| Agent Building and Learning Environment (ABLE), a core IBM autonomic computing technology, has enhanced rules function, new Eclipse-based tooling, and major enhancements to the agent platform, including life cycle services, security, and persistence. ABLE is a Java framework, component library, and productivity tool kit for building intelligent agents using machine learning and reasoning. The ABLE framework provides a set of Java interfaces and base classes to build a library of JavaBeans called AbleBeans. The library includes AbleBeans for reading and writing text and database data, for data transformation and scaling, for rule-based inferencing using Boolean and fuzzy logic, and for machine learning techniques, such as neural networks, Bayesian classifiers, and decision trees. Developers can extend the provided AbleBeans or implement their own custom algorithms. Rule sets created using the ABLE Rule Language can be used by any of the provided inferencing engines, which range from simple if-then scripting to lightweight inferencing to heavyweight AI algorithms using pattern matching and unification. Java objects can be created and manipulated using ABLE rules. User-defined functions can be invoked from rules to enable external data to be read and actions to be invoked. How Does It Work? Core beans may be combined to create function-specific JavaBeans called AbleAgents. Developers can implement their own AbleBeans and AbleAgents and plug them into ABLE's Agent Editor. Graphical and text inspectors are provided in the Agent Editor so that bean input, properties, and output can be viewed as machine learning progresses or as values change in response to methods invoked in the interactive development environment. Application-level agents can be constructed from AbleBean and AbleAgent components using the ABLE Agent Editor or a commercial bean builder environment. AbleBeans can be called directly from applications or can run autonomously on their own thread. Events can be used to pass data or invoke methods and can be processed in a synchronous or asynchronous manner. The distributed AbleBeans and AbleAgents are as follows: Data beans: -
AbleImport reads data from flat text files. -
AbleDBImport reads data from SQL databases. -
AbleFilter filters, transforms, and scales data using translate template specifications. -
AbleExport writes data to flat text files. -
AbleTimeSeriesFilter collects periods of data for use in predicting future values. Learning beans: -
Back Propagation implements an enhanced back propagation algorithm used for classification and prediction. -
Decision tree creates a decision tree for classification. -
Naive Bayes learns a probabilistic model for classification. -
Radial Basis Function uses radial basis functions to adjust weights in a single, hidden-layer neural network for prediction. -
Self-Organizing Map clusters data using Gaussian neighborhood function. -
Temporal Difference Learning uses reinforcement learning for time series forecasting; gradient descent is used to adjust network weights. Rules beans inferencing engines include: Agents: -
Genetic search manipulates a population of genetic objects, which may include AbleBeans. -
Neural classifier uses back propagation to classify data. -
Neural clustering uses self-organizing maps to segment data. -
Neural prediction uses back propagation to build regression models. -
Script uses rule sets to define its init, process, and timer actions. -
JavaScript names JavaScripts to run when the agent's init, process, or timer actions are called. |
