Timelines Are Expressive Enough to Capture Action-Based Temporal Planning

Gigante, Nicola; Montanari, Angelo; Mayer, Marta Cialdea; Orlandini, Andrea

doi:10.1109/time.2016.18

Cited by 15 publications

(23 citation statements)

References 13 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A fair empirical comparison to FAPE is not possible here, due to the difference in representation language, and the HTN planning approach. Finally, we note that other researchers (Gigante et al 2016) have shown that timeline based languages are able to express PDDL problems, which is the complement of our work.…”

Section: Other Related Worksupporting

confidence: 56%

Efficiently Reasoning with Interval Constraints in Forward Search Planning

Coles

Martínez

Savas

et al. 2019

AAAI

View full text Add to dashboard Cite

In this paper we present techniques for reasoning natively with quantitative/qualitative interval constraints in statebased PDDL planners. While these are considered important in modeling and solving problems in timeline based planners; reasoning with these in PDDL planners has seen relatively little attention, yet is a crucial step towards making PDDL planners applicable in real-world scenarios, such as space missions. Our main contribution is to extend the planner OPTIC to reason natively with Allen interval constraints. We show that our approach outperforms both MTP, the only PDDL planner capable of handling similar constraints and a compilation to PDDL 2.1, by an order of magnitude. We go on to present initial results indicating that our approach is competitive with a timeline based planner on a Mars rover domain, showing the potential of PDDL planners in this setting.

show abstract

Section: Other Related Worksupporting

confidence: 56%

Efficiently Reasoning with Interval Constraints in Forward Search Planning

Coles

Martínez

Savas

et al. 2019

AAAI

View full text Add to dashboard Cite

show abstract

“…The computational complexity of other planning formalisms has been studied before, from classical (Bylander 1994) to hierarchical (Erol, Hendler, and Nau 1996), to timeline-based planning (Gigante et al 2016;2017). When comparing our results with the timeline-based planning paradigm in particular, it is interesting to note a similar complexity jump, with a problem that is EXPSPACE-complete over discrete time (Gigante et al 2017), but becomes undecidable over dense time (Bozzelli et al 2018).…”

Section: Related Workmentioning

confidence: 99%

Decidability and Complexity of Action-Based Temporal Planning over Dense Time

Gigante

Micheli

Montanari

et al. 2020

AAAI

Self Cite

View full text Add to dashboard Cite

This paper studies the computational complexity of temporal planning, as represented by PDDL 2.1, interpreted over dense time. When time is considered discrete, the problem is known to be EXPSPACE-complete. However, the official PDDL 2.1 semantics, and many implementations, interpret time as a dense domain. This work provides several results about the complexity of the problem, studying a few interesting cases: whether a minimum amount ϵ of separation between mutually exclusive events is given, in contrast to the separation being simply required to be non-zero, and whether or not actions are allowed to overlap already running instances of themselves. We prove the problem to be PSPACE-complete when self-overlap is forbidden, whereas, when allowed, it becomes EXPSPACE-complete with ϵ-separation and undecidable with non-zero separation. These results clarify the computational consequences of different choices in the definition of the PDDL 2.1 semantics, which were vague until now.

show abstract

“…-Reasoning about time requirements: Although both paradigms are able to explicitly take time into account when creating the plans, it has been demonstrated that timeline-based planning is expressive enough to capture actionbased temporal planning, while the contrary is yet unclear [13]. If complex temporal reasoning is needed, as in the case of quantitative temporal relations among goals (for example representing that a goal must be achieved between 10 and 15 s after another goal has been reached), timeline-based planning is usually a better approach.…”

Section: Selection Of the Planning Paradigmmentioning

confidence: 99%

Challenges on the Application of Automated Planning for Comprehensive Geriatric Assessment Using an Autonomous Social Robot

García-Olaya

Fuentetaja

García-Polo

et al. 2018

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

http : //wvv.pl g. inf.uc3m.es Abstract. Comprehensive Geriatric Assessment is a medical procedure to evaluate t he physical, social and psychological status of elder patients. One of it s phases consists of performing different tests t o the pat ient or relatives. In t his paper we present the challenges to apply Automated P lanning to control an autonomous robot helping t he clinician to perform such test s. On t he one hand the paper focuses on the modelling decisions t aken, from an init ial approach where each t est was encoded using slightly different domains, to the final unified domain allowing any test to be represented. On the other hand, the paper deals with practical issues arisen when executing the plans. Preliminary t ests performed with real users show that t he proposed approach is able to seamlessly handle t he patient-robot interaction in real time, recovering from unexpected events and adapt ing t o t he users' preferred input method, while being able to gather all the informat ion needed by t he clinician.

show abstract

Timelines Are Expressive Enough to Capture Action-Based Temporal Planning

Cited by 15 publications

References 13 publications

Efficiently Reasoning with Interval Constraints in Forward Search Planning

Efficiently Reasoning with Interval Constraints in Forward Search Planning

Decidability and Complexity of Action-Based Temporal Planning over Dense Time

Challenges on the Application of Automated Planning for Comprehensive Geriatric Assessment Using an Autonomous Social Robot

Contact Info

Product

Resources

About