Filtering by
- All Subjects: artificial intelligence
- Creators: Computer Science and Engineering Program
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Resource Type: Text
![133901-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-05/133901-Thumbnail%20Image.png?versionId=AtyWRMjjA5hk3Fcq7tS7ozxtLE1gKvtq&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240427/us-west-2/s3/aws4_request&X-Amz-Date=20240427T060040Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=5e891f394b819c75677c76d0bf9a58cb27f42fb6880c0c4affafa957bb592659&itok=2QaOvFH-)
![136202-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-05/136202-Thumbnail%20Image.png?versionId=PZcB72d2_0.AFigEdZEJFaUlhAfaJ03O&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240427/us-west-2/s3/aws4_request&X-Amz-Date=20240427T060711Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=875a7f7dbd2766790b49b68eb2112c01b789cbb922b010c143bd9c11b8bd81d0&itok=u6UA5KLQ)
![135242-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-05/135242-Thumbnail%20Image.png?versionId=Z8fdOIsSxFrX6BZYXApvuwoDek_YOsZx&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240427/us-west-2/s3/aws4_request&X-Amz-Date=20240427T110958Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=2e6610b8a5101fa242b12a61eaed65ca07c39c196e4bc4bf7e5548c131f23906&itok=VRtleOw8)
![132967-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-05/132967-Thumbnail%20Image.png?versionId=xK9Uty7wWaIjK2UFTYEJ6R1h7z8rMlG0&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240427/us-west-2/s3/aws4_request&X-Amz-Date=20240427T060209Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=6d325324da6f5122bd9dd3f580032b5446a23baf9c0dfadf2392907a0e1e0381&itok=fc0ASig1)
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.
![133339-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-05/133339-Thumbnail%20Image.png?versionId=JktrTlNQ.Lxpux93rjzTeFHPXl8RpcZl&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240427/us-west-2/s3/aws4_request&X-Amz-Date=20240427T060711Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=fce19f22f976fa1b2bad5b35e421fbea5f887c217d1bb3cdb011bd6e880d9618&itok=3THKdy52)
![134257-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-05/134257-Thumbnail%20Image.png?versionId=PiJi8wDFO0r.fZMdrpddPyx1uOFuISwO&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240427/us-west-2/s3/aws4_request&X-Amz-Date=20240427T103304Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=3f6818deb0e389a7ee5c75462af04a53d71b039f3944332dbc58609efda286c9&itok=-YLTIY0y)
![135758-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-05/135758-Thumbnail%20Image.png?versionId=m6hhqvtqZnRbVaRPMs4its6fq1gAiLAN&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240427/us-west-2/s3/aws4_request&X-Amz-Date=20240427T111906Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=0b3169d25825133098ff22cc0b4407939803118a1baae1634b02464efa68301a&itok=BOaHtyww)
![135955-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-05/135955-Thumbnail%20Image.png?versionId=3euM8CuC1vxMX5UZq5k1tpPleODGzEpX&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240427/us-west-2/s3/aws4_request&X-Amz-Date=20240427T060127Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=8d0ab7f45a9a67fb4ca1e343e619810522fd1213b8cdc4d4f615824908126423&itok=8FUuRq5r)
![147905-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-07/147905-Thumbnail%20Image.png?versionId=4WDcYIKTzZbwopQtmpl0owH7C8kBlGRV&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240427/us-west-2/s3/aws4_request&X-Amz-Date=20240427T124613Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=5035271b3b245b62a53189969fc8045a0d8e1f6a7de7df46529db986381d7284&itok=0A1IszM_)
This paper is centered on the use of generative adversarial networks (GANs) to convert or generate RGB images from grayscale ones. The primary goal is to create sensible and colorful versions of a set of grayscale images by training a discriminator to recognize failed or generated images and training a generator to attempt to satisfy the discriminator. The network design is described in further detail below; however there are several potential issues that arise including the averaging of a color for certain images such that small details in an image are not assigned unique colors leading to a neutral blend. We attempt to mitigate this issue as much as possible.
![148333-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-07/148333-Thumbnail%20Image.png?versionId=bl.UDDXUolhUv27d0FyH93VBLiASjXAP&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240427/us-west-2/s3/aws4_request&X-Amz-Date=20240427T061035Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=b24b4fa016de0f249dfc132f9208686a2f98abe9cc1edebed7ab4185e88ede46&itok=rsUPOaK9)
This thesis attempts to explain Everettian quantum mechanics from the ground up, such that those with little to no experience in quantum physics can understand it. First, we introduce the history of quantum theory, and some concepts that make up the framework of quantum physics. Through these concepts, we reveal why interpretations are necessary to map the quantum world onto our classical world. We then introduce the Copenhagen interpretation, and how many-worlds differs from it. From there, we dive into the concepts of entanglement and decoherence, explaining how worlds branch in an Everettian universe, and how an Everettian universe can appear as our classical observed world. From there, we attempt to answer common questions about many-worlds and discuss whether there are philosophical ramifications to believing such a theory. Finally, we look at whether the many-worlds interpretation can be proven, and why one might choose to believe it.