Aseba is an event-based architecture for distributed control of mobile robots. It targets integrated multi-processor robots or groups of single-processor units, real or simulated. The core of aseba is a lightweight virtual machine tiny enough to run even on microcontrollers. Robots are programmed in a user-friendly scripting language using a cozy integrated development environment.
RapidMiner (formerly YALE) is a flexible Java environment for knowledge discovery in databases, machine learning, and data mining. Many nestable learning and preprocessing operators (including Weka) are provided. It features an XML-based graphical user interface, a plugin mechanism, and high-dimensional plotting, and provides an easy-to-use extension mechanism that makes it possible to integrate new operators and adapt the system to your personal requirements. A command line version is also included.
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).
Neural Network Framework is a C++ framework to develop, simulate, and analyze arbitrary complex neural networks. The programmer can use the classes provided to create neural networks with arbitrary topology and mixed type of neurons. It's very easy to add customized neurons and layers.
ACL2 is a mathematical logic, programming language, and mechanical theorem prover based on the applicative subset of Common Lisp. It is an "industrial-strength" version of the NQTHM or Boyer/Moore theorem prover, and has been used for the formal verification of commercial microprocessors, the Java Virtual Machine, interesting algorithms, and so forth.
HALoGEN is an extremely powerful and easy to use general-purpose natural language generation system. It consists of a symbolic generator, a forest ranker, and some sample inputs. The symbolic generator includes the Sensus Ontology dictionary based on WordNet. The forest ranker includes a 250 million word ngram language model (unigram, bigram, and trigram) trained on the Wall Street Journal newspaper text. The symbolic generator is written in LISP and requires a Lisp interpreter.
OMCSNetCPP is a C++ API and inference toolkit for accessing OMCSNet, a semantic network mined out of the Open Mind Common Sense knowledge base. The goal of this project is to provide a class library that allows programmers to easily add common sense reasoning capabilities to C++ applications.
LinkGrammar-WN is a lexicon expansion for the Link Grammar Parser. The Link Grammar Parser is a syntactic parser of the English language that is capable of handling a wide variety of syntactic constructions and is considered quite robust. The LinkGrammar-WN project aims to import lexical information from WordNet in an effort to increase the size of the LGP lexicon. This project is of interest to anyone interested in NLP (natural language parsing) of English text.
The OMCSNet-WordNet project aims to improve the quality of the OMCSNet dataset by using automated processes to map WordNet synonym sets to OMCSNet concepts and import additional semantic linkage data from WordNet. It is based on OMCSNet 1.2, a semantic network and inference toolkit written in Python/Java. OMCSNet currently contains over 280,000 separate pieces of common sense information extracted from the raw OMCS dataset. This project is also based on WordNet, an online lexical reference system that in recent years has become a popular tool for AI researchers.