Dynamic Controllability of Controllable Conditional Temporal Problems with Uncertainty

Cui, Jing; Haslum, Patrik

doi:10.1613/jair.1.11375

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…We do not cover PSTN extensions such as PSTNs with choices (Conrad, Shah, & Williams, 2009), conditional PSTNs (Combi, Posenato, Viganò, & Zavatteri, 2019), PSTNs with resource usage (Kumar, Wang, Kumar, Rogers, & Knoblock, 2018). Recent studies, such as that of Cui and Haslum (2019), has focused on controllability checking and computing dynamic decisions (or reliable policies) for dynamically controllable PSTNs, whereas we focus on the DSC or DDC of uncontrollable PSTNs.…”

Section: Landscape Of Ddc/dsc and Utility Measuresmentioning

confidence: 99%

Probabilistic Temporal Networks with Ordinary Distributions: Theory, Robustness and Expected Utility

Saint-Guillain¹,

Vaquero²,

Chien³

et al. 2021

jair

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Most existing works in Probabilistic Simple Temporal Networks (PSTNs) base their frameworks on well-defined, parametric probability distributions. Under the operational contexts of both strong and dynamic control, this paper addresses robustness measure of PSTNs, i.e. the execution success probability, where the probability distributions of the contingent durations are ordinary, not necessarily parametric, nor symmetric (e.g. histograms, PERT), as long as these can be discretized. In practice, one would obtain ordinary distributions by considering empirical observations (compiled as histograms), or even hand-drawn by field experts. In this new realm of PSTNs, we study and formally define concepts such as degree of weak/strong/dynamic controllability, robustness under a predefined dispatching protocol, and introduce the concept of PSTN expected execution utility. We also discuss the limitation of existing controllability levels, and propose new levels within dynamic controllability, to better characterize dynamic controllable PSTNs based on based practical complexity considerations. We propose a novel fixed-parameter pseudo-polynomial time computation method to obtain both the success probability and expected utility measures. We apply our computation method to various PSTN datasets, including realistic planetary exploration scenarios in the context of the Mars 2020 rover. Moreover, we propose additional original applications of the method.

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Section: Landscape Of Ddc/dsc and Utility Measuresmentioning

confidence: 99%

Probabilistic Temporal Networks with Ordinary Distributions: Theory, Robustness and Expected Utility

Saint-Guillain¹,

Vaquero²,

Chien³

et al. 2021

jair

View full text Add to dashboard Cite

show abstract

“…Previously, a centralized algorithm is proposed (Casanova et al 2016) that computes an optimal decoupling solution for MaSTNU by encoding both the constraints for finding a valid decoupling and the constraints for ensuring dynamic controllability (Cui and Haslum 2017) of the local networks in a single mixed-integer linear program (MILP). However, when privacy is of concern, and the agents do not want to expose the private parts of their local networks, a privacypreserving algorithm is desirable.…”

Section: Distributed Decoupling Algorithmmentioning

confidence: 99%

Privacy-Preserving Algorithm for Decoupling of Multi-Agent Plans with Uncertainty

Zhang

Williams

2021

ICAPS

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The execution of multi-agent plans often requires communication between agents in order to synchronize their tasks. In cases where communication is unreliable or undesirable, temporal decoupling algorithms allow agents to find a distributed execution strategy beforehand without requiring perfect communication on the fly. The state-of-the-art Multi-Agent Simple Temporal Network with Uncertainty (MaSTNU) framework extends the decoupling problem for Multi-Agent Simple Temporal Network (MaSTN) to allow the modeling of uncertain durations and allow agents to communicate when certain events occur and communication is available. However, the existing approach assumes centralized knowledge of the MaSTNU, whereas in the multi-agent context, privacy is an important concern. In this paper, we propose a distributed, privacy-preserving algorithm for finding distributed execution strategies for MaSTNU. Experiments also showed significant speed-up of the proposed algorithm when the multi-agent plan is loosely coupled and mostly private.

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“…Recently a new temporal formalism was proposed. The CCTPU formalism (Cui and Haslum 2017) can model STNUs with duration / edge alternatives, where subnetworks are chosen dynamically during execution. But this high expressivity comes at a price: Its verification algorithms are shown by benchmarks to scale like generate-andtest methods.…”

Section: Related Workmentioning

confidence: 99%

Planning with Temporal Uncertainty, Resources and Non-Linear Control Parameters

Nilsson

Kvarnström

Doherty

2018

ICAPS

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We consider a general and industrially motivated class of planning problems involving a combination of requirements that can be essential to autonomous robotic systems planning to act in the real world: Support for temporal uncertainty where nature determines the eventual duration of an action, resource consumption with a non-linear relationship to durations, and the need to select appropriate values for control parameters that affect time requirements and resource usage. To this end, an existing planner is extended with support for Simple Temporal Networks with Uncertainty, Timed Initial Literals, and temporal coverage goals. Control parameters are lifted from the main combinatorial planning problem into a constraint satisfaction problem that connects them to resource usage. Constraint processing is then integrated and interleaved with verification of temporal feasibility, using projections for partial temporal awareness in the constraint solver.

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Dynamic Controllability of Controllable Conditional Temporal Problems with Uncertainty

Cited by 3 publications

References 25 publications

Probabilistic Temporal Networks with Ordinary Distributions: Theory, Robustness and Expected Utility

Probabilistic Temporal Networks with Ordinary Distributions: Theory, Robustness and Expected Utility

Privacy-Preserving Algorithm for Decoupling of Multi-Agent Plans with Uncertainty

Planning with Temporal Uncertainty, Resources and Non-Linear Control Parameters

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