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.
DEVS has been developed for over a year to serve as an experimental framework for natural systems modeling techniques. It enables discrete event, general purpose, object oriented, component based, GIS connected, and collaborative visual simulation model development and execution. The sample model implementation shows that this experimental environment can be used for solving any complex problems solvable by discrete-event simulation, but it is especially suited for natural system simulation. Currently only hierarchical block and cellular models are modeled and simulated, but a multi-layered modeling paradigm for spatially distributed systems (with vector and cellular models) will eventually be implemented in the environment.
The Artificial Knowledge Interface for Reasoning Applications (AKIRA) project aims to create a C++ development framework to build cognitive architectures and complex artificial intelligent agents featuring KQML, fuzzy logic, neural networks, fuzzy cognitive maps, and DIPRA. DIPRA is a distributed version of the BDI (Belief Desire Intention) goal oriented model.
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.