Planning for autonomous systems typically requires reasoning with models at different levels of abstraction, and the harmonization of two competing sets of objectives: high-level mission goals that refer to an interaction of the system with the external environment, and low-level platform constraints that aim to preserve the integrity and the correct interaction of the subsystems. The complicated interplay between these two models makes it very hard to reason on the system as a whole, especially when the objective is to find plans with robustness guarantees, considering the non-deterministic behavior of the lower layers of the system. In this paper, we introduce the problem of Platform-Aware Mission Planning (PAMP), addressing it in the setting of temporal durative actions. The PAMP problem differs from standard temporal planning for its exists-forall nature: the high-level plan dealing with mission goals is required to satisfy safety and executability constraints, for all the possible non-deterministic executions of the low-level model of the platform and the environment. We propose two approaches for solving PAMP. The first baseline approach amalgamates the mission and platform levels, while the second is based on an abstraction-refinement loop that leverages the combination of a planner and a verification engine. We prove the soundness and completeness of the proposed approaches and validate them experimentally, demonstrating the importance of heterogeneous modeling and the superiority of the technique based on abstraction-refinement.

Linear Temporal Logic (LTL) is one of the most popular temporal logics and comes into play in a variety of branches of computer science. Among the various reasons of its widespread use there are its strong foundational properties: LTL is equivalent to counter-free ω-automata, to star-free ω-regular expressions, and (by Kamp's theorem) to the First-Order Theory of Linear Orders (FO-TLO). Safety and co-safety languages, where a finite prefix suffices to establish whether a word does not belong or belongs to the language, respectively, play a crucial role in lowering the complexity of problems like model checking and reactive synthesis for LTL. Safety-LTL (resp., coSafety-LTL) is a fragment of LTL where only the tomorrow, the weak tomorrow and the until temporal modalities (resp., the tomorrow, the weak tomorrow and the release temporal modalities) are allowed, that recognises safety (resp., co-safety) languages only. The main contribution of this paper is the introduction of a fragment of FO-TLO, called Safety-FO, and of its dual coSafety-FO, which are expressively complete with respect to the LTL-definable safety and co-safety languages. We prove that they exactly characterize Safety-LTL and coSafety-LTL, respectively, a result that joins Kamp's theorem, and provides a clearer view of the characterization of (fragments of) LTL in terms of first-order languages. In addition, it gives a direct, compact, and self-contained proof that any safety language definable in LTL is definable in Safety-LTL as well. As a by-product, we obtain some interesting results on the expressive power of the weak tomorrow operator of Safety-LTL, interpreted over finite and infinite words. Moreover, we prove that, when interpreted over finite words, Safety-LTL (resp.

Automated temporal planning is the technology of choice when controlling systems that can execute more actions in parallel and when temporal constraints, such as deadlines, are needed in the model. One limitation of several action-based planning systems is that actions are modeled as intervals having conditions and effects only at the extremes and as invariants, but no conditions nor effects can be specified at arbitrary points or sub-intervals. In this paper, we address this limitation by providing an effective heuristic-search technique for temporal planning, allowing the definition of actions with conditions and effects at any arbitrary time within the action duration. We experimentally demonstrate that our approach is far better than standard encodings in PDDL 2.1 and is competitive with other approaches that can (directly or indirectly) represent intermediate action conditions or effects.

Temporal planning is an active research area of Artificial Intelligence because of its many applications ranging from roboticsto logistics and beyond. Traditionally, authors focused on theautomatic synthesis of plans given a formal representation of thedomain and of the problem. However, the effectiveness of suchtechniques is limited by the complexity of the modeling phase: it ishard to produce a correct model for the planning problem at hand. In this paper, we present a technique to simplify the creation ofcorrect models by leveraging formal-verification tools for automaticvalidation. We start by using the ANML language, a very expressivelanguage for temporal planning problems that has been recentlypresented. We chose ANML because of its usability andreadability. Then, we present a sound-and-complete, formal encodingof the language into Linear Temporal Logic over predicates withinfinite-state variables. Thanks to this reduction, we enable theformal verification of several relevant properties over the planningproblem, providing useful feedback to the modeler.

