The Noble Ape Simulation is a collection of a number of autonomous simulation components including a landscape simulation, biological simulation, weather simulation, sentient creature (Noble Ape) simulation, and a simple intelligent-agent scripting language (ApeScript). Noble Ape also contains a social simulation where the Noble Apes can be tracked in terms of social groups and also over many generations to explain social phenomenon to users looking to study this kind of interaction. It has been in development for more than a fifteen years.
PEDSIM is a microscopic pedestrian crowd simulation system. The PEDSIM library allows you to use pedestrian dynamics in your own software. Based on pure C++/STL without additional packages, it runs on virtually every operating system. The PEDSIM Demo Application (Qt) gives you a quick overview of the capabilities, and is a starting point for your own experiments. PEDSIM is suitable for use in crowd simulations (e.g. indoor evacuation simulation, large scale outdoor simulations), where one is interested in output like pedestrian density or evacuation time. The quality of the individual agent's trajectory is high, PEDSIM can be used for creating massive pedestrian crowds in movies. Since libpedsim is easy to use and extend, it is a good starting point for science projects.
Conquest is a simultaneously-executed turn-based tactical combat game placed in a dark and distant future. Play the role of a futuristic commander. Divide your armies and conquer the world. Position satellites to reveal your opponents. Launch missiles to annihilate big armies, but watch out for incoming drop pods behind your back. Standing in your path to victory are other commanders like yourself. Fight them off one by one and prove you are the greatest of the great. The combination of fast gameplay and randomly generated maps equals intense, restless nights of battles for cities. Drag and drop your way to victory.
Fast Artificial Neural Network Library is a neural network library that implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks. Cross-platform execution in both fixed and floating point are supported. It includes a framework for easy handling of training data sets. It is easy to use, versatile, well documented, and fast. C++, Perl, PHP, .NET, Python, Delphi, Octave, Pure Data, and Mathematica bindings are available. A reference manual accompanies the library with examples and recommendations on how to use the library. A graphical user interface is also available for the library.