The Agent Building and Learning Environment (ABLE) project at the IBM T.J. Watson research laboratory started in early 1999. The goals of the project were to produce a fast, re-usable and scalable toolkit for creating intelligent software applications. ABLE release 1.0 was posted on the IBM alphaWorks site in May, 2000. The ABLE research team has delivered regular updates to alphaWorks with early releases focused on the core framework and Swing-based tooling, moving on to several years of work on our ABLE rule language (ARL) and rule engines, followed by our ABLE distributed multi-agent platform and Eclipse-based tooling. The most recent work adds agent-based modeling and simulation extensions on top of the ABLE toolit and enhanced Eclipse 3.7 support.
ABLE software technology has been delivered in IBM products since 2002, and has been applied to areas such as autonomic computing, automotive diagnostics, communications trace analysis, system health monitoring, medical diagnostics, agent-based modeling and simulation, complex workload generation, business rules and policy, adding intelligence to pervasive computing devices and healthcare payments and incentives simulations.
The Agent Building and Learning Environment (ABLE) is a Java-based framework, component library, and productivity toolkit for building intelligent agents that can use machine learning and reasoning.
ABLE has several major components:
ABLE includes these tools:
Team over the years: Joe Bigus has been the main person behind ABLE throughout. Jeff Pilgrim and Don Schlosnagle helped Joe create much of the basic framework. Biplav incorporated his Planner4J planning framework into ABLE.