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.

Timed Failure Propagation Graphs (TFPGs) are used in the design of safety-critical systems as a way of modeling failure propagation, and to evaluate and implement diagnostic systems. TFPGs are a very rich formalism: they allow to model Boolean combinations of faults and events, also dependent on the operational modes of the system and quantitative delays between them. TFPGs are often produced manually, from a given dynamic system of greater complexity, as abstract representations of the system behavior under specific faulty conditions. In this paper we tackle two key difficulties in this process: first, how to make sure that no important behavior of the system is overlooked in the TFPG, and that no spurious, non-existent behavior is introduced; second, how to devise the correct values for the delays between events. We propose a model checking approach to automatically validate the completeness and tightness of a TFPG for a given infinite-state dynamic system, and a procedure for the automated synthesis of the delay parameters. The proposed approach is evaluated on a number of synthetic and industrial benchmarks.

Timed Failure Propagation Graphs (TFPGs) are a formalism used in industry to describe failure propagation in a dynamic partially observable system. TFPGs are commonly used to perform model-based diagnosis. As in any model-based diagnosis approach, however, the quality of the diagnosis strongly depends on the quality of the model. Approaches to certify the quality of the TFPG are limited and mainly rely on testing. In this work we address this problem by leveraging efficient Satisfiability Modulo Theories (SMT) engines to perform exhaustive reasoning on TFPGs. We apply model-checking techniques to certify that a given TFPG satisfies (or not) a property of interest. Moreover, we discuss the problem of refinement and diagnosability testing and empirically show that our technique can be used to efficiently solve them.

In many practical domains, planning systems are required to reason about durative actions. A common assumption in the literature is that the executor is allowed to decide the duration of each action. However, this assumption may be too restrictive for applications. In this paper, we tackle the problem of temporal planning with uncontrollable action durations. We show how to generate robust plans,that guarantee goal achievement despite the uncontrollability of the actual duration of the actions. We extend the state-space temporalplanning framework, integrating recent techniques for solving temporalproblems under uncertainty. We discuss different ways of lifting the total order plans generated by the heuristic search to partial orderplans, showing (in)completeness results for each of them. We implemented our approach on top of COLIN, a state-of-the-art planner. An experimental evaluation over several benchmark problems shows the practical feasibility of the proposed approach.

A Simple Temporal Network with Uncertainty (STNU) is a structure for representing and reasoning about temporal constraints in domains where some temporal durations are not controlled by the executor. The most important property of an STNU is whether it is dynamically controllable (DC) whether there exists a strategy for executing the controllable time-points that guarantees that all constraints will be satisfied no matter how the uncontrollable durations turn out. This paper provides a novel mapping from STNUs to Timed Game Automata (TGAs) that: (1) explicates the deep theoretical relationships between STNUs and TGAs; and (2) enables the memoryless strategies generated from the TGA to be transformed into equivalent STNU execution strategies that reduce the real-time computational burden for the executor. The paper formally proves that the STNU-to-TGA encoding properly captures the execution semantics of STNUs.

Timelines are a formalism to model planning domains where the  temporal aspects are predominant, and have been used in many  real-world applications. Despite their practical success, a major limitation is the inability  to model temporal uncertainty, i.e. the plan executor cannot decide  the duration of some activities. In this paper we make two key contributions. First, we propose a comprehensive, semantically well founded framework that  (conservatively) extends with temporal uncertainty the state of the  art timeline approach. Second, we focus on the problem of producing time-triggered plans  that are robust with respect to temporal uncertainty, under a  bounded horizon. In this setting, we present the first complete  algorithm, and we show how it can be made practical by leveraging  the power of Satisfiability Modulo Theories.