Using Timed Game Automata to Synthesize Execution Strategies for Simple Temporal Networks with Uncertainty

Cimatti, Alessandro; Hunsberger, Luke; Micheli, Andrea; Roveri, Marco

doi:10.1609/aaai.v28i1.9040

Cited by 13 publications

(6 citation statements)

References 19 publications

(24 reference statements)

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“…CTP [42] allows the construction of conditional plans with temporal constraints. Strategies based on Simple Temporal Networks with Uncertainty (STNU) such as [14] are used as temporal scheduling tools where conditions and decisions can be added to STNUs [17,45]. [18] present an encoding to Conditional Simple Temporal Networks with Uncertainty and Resources (CST-NURs) with promising results in a set of applications that model temporal and numeric constraints.…”

Section: Related Workmentioning

confidence: 99%

“…n.children defines the set of n.name(s) (actions) associated with a n.name. If the action to label is a sensing action (line 12), TraCE maps the edge from node n to child child with the relevant outcome of the sensing action, obtained from the history H of Π T and U (line [13][14]. This labelling is stored in the last node n definition parameter: n.edge-map.…”

Section: Trace Planningmentioning

confidence: 99%

See 1 more Smart Citation

Temporal Planning with Incomplete Knowledge and Perceptual Information

Carreno¹,

Pétillot²,

Petrick³

2022

Electron. Proc. Theor. Comput. Sci.

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In real-world applications, the ability to reason about incomplete knowledge, sensing, temporal notions, and numeric constraints is vital. While several AI planners are capable of dealing with some of these requirements, they are mostly limited to problems with specific types of constraints. This paper presents a new planning approach that combines contingent plan construction within a temporal planning framework, offering solutions that consider numeric constraints and incomplete knowledge. We propose a small extension to the Planning Domain Definition Language (PDDL) to model (i) incomplete, (ii) knowledge sensing actions that operate over unknown propositions, and (iii) possible outcomes from non-deterministic sensing effects. We also introduce a new set of planning domains to evaluate our solver, which has shown good performance on a variety of problems.

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Section: Related Workmentioning

confidence: 99%

Section: Trace Planningmentioning

confidence: 99%

Temporal Planning with Incomplete Knowledge and Perceptual Information

Carreno¹,

Pétillot²,

Petrick³

2022

Electron. Proc. Theor. Comput. Sci.

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“…These networks encode a possibly infinite number of RTEDs. Following the approach in (Cimatti et al 2014b), it is possible to produce a strategy that is executable but unstructured: given a state of the execution (set of time points scheduled and a condition on their timing) the strategy associates an action to execute. We propose a language to express readily executable, closed-form strategies that closely resembles the structure of a program.…”

Section: Strategy Representation and Validationmentioning

confidence: 99%

“…The only approach for solving DTNU-DC is in (Cimatti et al 2014a). It generalizes the STNU-DC en-coding to timed games of (Cimatti et al 2014b) to DTNU and conditional networks. The approach allows for the generation of strategies in form of stateless controllers, that can be executed without on-line reasoning or constraint propagation.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Controllability of Disjunctive Temporal Networks: Validation and Synthesis of Executable Strategies

Cimatti

Micheli

Roveri

2016

AAAI

Self Cite

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The Temporal Network with Uncertainty (TNU) modeling framework is used to represent temporal knowledge in presence of qualitative temporal uncertainty. Dynamic Controllability (DC) is the problem of deciding the existence of a strategy for scheduling the controllable time points of the network observing past happenings only. In this paper, we address the DC problem for a very general class of TNU, namely Disjunctive Temporal Network with Uncertainty. We make the following contributions. First, we define strategies in the form of an executable language; second, we propose the first decision procedure to check whether a given strategy is a solution for the DC problem; third we present an efficient algorithm for strategy synthesis based on techniques derived from Timed Games and Satisfiability Modulo Theory. The experimental evaluation shows that the approach is superior to the state-of-the-art.

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“…[13], [14]) have proposed a dispatchable execution approach, where a flexible temporal plan is used by a controller that schedules activities on-line while guaranteeing constraint satisfaction. Recently, the use of TGA has been considered to address dynamic controllability in temporal networks with uncertainty [15] also in the case of disjunctive problems [16]. Since CBTP systems often rely on temporal networks for their solving process, such work could be indirectly exploited to furnish a mapping from flexible plans to TGA, and consequently give a methodology for synthesizing execution strategies for flexible plans.…”

Section: Introductionmentioning

confidence: 99%

An Executable Semantics of Flexible Plans in Terms of Timed Game Automata

Mayer

Orlandini

2015

2015 22nd International Symposium on Temporal Representation and Reasoning (TIME)

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Robust plan execution in uncertain and dynamic environments is a critical issue for plan-based autonomous systems, especially when uncertain events coexist with temporal flexibility. In this regard, many Planning and Scheduling systems model temporal uncertainty by means of flexible timelines, each of which describes the behavior of one of the system components, and consists of a sequence of events whose begin and end times range within given intervals.This work enriches a previously proposed formal characterization of flexible timelines and plans, considering the difference between controllable and uncontrollable activities. Two main sources of uncertainty are considered: i) some components of the system may depend on an external environment and cannot be planned by the executive; ii) there may be tasks whose duration cannot be exactly foreseen in advance. Such notions are formally defined and the consequent controllability issues are addressed, focusing, in particular, on dynamic controllability.Partially controllable flexible plans are given a semantics in terms of networks of timed game automata (TGA), showing how they can be encoded into such networks. The translation allows for exploiting existing verification tools for TGA, such as UPPAAL-TIGA, in order to check the dynamic controllability property for flexible plans and, possibly, generate a dynamic execution strategy that can be used for robust plan execution. Some preliminary experiments aimed at evaluating the feasibility of the approach are also presented.

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Using Timed Game Automata to Synthesize Execution Strategies for Simple Temporal Networks with Uncertainty

Cited by 13 publications

References 19 publications

Temporal Planning with Incomplete Knowledge and Perceptual Information

Temporal Planning with Incomplete Knowledge and Perceptual Information

Dynamic Controllability of Disjunctive Temporal Networks: Validation and Synthesis of Executable Strategies

An Executable Semantics of Flexible Plans in Terms of Timed Game Automata

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