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AI & Machine Learning Libraries

By Logan Kearsley. AI & Machine Learning Libraries.

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AI & Machine Learning Libraries

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  1. By Logan Kearsley AI & Machine Learning Libraries

  2. The purpose of this project is to design a system that combines the capabilities of multiple types of AI and machine learning systems, such as nervous networks and subsumption architectures, to produce a more flexible and versatile hybrid system. Purpose

  3. The end goal is to produce a set of basic library functions and architecture descriptions for the easy manipulation of the AI/ML subsystems (particularly neural networks), and use those to build an AI system capable of teaching itself how to complete tasks specified by a human-defined heuristic and altering learned behaviors to cope with changes in its operational environment with minimal human intervention. Goals

  4. Other Projects • Don't know of any other similar projects. • Builds on previous work done on multilayer perceptrons and subsumption architecture. • Varies in trying to find ways to combine the different approaches to AI.

  5. Design & Programming • Modular / Black Box Design • The end user should be able to put together a working AI system with minimal knowledge of how the internals work • Programming done in C

  6. Testing • Perceptron Neural Nets • Forced learning: make sure it will learn arbitrary input-output mappings after a certain number of exposures • Subsumption Architecture • Simple test problems: does it run the right code for each sub-problem?

  7. Algorithms • Perceptrons: • Delta-rule learning: weights are adjusted based on the distance between the net's current output and the optimal output • Matrix simulation: weights are stored in an I (# of inputs) by O (# of outputs) matrix for each layer, rather than simulating each neuron individually. • Subsumption Architecture: • Scheduler takes a list of function pointers to task-specific functions • Task functions return an output or null • Highest-prioritynon-null task has its output executed each iteration

  8. Algorithms • Perceptron structure • Individual Neurons • vs. • Weight Matrix

  9. Algorithms • Subsumption Architecture:

  10. Problems • Back-Propagation is really confusing!

  11. Results & Conclusions • Single-layer perceptron works well • Capable of learning arbitrary mappings, but not an arbitrary combination of them • Multi-layer nets should learn arbitrary combinations, but learning algorithm for hidden layers is confusing. • Can't re-use all of the same single-layer functions • Plan Change • Originally, wanted to create a working system • Now, project goal is to produce useful function libraries- working systems are just for testing the code

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