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Rakesh Gupta

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3 papers

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AAAI Conference 2012 Conference Paper

Improving Hybrid Vehicle Fuel Efficiency Using Inverse Reinforcement Learning

Adam Vogel
Deepak Ramachandran
Rakesh Gupta
Antoine Raux

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 balance of engine vs. battery power can be attained. In this paper, we utilize a probabilistic driving route prediction system, trained using Inverse Reinforcement Learning, to optimize the hybrid control policy. Our approach considers routes that the driver is likely to be taking, computing an optimal mix of engine and battery power. This approach has the potential to increase vehicle power efficiency while not requiring any hardware modification or change in driver behavior. Our method outperforms a standard hybrid control policy, yielding an average of 1. 22% fuel savings.

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AAAI Conference 2008 Conference Paper

Text Categorization with Knowledge Transfer from Heterogeneous Data Sources

Rakesh Gupta

Multi-category classification of short dialogues is a common task performed by humans. When assigning a question to an expert, a customer service operator tries to classify the customer query into one of N different classes for which experts are available. Similarly, questions on the web (for example questions at Yahoo Answers) can be automatically forwarded to a restricted group of people with a specific expertise. Typical questions are short and assume background world knowledge for correct classification. With exponentially increasing amount of knowledge available, with distinct properties (labeled vs unlabeled, structured vs unstructured), no single knowledge-transfer algorithm such as transfer learning, multi-task learning or selftaught learning can be applied universally. In this work we show that bag-of-words classifiers performs poorly on noisy short conversational text snippets. We present an algorithm for leveraging heterogeneous data sources and algorithms with significant improvements over any single algorithm, rivaling human performance. Using different algorithms for each knowledge source we use mutual information to aggressively prune features. With heterogeneous data sources including Wikipedia, Open Directory Project (ODP), and Yahoo Answers, we show 89. 4% and 96. 8% correct classification on Google Answers corpus and Switchboard corpus using only 200 features/class. This reflects a huge improvement over bag of words approaches and 48-65% error reduction over previously published state of art (Gabrilovich et. al. 2006).

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AAAI Conference 2004 Conference Paper

Common Sense Data Acquisition for Indoor Mobile Robots

Rakesh Gupta
Mykel J. Kochenderfer

Common sense knowledge can be efficiently collected from non-experts over the web in a similar fashion to the Open Mind family of distributed knowledge capture projects. We describe the collection of common sense data through the Open Mind Indoor Common Sense (OMICS) website. We restrict the domain to indoor home and office environments to obtain dense knowledge. The knowledge was collected through sentence templates that were generated dynamically based on previous user input. Entries were converted into relations and saved into a database. We discuss the results of this online collaborative effort and describe two applications of the collected data to indoor mobile robots. We discuss active desire selection based on current beliefs and commands and a room-labeling application based on probability estimates from the common sense knowledge base.

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