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.
UniModeling is a big data analytics tool for unified modeling and reasoning in outdoor and indoor spaces. It supports the construction of unified graph models of outdoor and indoor spaces and RFID deployments in these spaces. It enables probabilistic incorporation of RFID data, facilitating the tracking of moving objects and enables the search for them to be optimized. Also included are three reasoning applications that pertain to the positioning of RFID readers in outdoor and indoor spaces and the points of potential traffic (over)load in these spaces.
Thinknowlogy is grammar-based software, designed to utilize the Natural Laws of Intelligence in grammar, in order to create intelligence through natural language in software. This is demonstrated by programming in natural language, reasoning in natural language and drawing conclusions (more detailed than scientific solutions), making assumptions (with self-adjusting level of uncertainty), asking questions (about gaps in the knowledge), and detecting conflicts in the knowledge. It builds semantics autonomously (with no vocabularies or words lists), detecting some cases of semantic ambiguity. It is multi-grammar, proving that Natural Laws of Intelligence are universal.
pyuds is a Python library for measuring uncertainty in the Dempster-Shafer theory of evidence. The functionals supported are the Generalized Hartley (GH) uncertainty functional, Generalized Shannon (GS) uncertainty functional, and Aggregate Uncertainty (AU) functional. The library can be utilized either through its API, or through a user-friendly Web interface.
SHOGUN is a machine learning toolbox whose focus is on large scale kernel methods and especially on Support Vector Machines (SVM). It provides a generic SVM object interfacing to several different SVM implementations, all making use of the same underlying, efficient kernel implementations. Apart from SVMs and regression, SHOGUN also features a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons, and algorithms to train hidden Markov models. SHOGUN can be used from within C++, Matlab, R, Octave, and Python.