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Events for September 26, 2023
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PhD Dissertation Defense - Setareh Nasihati Gilani
Tue, Sep 26, 2023 @ 03:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Dissertation Defense - Setareh Nasihati Gilani
Committee Members: David Traum (Chair), Maja Mataric, Peter Kim, Kallirroi Georgila, Mohammad Soleymani
Title: Understanding and Generating Multimodal Feedback in Human Machine Story Telling
Abstract: People use feedback, verbal or nonverbal, from their interlocutors to guide their own behavior and alter the flow of conversation. In this thesis, we focus on human machine interactions that involve storytelling and investigate the role of understanding and providing feedback from the machines perspective. We explored the characteristics of stories that machines should use to increase rapport. We developed machine storytellers and listeners that can provide feedback and adapt their stories based on perceived multimodal feedback from their users. Finally, we investigated how machines can use real time predictions based on user feedback to further adapt the dialogue management policies of the system for better overall performance.Audiences: Everyone Is Invited
Contact: Melissa Ochoa
Event Link: https://usc.zoom.us/j/93206733633
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PhD Thesis Proposal - Taoan Huang
Tue, Sep 26, 2023 @ 03:30 PM - 05:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Thesis Proposal - Taoan Huang
Committee Members: Sven Koenig (co chair), Bistra Dilkina (co chair), Jyotirmoy Deshmukh, Stefanos Nikolaidis, John Carlsson, Peter Stuckey from Monash University
Title: Improving Decision Makings in Search Algorithms with Machine Learning for Combinatorial Optimizations
Abstract: Designing algorithms for combinatorial optimization problems (COP) are important and challenging tasks since it concerns a wide range of real world problems, such as vehicle routing, path planning and resource allocation problems. Most COPs are NP hard to solve and many research algorithms have been developed for them in the past few decades. Decision makings such as partitioning or pruning the search space and prioritizing exploration in the search space, are crucial to the efficiency and effectiveness of the search algorithms. Many of those heavily rely on domain expertise and human designed strategies.
In this thesis, we hypothesize that one can leverage machine learning to improve human designed decision making strategies in different categories of search algorithms for combinatorial optimization problems. We validate the hypothesis on the problems of multiagent path finding and solving mixed integer linear programs, introducing different machine learning techniques to advance a few state of the art optimal and heuristic search algorithms for the two problems.
Audiences: Everyone Is Invited
Contact: Melissa Ochoa
Event Link: https://usc.zoom.us/j/92825821724?pwd=a2RFY0x0QzV0S3hqYmkxakJvQUpYZz09
