Hybrid planning and scheduling

Planning and scheduling are well-established disciplines in the field of Artificial Intelligence. They provide flexibility, robustness, and effectiveness to complex software systems in a variety of application areas. While planning is the process of finding a course of action that achieves a goal or performs a specified task, scheduling deals with the assignment of resources and time to given activities, taking into account resource restrictions and temporal dependencies. In other words, planning focuses on reasoning about causal structures and identifying the necessary actions for achieving a specific goal; scheduling concentrates on resource consumption and production for optimizing a coherent parameter assignment of a plan. As successful these techniques clearly are, the actual demands of complex, real-world applications go far beyond the potential of these single methods, however. They require an adequate integration of these problem solving methods as well as a combination of different planning and scheduling paradigms. Particularly important are abstraction-based, hierarchical approaches because of both their expressive knowledge representation and their efficiency in finding solutions. Current state-of-the-art systems rarely address the question of method integration; isolated approaches do so only in ad hoc implementations and mostly lack a proper formal basis. This thesis presents a formal framework for plan and schedule generation based on a well-founded conceptualization of refinement planning: An abstract problem specification is transformed stepwise into a concrete, executable solution. In each refinement step, plan deficiencies identify faulty or under-developed parts of the plan, which in turn triggers the generation of transformation operators that try to resolve them. All involved entities are explicitly represented and therefore transparent to the framework. This property allows for two novel aspects of our approach: [...

A Unifying Framework For Hybrid Planning And Scheduling

Abstract. Many real-world application domains that demand planning and scheduling support do not allow for a clear separation of these capabilities. Typically, an adequate mixture of both methodologies is required, since some aspects of the underlying planning problem imply consequences on the scheduling part and vice versa. Several integration efforts have been undertaken to couple planning and scheduling methods, most of them using separate planning and scheduling components which iteratively exchange partial solutions until both agree on a result. This paper presents a framework that provides a uniform integration of hybrid planning –the combination of operator based partial order planning and abstraction based hierarchical task network planning – and a hierarchical scheduling approach. It is based on a proper formal account of refinement planning, which allows for the formal definition of hybrid planning, scheduling, and search strategies. In a first step, the scheduling functionality is used to produce plans that comply with time restrictions and resource bounds. We show how the resulting framework is thereby able to perform novel kinds of search strategies that opportunistically interleave what used to be separate planning and scheduling processes.

Dealing with continuous resources in AI planning

Abstract. This paper presents an approach towards probabilistic planning with continuous resources. It adopts stochastic concepts for continuous probabilities and integrates them into a STRIPS-based planning framework. The approach enables the construction of plans that are guaranteed to meet certain probability thresholds w.r.t. the consumption of critical resources. Furthermore, the consumption probabilities of multiple resources can be accumulated and thus an overall probability for a successful execution of an aggregate plan can be computed. We extend our approach to HTN-based planning and show how heuristics can be derived that lead to plans with a minimized average value/variance of their overall resource consumption.

From abstract crisis to concrete relief – A preliminary report on combining state abstraction and HTN planning

Abstract. Flexible support for crisis management can definitely be improved by making use of advanced planning capabilities. However, the complexity of the underlying domain often causes intractable efforts in modeling the domain as well as a huge search space to be explored by the system. A way to overcome these problems is to impose a structure not only according to tasks but also according to relationships between and properties of the objects involved, thereby using so-called decomposition axioms. We outline the prototype of a system that is capable of tackling planning for complex application domains. It is based on a well-founded combination of action and state abstractions. The paper presents the basic techniques and provides a formal semantic foundation of the approach. It introduces the planning system and illustrates its underlying principles by examples taken from the crisis management domain used in our ongoing project.

On the Identification and Use of Hierarchical Resources in Planning and Scheduling

Many real-world planning applications have to deal with resource allocation problems, and so does planning in the domain of crisis management assistance. In order to support resource allocation in these kind of applications, we present a new approach to the integration of scheduling capabilities and planning. The proposed methodology relies on a hybrid planner, which combines action and state abstraction by integrating hierarchical task network (HTN) planning and state based partial order causal link (POCL) planning into a common framework. We extend the abstraction mechanism of the planner to different kinds of abstraction for resources, namely subsumption, approximation, qualification, and aggregation