Barrett

Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.

Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.

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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,…

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.
ContributorsNakhleh, 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)
Created2019-05
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AI Planning and Team Abstractions

Description
Many real-world problems rely on the collaboration of multiple agents. Making plans for these multiple agents such that the goal state can be achieved becomes more and more difficult as the number of objects to consider increases. The increase in the number of objects results in the exponential increase in…

Many real-world problems rely on the collaboration of multiple agents. Making plans for these multiple agents such that the goal state can be achieved becomes more and more difficult as the number of objects to consider increases. The increase in the number of objects results in the exponential increase in time and space required to find a viable plan. By mapping each agent onto some team, creating an abstract plan, and applying the abstract plan to the concrete problem, we can produce plans that reach the goal state more quickly than by solving them directly. This is demonstrated by applying this method to multiple problems in a custom domain dubbed the “garden” domain.
ContributorsAtkinson, Kyle (Author) / Srivastava, Siddharth (Thesis director) / Shah, Naman (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05