A Structural Characterization of Temporal Dynamic Controllability

Morris, Paul

doi:10.1007/11889205_28

Cited by 60 publications

(97 citation statements)

References 4 publications

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“…In this paper, we denote this algorithm as MM5. In turn, (Morris, 2006) presented an O(n 4 )-time and (Morris, 2014) an O(n 3 )-time DC-checking algorithm for STNUs, i.e., there are two interesting optimizations of the MM5 algorithm not further discussed in this paper. (Tsamardinos et al, 2003) introduced the Conditional Temporal Problem (CTP) that augments an STN with observation timepoints.…”

Section: Background and Related Workmentioning

confidence: 99%

Simple Temporal Networks with Partially Shrinkable Uncertainty

Lanz

Posenato

Combi

et al. 2015

Proceedings of the International Conference on Agents and Artificial Intelligence

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Abstract:The Simple Temporal Network with Uncertainty (STNU) model focuses on the representation and evaluation of temporal constraints on time-point variables (timepoints), of which some (i.e., contingent timepoints) cannot be assigned (i.e., executed by the system), but only be observed. Moreover, a temporal constraint is expressed as an admissible range of delays between two timepoints. Regarding the STNU model, it is interesting to determine whether it is possible to execute all the timepoints under the control of the system, while still satisfying all given constraints, no matter when the contingent timepoints happen within the given time ranges (controllability check). Existing approaches assume that the original contingent time range cannot be modified during execution. In real world, however, the allowed time range may change within certain boundaries, but cannot be completely shrunk. To represent such possibility more properly, we propose Simple Temporal Network with Partially Shrinkable Uncertainty (STNPSU) as an extension of STNU. In particular, STNPSUs allow representing a contingent range in a way that can be shrunk during run time as long as shrinking does not go beyond a given threshold. We further show that STNPSUs allow representing STNUs as a special case, while maintaining the same efficiency for both controllability checks and execution.

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Section: Background and Related Workmentioning

confidence: 99%

Simple Temporal Networks with Partially Shrinkable Uncertainty

Lanz

Posenato

Combi

et al. 2015

Proceedings of the International Conference on Agents and Artificial Intelligence

View full text Add to dashboard Cite

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“…Recall that each path in an STN graph corresponds to a constraint that must be satisfied by any solution for the associated STN. In STNU graphs, it is the semi-reducible paths-defined below-that correspond to the (possibly conditional) constraints that must be satisfied by any dynamic execution strategy for the associated STNU (Morris, 2006). Whereas an STN is consistent if and only if its graph has no negative-length loops, an STNU is dynamically controllable if and only if its graph has no semi-reducible negative-length loops.…”

Section: Dynamicmentioning

confidence: 99%

“…A path in an STNU graph is called semi-reducible if it can be transformed into a path that has only ordinary or upper-case edges (Morris, 2006). The soundness of the edge-generation rules ensures that the constraints represented by semi-reducible paths must be satisfied by any dynamic execution strategy.…”

Section: Dynamicmentioning

confidence: 99%

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A Faster Algorithm for Checking the Dynamic Controllability of Simple Temporal Networks with Uncertainty

Hunsberger

2014

Proceedings of the 6th International Conference on Agents and Artificial Intelligence

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Abstract:A Simple Temporal Network (STN) is a structure containing time-points and temporal constraints that an agent can use to manage its activities. A Simple Temporal Network with Uncertainty (STNU) augments an STN to include contingent links that can be used to represent actions with uncertain durations. The most important property of an STNU is whether it is dynamically controllable (DC)-that is, whether there exists a strategy for executing its time-points such that all constraints will necessarily be satisfied no matter how the contingent durations happen to turn out (within their known bounds). The fastest algorithm for checking the dynamic controllability of STNUs reported in the literature so far is the O(N 4 )-time algorithm due to Morris. This paper presents a new DC-checking algorithm that empirical results confirm is faster than Morris' algorithm, in many cases showing an order of magnitude speed-up. The algorithm employs two novel techniques. First, new constraints generated by propagation are immediately incorporated into the network using a technique called rotating Dijkstra. Second, a heuristic that exploits the nesting structure of certain paths in the STNU graph is used to determine a good order in which to process the contingent links during constraint propagation.

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“…The Morris algorithm (Morris, 2006) builds on MM. Its theory and especially analysis contains several complicated new concepts taking it further from the simple intuition of MMV.…”

Section: Introductionmentioning

confidence: 99%

Classical Dynamic Controllability Revisited - A Tighter Bound on the Classical Algorithm

Nilsson

Kvarnström

Doherty

2014

Proceedings of the 6th International Conference on Agents and Artificial Intelligence

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Abstract:Simple Temporal Networks with Uncertainty (STNUs) allow the representation of temporal problems where some durations are uncontrollable (determined by nature), as is often the case for actions in planning. It is essential to verify that such networks are dynamically controllable (DC) -executable regardless of the outcomes of uncontrollable durations -and to convert them to an executable form. We use insights from incremental DC verification algorithms to re-analyze the original verification algorithm. This algorithm, thought to be pseudo-polynomial and subsumed by an O(n 5 ) algorithm and later an O(n 4 ) algorithm, is in fact O(n 4 ) given a small modification. This makes the algorithm attractive once again, given its basis in a less complex and more intuitive theory. Finally, we discuss a change reducing the amount of work performed by the algorithm. BACKGROUNDTime and concurrency are increasingly considered essential in planning and multi-agent environments, but temporal representations vary widely in expressivity. For example, Simple Temporal Problems (STPs, (Dechter et al., 1991)) allow us to efficiently determine whether a set of timepoints (events) can be assigned real-valued times in a way consistent with a set of constraints bounding temporal distances between timepoints. The start and end of an action can be represented as timepoints, but its possible durations can only be represented as an STP constraint if the execution mechanism can choose durations arbitrarily within the given bounds. Usually, exact durations are instead chosen by nature and agents must generate plans that work regardless of the eventual outcomes. STPs with Uncertainty, STPUs (Vidal and Ghallab, 1996), capture this aspect by introducing contingent timepoints that correspond to the end of an action, associated with contingent temporal constraints corresponding to possible durations to be decided by nature. One must then find a way to assign times to ordinary controlled timepoints (determine when to start actions) so that for every possible outcome for the contingent constraints (action durations), there exists some solution for the ordinary requirement constraints (corresponding to STP constraints).If an STPU allows us to schedule controlled timepoints (actions to be started) incrementally given that we receive information when a contingent timepoint occurs (an action ends), it is dynamically controllable (DC) and can be efficiently executed by a dispatcher (Muscettola et al., 1998). Conversely, guaranteeing that constraints are satisfied when executing a non-DC plan is impossible, as it would require information about future duration outcomes.Three algorithms for verifying the dynamic controllability of a complete STPU have been published: 1. MMV (Morris et al., 2001), here also called the classical algorithm. It is a simple algorithm that derives and tightens constraints using specific rules. It is easily implemented, captures the intuition behind STNUs and has a direct correctness proof. Its run-time is pseudo-polynomial...

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A Structural Characterization of Temporal Dynamic Controllability

Cited by 60 publications

References 4 publications

Simple Temporal Networks with Partially Shrinkable Uncertainty

Simple Temporal Networks with Partially Shrinkable Uncertainty

A Faster Algorithm for Checking the Dynamic Controllability of Simple Temporal Networks with Uncertainty

Classical Dynamic Controllability Revisited - A Tighter Bound on the Classical Algorithm

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