Asimulator is a simulator for intelligent agents, useful to practice search algorithms, in AI courses, or for fun. The agent's goal is to understand precepts and respond with actions in a virtual world (consisting of a grid up to 129x129) to maximize a score. The simulator opens a socket, so any language can be used for agents. (Samples in Ada are included.) Agent debug output can be shown. Both text in a log window and symbols on the map can be used to visualize thoughts.
Apertium is a machine translation platform, initially aimed at related-language pairs, but recently expanded to deal with more divergent language pairs (such as English-Catalan). The platform provides a language-independent machine translation engine, tools to manage the linguistic data necessary to build a machine translation system for a given language pair, and linguistic data for a growing number of language pairs.
Algraeph is a tool for manual alignment of linguistic graphs, such as phrase structure trees or dependency structures, where each node corresponds to a subsequence of the analyzed input sentence. It allows you to express the similarity between two graphs by aligning their nodes and attaching relation labels to these alignments. Graphs are read from one or more graphbanks (or treebanks) in the GraphML or Alpino formats. Alignment relations are user-defined and are stored in a simple XML format, which can be used for further processing. The resulting parallel graph corpus is a useful data set for many tasks in computational linguistics and natural language processing.
The XEVM is an XML processing engine. It's a multi-threaded, Pub/Sub environment for dynamic programming on an event-driven state machine with TCP communications, tight fault free memory management, powerful set algebra, and a magical database. It is 100% C++ (25,000 LOC), with a thin porting layer; there are implementations for POSIX (Mac/Linux) and Win32. The XEVM is for processing XEPL (the Xepl Engine Programming Language).
RegMAS (Regional Multi Agent Simulator) is a spatially explicit multi-agent model framework, designed for long-term simulations of effects of government policies over agricultural systems (e.g. farm sizes, incomes, land use). RegMAS uses a profit-maximization algorithm to derive farmers' behaviors. Using RegMAS, researchers can write regional-specific models, adapting the pool of activities and resources to their specific region.
The main goal of the Cognitive Vision project is to improve the results of artificial intelligence. The current scope is to equip the abilities of the AIBO (robot dog of the Sony) to explore its environment and to recognize the local position (e.g. navigation in a room). The first trial is to use and develop image processing methods with a single webcam and to apply these techniques with AIBO.
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.