottleneck is vital. Specifically, since the ability to make reasonable decisions on behalf of a user depends on that user’s preferences over outcomes in the domain in question, AI systems must assess or estimate these preferences before making decisions. Designing effective preference assessment(More)
This is an overview of partially observable Markov decision processes (POMDPs). We describe POMDP value and policy iteration as well as gradient ascent algorithms. The emphasis is on solution methods that work directly in the space of policies.
We examine the cold-start recommendation task in an online retail setting for users who have not yet purchased (or interacted in a meaningful way with) any available items but who have granted access to limited side information, such as basic demographic data (gender, age, location) or social network information (Facebook friends or page likes). We(More)