We are pleased to announce that our NSF Eager proposal has been accepted:
Award Abstract #1836565: EAGER: Collaborative Research: A Unified Learnable Roadmap for Sequential Decision Making in Relatinoal Domains
This project seeks to develop new algorithms and data structures for learning and planning in situations where the environment is represented with a set of relations between objects. Relational representations capture interactions between objects in a succinct and easily interpretable representation.
Examples of domains that are well-suited to relational representations includes intelligent drones assisting soldiers, activities in a supply chain management, communication and friendship connections in a social network, and tracking individuals and activities in video.
Most recent advances in machine learning and planning, such as so-called “deep neural networks”, however, employ simple “flat” representations, where the state of the world is an uninterpreted string of bits. This project will make machine learning and planning methods easier to use and more robust by generalizing them so that they explicitly work with relational models and data.
The methods, theory, and data resulting from this proposal will impact the scientific community in several positive ways and will be made publicly available through an appropriate website. The research will be disseminated through refereed journals and conference proceedings and made available to researchers. Code for the proposed algorithms and descriptions of new benchmark problems will also be made publicly available. The investigators will work on organizing workshops and tutorials based on the challenges and findings arising from this project.
Many special purpose solutions have been developed to address small parts of these problems, but there are no general purpose tools that harness recent advances in machine learning to tackle this family of problems. This proposal seeks to develop such tools, drawing upon the investigators’ prior experience in learning relational regression trees and experience in value function approximation for reinforcement learning. In addition, this project seeks to build a bridge between recent advances in deep learning, which generally has not been compatible with relational representations, and recent advances in relational learning.
Read more on the National Science Foundation’s webpage.