Popular recipes tagged "artificial_intelligence" but not "algorithm" and "tac"

Uno (Text-Based) (Python)

2017-07-15T00:46:59-07:00

Python recipe 580811 by Brandon Martin (artificial_intelligence, cards, game, text_game, uno).

A text based recreation of the classic card game featuring functional AIs to play with. Some rules have been modified. User interface is text based, non-curses, using only simple python commands to draw it.

Priority Queue (with deletion) (Python)

2013-12-08T21:12:55-08:00

Python recipe 578780 by elazar (artificial_intelligence, heap, heapq, priority_queue, queue). Revision 5.

Based on the interface defined in aima-python http://aima-python.googlecode.com/svn/trunk/utils.py

Yields better performance.

State Machine Framework (AI) (Python)

2013-09-12T19:50:06-07:00

Python recipe 578656 by Matt Jones (artificial_intelligence, state_machine).

A simple state machine framework that could be used for AI or long processing operations. A simple example is provided as well.

Python 3.2 required

Genetic Algorithm Neural Network in Python Source Code (Python)

2012-08-16T16:31:12-07:00

Python recipe 578241 by David Adler (artificial_intelligence, neural, neural_network).

A simple genetic algorithm neural network.

Artificial Neuroglial Network (ANGN) (Python)

2012-10-02T16:18:36-07:00

Python recipe 578242 by David Adler (artificial_intelligence, genetic_algorithm, genetic_algorithms, neural, neural_networks). Revision 5.

This is an attempt at emulating the algorithm from these scientific articles:

The objective of the program is to train a neural network to classify the four inputs (the dimensions of a flower) into one of three categories (three species of flower), (taken from the Iris Data Set from the UCI Machine Learning Repository). This program has two learning phases: the first is a genetic algorithm (supervised), the second is a neuroglial algorithm (unsupervised). This ANGN is a development of a previous program only consisting of a genetic algorithm which can be found here.

The second phase aims to emulate astrocytic interaction with neurons in the brain. The algorithm is based on two axioms: a) astrocytes are activated by persistent neuronal activity b) astrocytic effects occur over a longer time-scale than neurons. Each neuron has an associated astrocyte which counts the number of times its associated neuron fires (+1 for active -1 for inactive). If the counter reaches its threshold (defined as Athresh) the astrocyte is activated and for the next x iterations (defined as Adur) the astrocyte modifies the incoming weights to that particular neuron. If the counter reached a maximum due to persistent firing the incoming weights are increase by 25% for the proceeding Adur iterations; conversely if the counter reached a minimum due to persistent lack of firing the weights are decreased by 50% for the following Adur iterations). For a detailed description of the algorithm see the linked articles. For a general understanding of how this program was coded look at the pseudo-code/schematic here.

Any comments for improvements are welcome. There are several issues in this program which require addressing, please scroll down below code to read about these issues.