GUI-Based, Efficient Genetic Programming and AI Planning for Unity3D
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{gold:2022:GPTP,
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author = "Robert Gold and Andrew Haydn Grant and
Erik Hemberg and Chathika Gunaratne and Una-May O'Reilly",
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title = "{GUI-Based,} Efficient Genetic Programming and {AI}
Planning for {Unity3D}",
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booktitle = "Genetic Programming Theory and Practice XIX",
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year = "2022",
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editor = "Leonardo Trujillo and Stephan M. Winkler and
Sara Silva and Wolfgang Banzhaf",
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series = "Genetic and Evolutionary Computation",
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pages = "57--79",
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address = "Ann Arbor, USA",
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month = jun # " 2-4",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-981-19-8459-4",
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URL = "http://link.springer.com/chapter/10.1007/978-981-19-8460-0_3",
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DOI = "doi:10.1007/978-981-19-8460-0_3",
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abstract = "We present a GUI-driven and efficient Genetic
Programming (GP) and AI Planning framework designed for
agent-based learning research. Our framework,
ABL-Unity3D, is built in Unity3D, a game development
environment. ABL-Unity3D addresses challenges entailed
in co-opting Unity3D: making the simulator serve agent
learning rather than humans playing a game, lowering
fitness evaluation time to make learning
computationally feasible, and interfacing GP with an AI
Planner to support hybrid algorithms. We achieve this
by developing a Graphical User Interface (GUI) using
the Unity3D editor’s programmable interface and
performance optimizations. These optimizations result
in at least a 3x speedup. In addition, we describe
ABL-Unity3D by explaining how to use it for an example
experiment using GP and AI Planning. We benchmark
ABL-Unity3D by measuring the performance and speed of
the AI Planner alone, GP alone, and the AI Planner with
GP.",
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notes = "Part of \cite{Banzhaf:2022:GPTP} published after the
workshop in 2023",
- }
Genetic Programming entries for
Robert Gold
Andrew Haydn Grant
Erik Hemberg
Chathika S Gunaratne
Una-May O'Reilly
Citations