The PANDA Framework for Hierarchical Planning

Höller, Daniel; Behnke, Gregor; Bercher, Pascal; Biundo, Susanne

doi:10.1007/s13218-020-00699-y

Cited by 11 publications

(7 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We therefore integrated the generation mechanisms into the PANDA framework 6 (Höller et al 2021) and combined them with the progression search algorithm described by Höller et al (2020, Alg. 3).…”

Section: Discussionmentioning

confidence: 99%

Landmark Generation in HTN Planning

Höller

Bercher

2021

AAAI

Self Cite

View full text Add to dashboard Cite

Landmarks (LMs) are state features that need to be made true or tasks that need to be contained in every solution of a planning problem. They are a valuable source of information in planning and can be exploited in various ways. LMs have been used both in classical and hierarchical planning, but while there is much work in classical planning, the techniques in hierarchical planning are less evolved. We introduce a novel LM generation method for Hierarchical Task Network (HTN) planning and show that it is sound and incomplete. We show that every complete approach is as hard as the co-class of the underlying HTN problem, i.e. coNP-hard for our setting (while our approach is in P). On a widely used benchmark set, our approach finds more than twice the number of landmarks than the approach from the literature. Though our focus is on LM generation, we show that the newly discovered landmarks bear information beneficial for solvers.

show abstract

Section: Discussionmentioning

confidence: 99%

Landmark Generation in HTN Planning

Höller

Bercher

2021

AAAI

Self Cite

View full text Add to dashboard Cite

show abstract

“…We implemented the presented approach autoSym and empirically compared it with other optimal (TO)HTN planners. 4 autoSym is based on the PANDA planning framework in its C++ version pandaPI (Höller et al 2021). Since autoSym operates on a grounded model, we use parser and grounder of pandaPI (Behnke et al 2020).…”

Section: Empirical Evaluationmentioning

confidence: 99%

Symbolic Search for Optimal Total-Order HTN Planning

Behnke

Speck

2021

AAAI

Self Cite

View full text Add to dashboard Cite

Symbolic search has proven to be a useful approach to optimal classical planning. In Hierarchical Task Network (HTN) planning, however, there is little work on optimal planning. One reason for this is that in HTN planning, most algorithms are based on heuristic search, and admissible heuristics have to incorporate the structure of the task network in order to be informative. In this paper, we present a novel approach to optimal (totally-ordered) HTN planning, which is based on symbolic search. An empirical analysis shows that our symbolic approach outperforms the current state of the art for optimal totally-ordered HTN planning.

show abstract

“…Among HTN planners, the Simple Hierarchical Ordered Planner (SHOP) [21] is the most well-known planner that has been extended with numerous additional functionality, such as partially ordered tasks in SHOP2 [22], preferences in HTNPlan-P [23], etc. Other hierarchical planners include SIADEX [24], SH [25], and PANDA [26].…”

Section: Ai Planningmentioning

confidence: 99%

“…Therefore, we assign the "+" score to the proposed approach. Most of existing planning artefacts (e.g., Pan-daPIParser [26]) depend on operating system libraries, which may be due to the programming language used (e.g., C++) and the design of the artefact's architecture. Rare are the planning artefacts that offer encapsulated solutions (e.g., virtual images).…”

Section: Reusabilitymentioning

confidence: 99%

Software Architecture for Next-Generation AI Planning Systems

Graef,

Georgievski

2021

Preprint

View full text Add to dashboard Cite

Artificial Intelligence (AI) planning is a flourishing research and development discipline that provides powerful tools for searching a course of action that achieves some user goal. While these planning tools show excellent performance on benchmark planning problems, they represent challenging software systems when it comes to their use and integration in real-world applications. In fact, even in-depth understanding of their internal mechanisms does not guarantee that one can successfully set up, use and manipulate existing planning tools. We contribute toward alleviating this situation by proposing a service-oriented planning architecture to be at the core of the ability to design, develop and use next-generation AI planning systems. We collect and classify common planning capabilities to form the building blocks of the planning architecture. We incorporate software design principles and patterns into the architecture to allow for usability, interoperability and reusability of the planning capabilities. Our prototype planning system demonstrates the potential of our approach for rapid prototyping and flexibility of system composition. Finally, we provide insight into the qualitative advantages of our approach when compared to a typical planning tool.

show abstract

The PANDA Framework for Hierarchical Planning

Cited by 11 publications

References 32 publications

Landmark Generation in HTN Planning

Landmark Generation in HTN Planning

Symbolic Search for Optimal Total-Order HTN Planning

Software Architecture for Next-Generation AI Planning Systems

Contact Info

Product

Resources

About