Hybrid Planning Using Flexible Strategies

Schattenberg, Bernd; Weigl, Andreas; Biundo, Susanne

doi:10.1007/11551263_21

Cited by 12 publications

(3 citation statements)

References 4 publications

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“…In that work, POP is modified to perform HTN planning [9,57,58,104]. In contrast, Fox uses the term "hybrid planning" for a combination of planning approaches having specialized solvers, such as optimization algorithms and model-checking [35].…”

Section: Different Notions Of Hybrid Planningmentioning

confidence: 99%

Hybrid Planning in Self-Adaptive Systems

Pandey¹

2017

2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS*W)

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Section: Different Notions Of Hybrid Planningmentioning

confidence: 99%

Hybrid Planning in Self-Adaptive Systems

Pandey¹

2017

2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS*W)

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“…Our starting point for recruiting evaluation candidates lies in a small series of experiments that have been conducted in previous work on hybrid planning strategies, also in the context of hybrid system configurations that do not insert new tasks [231,234]. We extend the respective component collection such that it now includes most of the flexible HotSpot and HotZone strategies; the complete list of participating modification and plan selection functions is given in Table 6.1.…”

Section: Strategy Combinationsmentioning

confidence: 99%

Hybrid Planning and Scheduling

Schattenberg¹

2015

Künstl Intell

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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...

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“…They typically sacrifice completeness of the search procedure for the sake of finding "good" solutions and to find them very efficiently, respectively. Their technical details are out of the scope of this presentations, hence we refer the reader to (Schattenberg et al (2005(Schattenberg et al ( , 2007). It is however important to point out that planning strategies can be designed to operate on qualities of the examined plans as well as of the refinements; we will draw on both in the sections below.…”

Section: Formal Frameworkmentioning

confidence: 99%

Planning Models for Two-Way Avoidance and Reversal Learning

Schattenberg

Schulz

Brechmann

et al. 2012

IFAC Proceedings Volumes

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Reinforcement learning models can explain various aspects of two-way avoidance learning but do not provide a rationale for the relationship found between the dynamics of initial learning and those of reversal learning. Artificial Intelligence planning offers a novel way to conceptualize the learners' cognitive processes by providing an explicit representation of and reasoning about internal processing stages. Our hybrid planning and plan repair approach demonstrates that the empirically found relationships could be motivated from a consistent theoretical framework.

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Hybrid Planning Using Flexible Strategies

Cited by 12 publications

References 4 publications

Hybrid Planning in Self-Adaptive Systems

Hybrid Planning in Self-Adaptive Systems

Hybrid Planning and Scheduling

Planning Models for Two-Way Avoidance and Reversal Learning

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