DEVS has been developed for over a year to serve as an experimental framework for natural systems modeling techniques. It enables discrete event, general purpose, object oriented, component based, GIS connected, and collaborative visual simulation model development and execution. The sample model implementation shows that this experimental environment can be used for solving any complex problems solvable by discrete-event simulation, but it is especially suited for natural system simulation. Currently only hierarchical block and cellular models are modeled and simulated, but a multi-layered modeling paradigm for spatially distributed systems (with vector and cellular models) will eventually be implemented in the environment.
The Pattern Recognition Application Programmer's Interface aims to be a fully-featured, easy-to-use general C++ framework for various pattern recognition tasks, especially image analysis. It features support for many image formats, well-known image analysis methods, classification and feature analysis tools, XML serialization, etc.
Turing Machine (C++ Implementation) is a Turing machine simulation that is defined by a series of input files. These include a metafile containing data related to some Turing machine, a states file containing a list of initial, halting, and internal states, an alphabet file of empty, input, and internal symbols, a transition file of transition rules, and input word files, which detail the input given on a tape.
The OpenAI site is centered around an Open Source project and community involving artificial intelligence. The project itself is the creation of a set of tools that are considered to be models of human intelligence or biomimicry. These tools are intended to be integrated into applications or used stand alone for research.
Stochastic discrimination is a general methodology for constructing classifiers appropriate for pattern recognition. It is based on combining arbitrary numbers of very weak components, which are usually generated by some pseudorandom process, and it has the property that the very complex and accurate classifiers produced in this way retain the ability, characteristic of their weak component pieces, to generalize to new data as complexity increases. These utilities provide an implementation of this algorithm.