ABLE is a Java framework, component library, and productivity tool kit for building intelligent agents using machine learning and reasoning. The ABLE research project is made available by the IBM T. J. Watson Research Center.

The ABLE framework provides a set of Java interfaces and base classes used 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 light-weight inferencing to heavy-weight 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 and AbleDBExport write data to flat text files and SQL databases.
• AbleTimeSeriesFilter collects periods of data for use in predicting future values.

Learning beans:

• Back Propagation implements enhanced back propagation algorithm used for classification and prediction.
• Decision tree creates a decision tree for classification.
• Naive Bayes learns a probabalistic 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.
• emporal Difference Learning uses reinforcement learning for time series forecasting; gradient descent is used to adjust network weights.

Rules beans inferencing engines include:

• Backward chaining:
• Forward chaining
• Forward chaining with working memory
• Forward chaining with working memory and Rete'-based pattern matching
• Planning
• Predicate logic
• Fuzzy logic
• Script

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.
• Rule agent contains a rule set whose rule blocks define its init, process, and timer actions
• Script uses rule sets to define its init, process, and timer actions.
• JavaScript names JavaScripts to run when the agent's init, process, or time actions are called.

The development team can be contacted by email at ableinfo@us.ibm.com or newsgroup at news://forums.ibm.com/forums.software.able.

Also see the developerWorks articles:
Adding rules to applications: Use the ABLE Rule Language to write simple business rules or more complex inferencing rules.

The features and facets of the Agent Building and Learning Environment (ABLE): Learn about the major features and facets of the Agent Building and Learning Environment (ABLE), including the ABLE architecture and how to manipulate data beans, rule beans, and learning beans to be used in a wide variety of applications.

Use autonomic computing for problem determination: Perform root-cause analysis with the Autonomic Management Engine and ABLE components.

Joseph P. Bigus, Ph.D., is a senior technical staff member at the IBM T. J. Watson Research Center in Hawthorne, N. Y., where he is leader of the ABLE research project. He is a member of the IBM Academy of Technology and is an IBM Master Inventor with over 20 U.S. patents. Dr. Bigus was an architect of the IBM Neural Network Utility and Intelligent Miner for Data products. He has written two books: Data Mining with Neural Networks (McGraw-Hill) and Constructing Intelligent Agents with Java (Wiley).

Don Schlosnagle is a software engineer at IBM Software Development lab in Rochester, MN. He wrote, in Common Lisp, a PC-based natural language DB2 query program that was successfully marketed by IBM as a PRPQ. Mr. Schlosnagle has since worked on the Neural Network Utility and on Intelligent Miner for Data, where he contributed the fuzzy logic inference system for evaluating proposed neural network architectures. A greatly enhanced version of the fuzzy system is included in ABLE.

Jeff Pilgrim is a software engineer at IBM Software Development lab in Rochester, MN. His development experience includes work on Intelligent Miner for Data, Neural Network Utility, wide-area wireless computing, and Management Central. Previously, he was a developer and architect for the 9221, 9370, and AS/400 configurators, as well as for numerous internal industrial engineering applications.

Irina Rish, Ph.D., is a research staff member at the IBM T. J. Watson Research Center. Her research interests include probabilistic reasoning in Bayesian networks, constraint satisfaction and optimization, machine learning, and practical applications, including performance management in distributed computer systems.

W. Nathaniel (Nat) Mills, III, is a senior software engineer at the IBM T. J. Watson Research Center. He designed and co-developed the Page Detailer Web performance analysis software that is shipped with WebSphere Studio, Advanced Edition. Mr. Mills's research interests include systems management and "rationale management," which seeks to expose the reasoning that enables the making of decisions.

Jim Hanson, Ph.D., is a research staff member at the IBM T. J. Watson Research Center. His current research interests include conversation support for software agents, simulation and analysis of complex systems, emergent phenomena in distributed computation, and autonomic computation.

Richard Goodwin, Ph.D., is a research staff member at the IBM T. J. Watson Research Center. Since joining IBM, Dr. Goodwin has worked on agent-based optimization (Asynchronous Teams of Agents), electronic marketplaces, and decision-support systems. His current focus is on semantic Web representations and semantic Web services.

Biplav Srivastava, Ph.D., is a research staff member at the IBM India Research Center in New Delhi, India. His current focus is on practical planning for business applications and dynamic process/data integration.

Please email all questions, comments, and suggestions to ableinfo@us.ibm.com, or submit them to the alphaWorks forum for ABLE.