FramerD is a semi-structured object database integrated with a Scheme-based scripting language which supports multi-lingual programming (with pervasive Unicode), a stable module system for programming in the large, distributed applications (via an extensible RPC protocol), non-deterministic (PROLOG-like) evaluation for search and set operations, multi-threaded program execution, extensive tools for text and language analysis, built-in HTML/XML/MIME parsers, and intuitive (CGI- and FastCGI-based) Web scripting. The built-in object database robustly supports millions of objects and indexed access to those objects, both through disk files and networked servers.
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.
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.
aiParts is a set of C++ classes that can be used to implement artificial intelligence, including classes that implement the HighHope technique. Sample programs include "find the shortest path" and "assign people and/or equipment to projects". A problem assembled from subclasses of the High-Hope classes knows how to solve itself by searching for a good solution.