Non-Deterministic Planning with Temporally Extended Goals: LTL over Finite and Infinite Traces

Rivas, Alberto; Triantafillou, Eleni; Muise, Christian; Baier, Jorge A.; McIlraith, Sheila A.

doi:10.1609/aaai.v31i1.11058

Cited by 49 publications

(49 citation statements)

References 11 publications

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“…The formulation actually brings QNP planning into the realm of standard FOND planning by dealing with the underlying fairness assumptions explicitly. The use of fairness assumptions connects also to works on LTL planning and synthesis (Camacho et al, 2019;Aminof et al, 2019), and to works addressing temporally extended goals (De Giacomo & Vardi, 1999;Patrizi, Lipovetzky, & Geffner, 2013;Camacho, Triantafillou, Muise, Baier, & McIlraith, 2017;Camacho et al, 2019;Aminof et al, 2020). Our work can be seen as a special case of planning under LTL assumptions (Aminof et al, 2019) that targets an LTL fragment that is relevant for FOND planning and is computationally simpler.…”

Section: Related Workmentioning

confidence: 85%

Flexible FOND Planning with Explicit Fairness Assumptions

Rodriguez¹,

Bonet²,

Sardiña³

et al. 2022

jair

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We consider the problem of reaching a propositional goal condition in fully-observable nondeterministic (FOND) planning under a general class of fairness assumptions that are given explicitly. The fairness assumptions are of the form A/B and say that state trajectories that contain infinite occurrences of an action a from A in a state s and finite occurrence of actions from B, must also contain infinite occurrences of action a in s followed by each one of its possible outcomes. The infinite trajectories that violate this condition are deemed as unfair, and the solutions are policies for which all the fair trajectories reach a goal state. We show that strong and strong-cyclic FOND planning, as well as QNP planning, a planning model introduced recently for generalized planning, are all special cases of FOND planning with fairness assumptions of this form which can also be combined. FOND+ planning, as this form of planning is called, combines the syntax of FOND planning with some of the versatility of LTL for expressing fairness constraints. A sound and complete FOND+ planner is implemented by reducing FOND+ planning to answer set programs, and its performance is evaluated in comparison with FOND and QNP planners, and LTL synthesis tools. Two other FOND+ planners are introduced as well which are more scalable but are not complete.

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Section: Related Workmentioning

confidence: 85%

Flexible FOND Planning with Explicit Fairness Assumptions

Rodriguez¹,

Bonet²,

Sardiña³

et al. 2022

jair

View full text Add to dashboard Cite

show abstract

“…Since P4 has an intrinsic finite trace semantics, as given by the final( • ) in the goal formula, another possible improvement is to directly use LTL over finite traces (LTL f ) instead of standard LTL over infinite traces. LTL f synthesis and planning have several points in common, with the former that can be seen as a generalization of the latter (see, e.g., [10], [12], [16], [17], [18], [57]). Moreover, several work has been done on LTL f synthesis with assumptions, such as [1], [2], [11], which is related to preference planning.…”

Section: Discussion About Improvements and Extensionsmentioning

confidence: 99%

Probabilistic Preference Planning Problem for Markov Decision Processes

Turrini

Hahn

et al. 2022

IIEEE Trans. Software Eng.

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The classical planning problem aims to find a sequence of permitted actions leading a system to a designed state, i.e., to achieve the system's task. However, in many realistic cases we also have requirements on how to complete the task, indicating that some behaviors and situations are more preferred than others. In this paper, we present the probabilistic preference-based planning problem (P4) for Markov decision processes, where the preferences are defined based on an enriched probabilistic LTL-style logic. We first recall P4Solver, an SMT-based planner computing the preferred plan by reducing the problem to a quadratic programming one previously developed to solve P4. To improve computational efficiency and scalability, we then introduce a new encoding of the probabilistic preference-based planning problem as a multi-objective model checking one, and propose the corresponding planner P4Solver MO . We illustrate the efficacy of both planners on some selected case studies to show that the model checking-based algorithm is considerably more efficient than the quadratic-programming-based one.

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“…While a number of FOND planners exist that deal with different LTL fragments (Patrizi, Lipovetzky, and Geffner 2013;Camacho et al 2017;Camacho et al 2018), none seem to correctly handle the fragment needed here. We have used general LTL tools to cope with the unrestricted temporal assumptions.…”

Section: Discussionmentioning

confidence: 99%

High-level Programming via Generalized Planning and LTL Synthesis

Bonet

Giacomo

Geffner

et al. 2020

Proceedings of the Seventeenth International Conference on Principles of Knowledge Representation and Reasoning

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We look at program synthesis where the aim is to automatically synthesize a controller that operates on data structures and from which a concrete program can be easily derived. We do not aim at a fully-automatic process or tool that produces a program meeting a given specification of the program’s behaviour. Rather, we aim at the design of a clear and well-founded approach for supporting programmers at the design and implementation phases. Concretely, we first show that a program synthesis task can be modeled as a generalized planning problem. This is done at an abstraction level where the involved data structures are seen as black-boxes that can be interfaced with actions and observations, the first corresponding to the operations and the second to the queries provided by the data structure. The abstraction level is high enough to capture intuitive and common assumptions as well as general and simple strategies used by programmers, and yet it contains sufficient structure to support the automated generation of concrete solutions (in the form of controllers). From such controllers and the use of standard data structures, an actual program in a general language like C++ or Python can be easily obtained. Then, we discuss how the resulting generalized planning problem can be reduced to an LTL synthesis problem, thus making available any LTL synthesis engine for obtaining the controllers. We illustrate the effectiveness of the approach on a series of examples.

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Non-Deterministic Planning with Temporally Extended Goals: LTL over Finite and Infinite Traces

Cited by 49 publications

References 11 publications

Flexible FOND Planning with Explicit Fairness Assumptions

Flexible FOND Planning with Explicit Fairness Assumptions

Probabilistic Preference Planning Problem for Markov Decision Processes

High-level Programming via Generalized Planning and LTL Synthesis

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