Bringing planning to autonomic applications with ABLE

Planning has received tremendous interest as a research area within AI over the last three decades but it has not been applied commercially as widely as its other AI counterparts like learning or data mining. The reasons are many: the utility of planning in business applications was unclear, the planners used to work best in small domains and there was no general purpose planning and execution infrastructure widely available. Much has changed lately. Compelling applications have emerged, e.g., computing systems have become so complex that the IT industry recognizes the necessity of deliberative methods to make these systems self-configuring, self-healing, self-optimizing and self-protecting. Planning has seen an upsurge in the last decade with new planners that are orders of magnitude faster than before and are able to scale this performance to complex domains, e.g., those with metric and temporal constraints. However, planning and execution infrastructure is still tightly tied to a specific application which can have its own idiosyncrasies. In this paper, we fill the infrastructural gap by providing a domain independent planning and execution environment that is implemented in the ABLE agent building toolkit, and demonstrate its ability to solve practical business applications. The planning-enabled ABLE is publicly available and is being used to solve a variety of planning applications in IBM including the self-management/autonomic computing scenarios.