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Bucknell CSCI 357: Using Python ACT-R to train an AI "Roomba" to intelligently clean a virtual room.

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Py3ACTR-master		Py3ACTR-master
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README.md		README.md
sandwich.py		sandwich.py
vacuum.py		vacuum.py

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Learning Rule-Based Expert Systems Using Python ACT-R

Assignment Description

The primary goal of this assignment is to create an intelligent "Roomba" robot that can intelligently navigate through a finite virtual 2D space. The main idea is to use Python ACT-R to specify behaviors that the Roomba should use in certain situations. These include spiraling, bouncing, and wall-following. The code for this portion of the assignment is in vacuum.py.

Another part of this assignment is to build a program with Python ACT-R that effectively creates a (virtual and non-visual) sandwich from the bottom up. This is a simple part of the assignment and is used to demonstrate how rules can be created using Python ACT-R. The code for this portion of the assignment is in sandwich.py.

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Bucknell CSCI 357: Using Python ACT-R to train an AI "Roomba" to intelligently clean a virtual room.

python machine-learning ai neural-networks act-r

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