WEBWEAVR-III is a research toolkit that supports the construction of Bayesian networks, inference in standard and dynamic Bayesian networks and decomposable Markov networks, the construction and verification of multiply sectioned Bayesian networks (MSBNs), inference in multi-agent MSBNs, and learning decomposable Markov networks.
DREAM (The Distributed Resource Evolutionary Algorithm Machine) seeks to provide the technology and software infrastructure necessary to support the next generation of evolving infohabitants in a way that makes that infrastructure universal, open, and scalable. It will use existing hardware infrastructure in a much more efficient manner, by utilising otherwise-unused CPU time. It will allow infohabitants to co-operate, communicate, negotiate and trade; and emergent behaviour is expected to result. It is expected that there will be an emergent economy that results from the provision and use of CPU cycles by infohabitants and their owners. The DREAM infrastructure will be evaluated with new work on distributed data mining, distributed scheduling, and the modelling of economic and social behaviour.
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.
lemontree provides a very fast Java class for the Walsh Hadamard transform (WHT) and O'Connor transform (OCT). You can regard the OCT as a black box which turns arbitrary numerical data into data with a Gaussian distribution. There are inverse transforms, also. It has many applications, such as Random Projections, Compressive Sensing, Neural Nets, and Genetic algorithms.
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.
Lindenmayer Systems in Python provides a simple implementation of Lindenmayer systems (also called "L-systems" or "substitution systems"). In basic form, a Lindenmayer system consists of a starting string of symbols from an alphabet which has repeated transitions applied to it, specified by a list of transition search-and-replace rules. In addition to the standard formulation, two alternative implementations are included: sequential systems (in which at most one rule is applied) and tag systems (in which the transition only takes place at the beginning and end of the string). Despite being implemented entirely in Python, for reasonable rules on a modern machine, the system is capable of running thousands of generations per second. Lindenmayer systems are found in artificial intelligence and artificial life and can be used to generate fractal patterns (usually via mapping symbols from the alphabet to turtle commands), organic-looking patterns that can simulate plants or other living things, or even music.