Full metadata
Title
Learning Generalized Heuristics Using Deep Neural Networks
Description
Classical planning is a field of Artificial Intelligence concerned with allowing autonomous agents to make reasonable decisions in complex environments. This work investigates
the application of deep learning and planning techniques, with the aim of constructing generalized plans capable of solving multiple problem instances. We construct a Deep Neural Network that, given an abstract problem state, predicts both (i) the best action to be taken from that state and (ii) the generalized “role” of the object being manipulated. The neural network was tested on two classical planning domains: the blocks world domain and the logistic domain. Results indicate that neural networks are capable of making such
predictions with high accuracy, indicating a promising new framework for approaching generalized planning problems.
the application of deep learning and planning techniques, with the aim of constructing generalized plans capable of solving multiple problem instances. We construct a Deep Neural Network that, given an abstract problem state, predicts both (i) the best action to be taken from that state and (ii) the generalized “role” of the object being manipulated. The neural network was tested on two classical planning domains: the blocks world domain and the logistic domain. Results indicate that neural networks are capable of making such
predictions with high accuracy, indicating a promising new framework for approaching generalized planning problems.
Date Created
2019-05
Contributors
- Nakhleh, Julia Blair (Author)
- Srivastava, Siddharth (Thesis director)
- Fainekos, Georgios (Committee member)
- Computer Science and Engineering Program (Contributor)
- School of International Letters and Cultures (Contributor)
- Barrett, The Honors College (Contributor)
Topical Subject
Resource Type
Extent
7 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Series
Academic Year 2018-2019
Handle
https://hdl.handle.net/2286/R.I.52244
Level of coding
minimal
Cataloging Standards
System Created
- 2019-04-04 12:00:07
System Modified
- 2021-08-11 04:09:57
- 3 years 2 months ago
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