Software Foundation Libraries for Intelligent Systems
We describe a set of object-oriented software foundation libraries for the design and modeling of complex and intelligent systems. The basic thesis underlying the libraries is that the language required to represent and design all such systems in a scalable way is the language of sparse recursive graphs with the corresponding local message passing propagation algorithms. We provide some formal arguments to support this thesis going from decision theory, to Bayesian statistics, to probabilistic graphical models. The current version of the libraries consist of 6 different modules: ObjectCost, ObjectStat, ObjectNet, ObjectComp, ObjectData, and ObjectGraph. The libraries support most of the standard models used in artificial intelligence and machine learning, such as neural networks, hidden Markov models, and Bayesian networks. The libraries are being used in real data mining applications and products, and are actively being developed and extended.