Quantifying Degrees of Controllability in Temporal Networks with Uncertainty

Akmal, Shyan; Ammons, Savana; Li, Hemeng; Boerkoel, James C.

doi:10.1609/icaps.v29i1.3456

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…STNUs are dynamically controllable if we can find a scheduling strategy, where some decisions are contingent on the outcomes of uncertain events during execution, that guarantees success. Dynamic controllability is a less restrictive property than strong controllability; the decision to execute a timepoint can be deferred until information about uncontrollable timepoints is known (Vidal and Ghallab 1996;Vidal 2000;Morris, Muscettola, and Vidal 2001;Morris and Muscettola 2005;Morris 2014;Akmal et al 2019).…”

Section: Temporal Networkmentioning

confidence: 99%

DREAM: An Algorithm for Mitigating the Overhead of Robust Rescheduling

Abrahams

Chu

Diehl

et al. 2019

ICAPS

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Generating and executing temporal plans is difficult in uncertain environments. The current state-of-the-art algorithm for probabilistic temporal networks maintains a high success rate by rescheduling frequently as uncertain events are resolved, but this approach involves substantial resource overhead due to computing and communicating new schedules between agents. Aggressive rescheduling could thus reduce overall mission duration or success in situations where agents have limited energy or computing power, and may not be feasible when communication is limited. In this paper, we propose new approaches for heuristically deciding when rescheduling is most efficacious. We propose two compatible approaches, Allowable Risk and Sufficient Change, that can be employed in combination to compromise between the computation rate, the communication rate, and success rate for new schedules. We show empirically that both approaches allow us to gracefully trade success rate for lower computation and/or communication as compared to the state-of-the-art dynamic scheduling algorithm.

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Section: Temporal Networkmentioning

confidence: 99%

DREAM: An Algorithm for Mitigating the Overhead of Robust Rescheduling

Abrahams

Chu

Diehl

et al. 2019

ICAPS

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SMT-Based Repair of Disjunctive Temporal Networks with Uncertainty: Strong and Weak Controllability

Sumic,

Cimatti,

Micheli

et al. 2024

Lecture Notes in Computer Science

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Dynamic Control of Probabilistic Simple Temporal Networks

Gao¹,

Popowski²,

Boerkoel³

2020

AAAI

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The controllability of a temporal network is defined as an agent's ability to navigate around the uncertainty in its schedule and is well-studied for certain networks of temporal constraints. However, many interesting real-world problems can be better represented as Probabilistic Simple Temporal Networks (PSTNs) in which the uncertain durations are represented using potentially-unbounded probability density functions. This can make it inherently impossible to control for all eventualities. In this paper, we propose two new dynamic controllability algorithms that attempt to maximize the likelihood of successfully executing a schedule within a PSTN. The first approach, which we call Min-Loss DC, finds a dynamic scheduling strategy that minimizes loss of control by using a conflict-directed search to decide where to sacrifice the control in a way that optimizes overall success. The second approach, which we call Max-Gain DC, works in the other direction: it finds a dynamically controllable schedule and then attempts to progressively strengthen it by capturing additional uncertainty. Our approaches are the first known that work by finding maximally dynamically controllable schedules. We empirically compare our approaches against two existing PSTN offline dispatch approaches and one online approach and show that our Min-Loss DC algorithm outperforms the others in terms of maximizing execution success while maintaining competitive runtimes.

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Quantifying Degrees of Controllability in Temporal Networks with Uncertainty

Cited by 4 publications

References 18 publications

DREAM: An Algorithm for Mitigating the Overhead of Robust Rescheduling

DREAM: An Algorithm for Mitigating the Overhead of Robust Rescheduling

SMT-Based Repair of Disjunctive Temporal Networks with Uncertainty: Strong and Weak Controllability

Dynamic Control of Probabilistic Simple Temporal Networks

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