tinyMAS is a multiagent platform that provides base concepts (such as kernel, message, yellow pages, white pages, and transport service) and extended concepts (such as environment, influence, and perception). It aims to provide an easy-to-understand and and easy-to-use platform dedicated for multiagent engineer/research courses. TinyMAS is no longer under development. A large amount of its source code has been merged into the Janus platform.
JASA is a high-performance auction simulator. It is designed for performing experiments in agent-based computational economics. It implements variants of the double-auction market, which is commonly used to run real-world market places such as stock exchanges. It is designed to be highly extensible so that other types of auctions can easily be implemented. The software also provides a base classes for implementing simple adaptive trading agents.
SIP provides image processing, pattern recognition, and computer vision routines for SciLab, a Matlab-like matrix-oriented programming environment. SIP is able to read/write images in almost 90 major formats, including JPEG, PNG, BMP, GIF, FITS, and TIFF. It includes routines for filtering, segmentation, edge detection, morphology, curvature, fractal dimension, distance transforms, multiscale skeletons, and more.
Into is a cross-platform machine intelligence application framework written in C++. Into provides a different, fast way to build high-performance applications for image analysis, machine vision, pattern recognition, and artificial intelligence. It features a layered API and more than 20 fully interoperable plug-in modules for accessing image and data sources, powerful feature extractors, classifiers, neural networks, and much more. It also provides Ydin, an innovative execution engine that makes it easy to create dynamic programs that automatically run in parallel, enabling you to create more with less hassle, less code, and less time. Into uses Qt to let you create beautiful user interfaces for your applications with ease.
Urbi is a robotics software platform. It includes a C++/Java middleware API called UObject to interface components such as motors, cameras, and algorithms, and an innovative scripting language, urbiscript, with built-in support for parallel and event-based programming, used to write high-level behaviors and orchestrate the interactions between components. UObject components are built as shared libraries exposed as native objects within urbiscript, and either hot-plugged in a running Urbi engine, or started as a remote autonomous process communicating with the engine via the network. At any time, new urbiscript code can be sent to a running Urbi engine via a simple telnet, to introspect the state of components, modify existing code, or add new behaviors. Urbi is cross-platform and supports several robots (Gostai Jazz, Lego Mindstorms, Aldebaran Nao, Segway RMP, Spykee, Bioloid, etc.) and a simulator (Webots).
ffnet is a fast and easy-to-use feed-forward neural network training solution for Python. You can use it to train, test, save, load, and use an artificial neural network with sigmoid activation functions. Any network connectivity without cycles is allowed (not only layered). Training can be performed with several optimization schemes, including genetic alorithm based optimization. There is access to exact partial derivatives of network outputs versus its inputs. Normalization of data is handled automatically by ffnet.
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.
METSlib is an object-oriented metaheuristics framework in C++ designed to make implementing or adapting models easy. The model is modular: all the implemented search algorithms can be applied to the same model. METSlib implements the basics of some metaheuristics algorithms, such as Random Restart Local Search, Variable Neighborhood Search, Iterated Local Search, Simulated Annealing, and Tabu Search. For each algorithm, you must implement an objective function, a neighborhood (move manager), and some moves. Tabu Search is one of the fastest ways to generate near-optimal solutions to a wide range of hard combinatorial optimization problems.