Hybrid Planning and Scheduling

Abstract

PrefacePlanning 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: First, any planning and scheduling methodology can be functionally decomposed and mapped on the deficiency announcement and plan transformation generation principle, and second, the framework allows for an explicit declaration of planning strategies. We first investigate the flexibility of the extremely modular system design by instantiating the framework in a variety of system configurations including classical partial-order causal-link (POCL) planning, hierarchical task-network (HTN) planning, and classical scheduling.As a key feature, the presented approach provides a formally integrated treatment of action and state abstraction, thus naturally combining causality-focused reasoning with hierarchical, procedure-oriented methods. While the use of procedural knowledge allows to rely on well-known, predefined solutions to planning problems, the non-hierarchical methods provide the flexibility to come up with non-standard solutions and to complete under-specified problem instances, respectively. The resulting technique of hybrid planning is capable of constructing a plan's caus...

FAPE: a Constraint-based Planner for Generative and Hierarchical Temporal Planning

“…a flaw with no resolvers). VG06], and hierarchical planners, e.g., HiPOP [Bec+14] and PANDA [Sch09]. The differences between all those planners lie in four aspects: (i) their definition of flaws and resolvers, (ii) the type of constraints and propagation used to reason about variables in the plan, (iii) their internal representation of a chronicle, and (iv) the strategy for exploring the search space…”

HTN-like solutions for classical planning problems: An application to BDI agent systems

“…Whereas HTN systems are concerned with hypothetical off-line reasoning about actions and their potential interactions within a pursued plan for solving a task, BDI agent systems focus on the effective online execution of hierarchical agent programs in complex and dynamic environments. There are many BDI-style agent programming languages and systems available (e.g., [7,11,38]) as well as HTN planning systems (e.g., [35,36,40,43,15]).…”

Hybrid planning and distributed iterative repair for multi-robot missions with communication losses

“…Approaches that mixes POP and HTN, in a attempt to have the benefits of both, have been studied under the term of hybrid planning, for example by including hierarchical planning in a more general framework in UCP [34] or in a constraint satisfaction programming (CSP) planner in CHIMP [56]. Using the PANDA system, Schattenberg [55] conducted an empirical study of several search strategies in hybrid planning. The FAPE system [19] integrates closely planning and acting and do temporal planning with resource constraints.…”