Towards a Planning-Based Neural-Symbolic Framework for Egocentric Agent Design
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Authors
Liu, Xiaotian
Date
Type
thesis
Language
eng
Keyword
Neural Symbolic , Machine Learning , Computer Vision , Egocentric Planning , Partial Obsevability , Embodied Agent
Alternative Title
Abstract
In the last decade, deep neural networks have given embodied agents many tools that can extract information from environmental data. However, tasks such as reasoning and long-term planning cannot be done effectively using deep neural networks. Symbolic methods, such as automated planning, are still methods of choice for embodied agents in most applications. The integration of a planning system with deep neural networks seems to be the natural next step for embodied agent design. However, many challenges from both the planning and deep learning side need to be solved for such hybrid systems. This work proposes a neural-symbolic framework for constructing embodied agents, capable of semantic navigation, that can take advantage of both neural and symbolic algorithms. The framework uses neural networks for low-level information extraction and automated planners for high-level reasoning. The main challenges our approach addresses are converting traditional pansophical planners to egocentric perspectives and finding the appropriate factored representation for environmental information discovered by such agents. The thesis has two main contributions towards building a neural-symbolic embodied agents. Our first contribution is presenting a method of converting classic pansophical planning problems to egocentric alternatives. Secondly, we propose a spatial semantic graph structure to store environmental information for autonomous object navigation.
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Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution-NonCommercial-NoDerivs 3.0 United States
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution-NonCommercial-NoDerivs 3.0 United States