Instantaneous Reaction-Time in Dynamic-Consistency Checking of Conditional Simple Temporal Networks

Cairo, Massimo; Comin, Carlo; Rizzi, Roméo

doi:10.1109/time.2016.16

Cited by 3 publications

(3 citation statements)

References 15 publications

(44 reference statements)

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“…A dynamically consistent CSTN is one that has an execution strategy that guarantees that all relevant constraints will be satisfied no matter which values are observed in real time. This paper focuses on π-dynamic strategies, which can react instantaneously to observations (Cairo, Comin, and Rizzi 2016). The full set of definitions is given in the Appendix.…”

Section: The Dynamic Consistency Of Cstnsmentioning

confidence: 99%

Faster Dynamic-Consistency Checking for Conditional Simple Temporal Networks

Hunsberger

Posenato

2020

ICAPS

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A Conditional Simple Temporal Network (CSTN) is a structure for representing and reasoning about time in domains where temporal constraints may be conditioned on outcomes of observations made in real time. A CSTN is dynamically consistent (DC) if there is a strategy for executing its time-points such that all relevant constraints will necessarily be satisfied no matter which outcomes happen to be observed. The literature on CSTNs contains only one sound-and-complete DC-checking algorithm that has been implemented and empirically evaluated. It is a graph-based algorithm that propagates labeled constraints/edges. A second algorithm has been proposed, but not evaluated. It aims to speed up DC checking by more efficiently dealing with so-called negative q-loops.This paper presents a new two-phase approach to DC-checking for CSTNs. The first phase focuses on identifying negative q-loops and labeling key time-points within them. The second phase focuses on computing (labeled) distances from each time-point to a single sink node. The new algorithm, which is also sound and complete for DC-checking, is then empirically evaluated against both pre-existing algorithms and shown to be much faster across not only previously published benchmark problems, but also a new set of benchmark problems. The results show that, on DC instances, the new algorithm tends to be an order of magnitude faster than both existing algorithms. On all other benchmark cases, the new algorithm performs better than or equivalently to the existing algorithms.

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Section: The Dynamic Consistency Of Cstnsmentioning

confidence: 99%

Faster Dynamic-Consistency Checking for Conditional Simple Temporal Networks

Hunsberger

Posenato

2020

ICAPS

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“…Finally, in [6], it is introduced and studied π-DC, a sound notion of dynamic consistency with an instantaneous reaction time, i.e., one in which the Planner is allowed to react to any observation at the same instant of time in which the observation is made. It turns out that π-DC is not equivalent to ǫ-DC with ǫ = 0, and that the latter is actually inadequate for modeling an instantaneous reaction-time.…”

Section: Related Workmentioning

confidence: 99%

Checking dynamic consistency of conditional hyper temporal networks via mean payoff games

Comin¹,

Rizzi²

2018

Information and Computation

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Conditional Simple Temporal Network (CSTN) is a constraint-based graph-formalism for conditional temporal planning. It offers a more flexible formalism than the equivalent CSTP model of [29], from which it was derived mainly as a sound formalization. Three notions of consistency arise for CSTNs: weak, strong, and dynamic. Dynamic consistency is the most interesting notion, but it is also the most challenging and it was conjectured to be hard to assess.[29] gave a doubly-exponential time algorithm for checking dynamic consistency in CSTNs and to produce an exponentially sized dynamic execution strategy whenever the input CSTN is dynamically-consistent. CSTNs may be viewed as an extension of Simple Temporal Networks (STNs) [16], directed weighted graphs where nodes represent events to be scheduled in time and arcs represent temporal distance constraints between pairs of events. Recently, STNs have been generalized into Hyper Temporal Networks (HyTNs), by considering weighted directed hypergraphs where each hyperarc models a disjunctive temporal constraint named hyperconstraint; being directed, the hyperarcs can be either multi-head or multi-tail. The computational equivalence between checking consistency in HyTNs and determining winning regions in Mean Payoff Games (MPGs) was also pointed out; MPGs are a family of 2-player infinite pebble games played on finite graphs, which is well known for having applications in model-checking and formal verification. In this work we introduce the Conditional Hyper Temporal Network (CHyTN) model, a natural extension and generalization of both the CSTN and the HyTN model which is obtained by blending them together. We show that deciding whether a given CSTN or CHyTN is dynamically-consistent is coNP-hard; and that deciding whether a given CHyTN is dynamically-consistent is PSPACE-hard, provided that the input instances are allowed to include both multi-head and multi-tail hyperarcs. In light of this, we continue our study by focusing on CHyTNs that allow only multi-head hyperarcs, and we offer the first deterministic (pseudo) singly-exponential time algorithm for the problem of checking the dynamic consistency of such CHyTNs, also producing a dynamic execution strategy whenever the input CHyTN is dynamically-consistent. Since CSTNs are a special case of CHyTNs, as a byproduct this provides the first sound-and-complete (pseudo) singly-exponential time algorithm for checking dynamic consistency in CSTNs. The proposed algorithm is based on a novel connection between CHyTNs and MPGs; due to the existence of efficient pseudo-polynomial time algorithms for MPGs, it is quite promising to be competitive in practice. The presentation of such connection is mediated by the HyTN model. In order to analyze the time complexity of the algorithm, we introduce a refined notion of dynamic consistency, named ǫ-dynamic consistency, and present a sharp lower bounding analysis on the critical value of the reaction timeε where a CHyTN transits from being, to not being, dynamicallyconsistent. The proof ...

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“…≺s X represents that A? preceded X in the scenario s in the sense of the π-DC semantics(Cairo, Comin, and Rizzi 2016;Hunsberger and Posenato 2018a).…”

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confidence: 99%

Propagating Piecewise-Linear Weights in Temporal Networks

Hunsberger

Posenato

2019

ICAPS

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This paper presents a novel technique using piecewise-linear functions (PLFs) as weights on edges in the graphs of two kinds of temporal networks to solve several previously open problems. Generalizing constraint-propagation rules to accommodate PLF weights requires implementing a small handful of functions. Most problems are solved by inserting one or more edges with an initial weight of δ (a variable), then using the modified rules to propagate the PLF weights. For one kind of network, a new set of propagation rules is introduced to avoid a non-termination issue that arises when propagating PLF weights. The paper also presents two new results for determining the tightest horizon that can be imposed while preserving a network’s dynamic consistency/controllability.

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Instantaneous Reaction-Time in Dynamic-Consistency Checking of Conditional Simple Temporal Networks

Cited by 3 publications

References 15 publications

Faster Dynamic-Consistency Checking for Conditional Simple Temporal Networks

Faster Dynamic-Consistency Checking for Conditional Simple Temporal Networks

Checking dynamic consistency of conditional hyper temporal networks via mean payoff games

Propagating Piecewise-Linear Weights in Temporal Networks

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