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Deciding what mix of engine and battery power to use is critical to hybrid vehicles' fuel efficiency. Current solutions consider several factors such as the charge of the battery and how efficient the engine operates at a given speed. Previous research has shown that by taking into account the future power requirements of the vehicle, a more efficient(More)
Grice characterized communication in terms of the cooperative principle, which enjoins speakers to make only contributions that serve the evolving conversational goals. We show that the cooperative principle and the associated maxims of relevance, quality, and quantity emerge from multi-agent decision theory. We utilize the Decentralized Partially(More)
Conversational implicatures involve reasoning about multiply nested belief structures. This complexity poses significant challenges for computational models of conversation and cognition. We show that agents in the multi-agent Decentralized-POMDP reach implicature-rich interpretations simply as a by-product of the way they reason about each other to(More)
Online marketplaces increasingly act as intermediaries in labor, housing, dating, and other markets where traders match with each other. These marketplaces use novel data generated by users' activities on the website to design algorithms and products that influence the search and matching process. I use internal data from Airbnb, a prominent online(More)
Recursive Bayesian models of linguistic communication capture a variety of intricate kinds of pragmatic enrichment, but they tend to depend on the unrealistic assumption that agents are invariably optimal reasoners. We present a discrimina-tive model that seeks to capitalize on the insights of such approaches while addressing these concerns about(More)
When the transition probabilities and rewards of a Markov Decision Process (MDP) are known, an agent can obtain the optimal policy without any interaction with the environment. However, exact transition probabilities are difficult for experts to specify. One option left to an agent is a long and potentially costly exploration of the environment. In this(More)
Recommendation dialog systems help users navigate e-commerce listings by asking questions about users' preferences toward relevant domain attributes. We present a framework for generating and ranking fine-grained, highly relevant questions from user-generated reviews. We demonstrate our approach on a new dataset just released by Yelp, and release a new(More)