Symbolic discrete-time planning with continuous numeric action parameters for agent-controlled processes

Abstract In industrial domains such as manufacturing control, a trend away from centralized planning and scheduling towards more flexible distributed agent-based approaches could be observed over recent years. To be of practical relevance, the local control mechanisms of the autonomous agents must be able to dependably adhere and dynamically adjust to complex numeric goal systems like business key performance indicators in an economically beneficial way. However, planning with numeric state variables and objectives still poses a challenging task within the field of artificial intelligence (AI). In this article, a new general-purpose AI planning approach is presented that operates in two stages and extends existing domain-independent modeling formalisms like PDDL with continuous (i.e., infinite-domain) numeric action parameters, which are currently still unsupported by state-of-the-art AI planners. In doing so, it enables the solution of mathematical optimization problems at the action level of the planning tasks, which are inherent to many real-world control problems. To deal with certain difficulties concerning reliable and fast detection of action applicability that arise when planning with real-valued action parameters, the implemented planner allows resorting to an adjustable “satisficing” strategy by means of partial execution and subsequent repair of infeasible plans over the course of time. The functioning of the system is evaluated in a multi-agent simulation of a shop floor control scenario with focus on the effects the possible problem cases and different degrees of satisficing have on attained plan quality and total planning time. As the results demonstrate the basic practicability of the approach for the given setting, this contribution constitutes an important step towards the effective and dependable integration of complex numeric goal systems and non-linear multi-criteria optimization tasks into autonomous agent-controlled industrial processes in a reusable, domain-independent way.