Scaling up Planning by teasing out Resource Scheduling

Planning consists of an action selection phase where actions are selected and ordered to reach the desired goals, and a resource allocation phase where enough resources are assigned to ensure the successful execution of the chosen actions. Most planners do not distinguish between these phases, and perform both action selection and resource assignment employing the same algorithm. We shall show that this strategy severely curtails the scale-up potential of existing planners, including such recent ones as Graphplan and Blackbox. In response, we propose a novel planning framework in which resource allocation is teased apart from planning, and is handled in a separate "scheduling" phase. We avoid resource constraints during planning and produce an abstract plan that can correctly achieve the goals but for the resource constraints. Next, based on the actual resource availability, the abstract plan will be allocated resources to produce an executable plan. Our approach not only preserves bot..