Complexity of qualitative timeline-based planning

The timeline-based approach to automated planning was originally developed in the context of space missions. In this approach, problem domains are expressed as systems consisting of independent but interacting components whose behaviors over time, the timelines, are governed by a set of temporal constraints, called synchronization rules. Although timeline-based system descriptions have been successfully used in practice for decades, the research on the theoretical aspects only started recently. In the last few years, some interesting results have been shown concerning both its expressive power and the computational complexity of the related planning problem. In particular, the general problem has been proved to be EXPSPACE-complete. Given the applicability of the approach in many practical scenarios, it is thus natural to ask whether computationally simpler but still expressive fragments can be identified. In this paper, we study the timeline-based planning problem with the restriction that only qualitative synchronization rules, i.e., rules without explicit time bounds in the constraints, are allowed. We show that the problem becomes PSPACE-complete

Timelines Are Expressive Enough to Capture Action-Based Temporal Planning

Planning problems are usually expressed by specifying which actions can be performed to obtain a given goal. In temporal planning problems, actions come with a time duration and can overlap in time, which noticeably increase the complexity of the reasoning process. Action-based temporal planning has been thoroughly studied from the complexity-theoretic point of view, and has been proved to be EXPSPACE-complete in its general formulation. Conversely, timeline-based planning problems are represented as a collection of variables whose time-varying behavior is governed by a set of temporal constraints, called synchronization rules. Timelines provide a unified framework to reason about planning and execution under uncertainty. Timeline-based systems are being successfully employed in real-world complex tasks, but, in contrast to action-based planning, little is known on their computational complexity and expressiveness. In particular, a comparison of the expressiveness of the action- and timeline-based formalisms is still missing. This paper contributes a first step in this direction by proving the EXPSPACE-completeness of timeline-based planning with no temporal horizon and bounded temporal relations only. The result is shown via a reduction from action-based temporal planning, thus proving that timelines are expressive enough to capture it

Qualitative past Timeline-Based Games

This extended abstract discusses timeline-based planning, a modeling approach that offers a unique way to model complex systems. Recently, the timeline-based planning framework has been extended to handle general nondeterminism in a game-theoretic setting, resulting in timeline-based games. In this context, the problem of establishing whether a timeline-based game admits a winning strategy and synthesizing such a strategy have been addressed. We propose exploring simpler yet expressive fragments of timeline-based games by leveraging results about the role of past operators in synthesis from temporal logic specifications. The qualitative fragment of timeline-based planning is a good starting point for this exploration. We suggest introducing syntactic restrictions on synchronization rules so that they only constrain the behavior of the system before the current time point, which is expected to lower the complexity of synthesizing timeline-based games to EXPTIME. 2012 ACM Subject Classification Computing methodologies → Planning for deterministic action

Complexity of Timeline-Based Planning over Dense Temporal Domains: Exploring the Middle Ground

In this paper, we address complexity issues for timeline-based planning over dense temporal domains. The planning problem is modeled by means of a set of independent, but interacting, components, each one represented by a number of state variables, whose behavior over time (timelines) is governed by a set of temporal constraints (synchronization rules). While the temporal domain is usually assumed to be discrete, here we consider the dense case. Dense timeline-based planning has been recently shown to be undecidable in the general case; decidability (NP-completeness) can be recovered by restricting to purely existential synchronization rules (trigger-less rules). In this paper, we investigate the unexplored area of intermediate cases in between these two extremes. We first show that decidability and non-primitive recursive-hardness can be proved by admitting synchronization rules with a trigger, but forcing them to suitably check constraints only in the future with respect to the trigger (future simple rules). More "tractable" results can be obtained by additionally constraining the form of intervals in future simple rules: EXPSPACE-completeness is guaranteed by avoiding singular intervals, PSPACE-completeness by admitting only intervals of the forms [0,a] and [b,$\infty$[.Comment: In Proceedings GandALF 2018, arXiv:1809.0241

Undecidability of future timeline-based planning over dense temporal domains

The present work focuses on timeline-based planning over dense temporal domains. In automated planning, the temporal domain is commonly assumed to be discrete, the dense case being dealt with by resorting to some form of discretization. In the last years, the planning problem over dense temporal domains has been finally addressed both in the timeline-based setting and, very recently, in the action-based one. Dense timeline-based planning, in its full generality, has been shown to be undecidable. Decidability has been recovered by imposing suitable syntactic and/or semantic restrictions (the complexity of decidable fragments varies a lot, spanning from non-primitive recursive hardness to NP-completeness, passing through EXPSPACE- and PSPACE-completeness). In this paper, we proved that restricting to the future fragment is not enough to get decidability

Taming the complexity of timeline-based planning over dense temporal domains

The problem of timeline-based planning (TP) over dense temporal domains is known to be undecidable. In this paper, we introduce two semantic variants of TP, called strong minimal and weak minimal semantics, which allow to express meaningful properties. Both semantics are based on the minimality in the time distances of the existentially-quantified time events from the universally-quantified reference event, but the weak minimal variant distinguishes minimality in the past from minimality in the future. Surprisingly, we show that, despite the (apparently) small difference in the two semantics, for the strong minimal one, the TP problem is still undecidable, while for the weak minimal one, the TP problem is just PSPACE-complete. Membership in PSPACE is determined by exploiting a strictly more expressive extension (ECA+) of the well-known robust class of Event-Clock Automata (ECA) that allows to encode the weak minimal TP problem and to reduce it to non-emptiness of Timed Automata (TA). Finally, an extension of ECA+(ECA++) is considered, proving that its non-emptiness problem is undecidable. We believe that the two extensions of ECA (ECA+ and ECA++), introduced for technical reasons, are actually valuable per sé in the field of TA

A novel automata-theoretic approach to timeline-based planning

Timeline-based planning is a well-established approach successfully employed in a number of application domains. A very restricted fragment, featuring only bounded temporal relations and token durations, is expressive enough to capture action-based temporal planning. As for computational complexity, it has been shown to be EXPSPACE-complete when unbounded temporal relations, but only bounded token durations, are allowed. In this paper, we present a novel automata-theoretic characterisation of timeline-based planning where the existence of a plan is shown to be equivalent to the nonemptiness of the language recognised by a nondeterministic finite-state automaton that suitably encodes all the problem constraints (timelines and synchronisation rules). Besides allowing us to restate known complexity results in a fairly natural and compact way, such an alternative characterisation makes it possible to finally establish the exact complexity of the full version of the problem with unbounded temporal relations and token durations, which was still open and turns out to be EXPSPACE-complete. Moreover, the proposed technique is general enough to cope with (infinite) recurrent goals, which received little attention so far, despite being quite common in real-word application scenarios

Complexity of Timeline-Based Planning over Dense Temporal Domains: Exploring the Middle Ground

In this paper, we address complexity issues for timeline-based planning over dense temporal domains. The planning problem is modeled by means of a set of independent, but interacting, components, each one represented by a number of state variables, whose behavior over time (timelines) is governed by a set of temporal constraints (synchronization rules). While the temporal domain is usually assumed to be discrete, here we consider the dense case. Dense timeline-based planning has been recently shown to be undecidable in the general case; decidability (NP-completeness) can be recovered by restricting to purely existential synchronization rules (trigger-less rules). In this paper, we investigate the unexplored area of intermediate cases in between these two extremes. We first show that decidability and non-primitive recursive-hardness can be proved by admitting synchronization rules with a trigger, but forcing them to suitably check constraints only in the future with respect to the trigger (future simple rules). More "tractable" results can be obtained by additionally constraining the form of intervals in future simple rules: EXPSPACE-completeness is guaranteed by avoiding singular intervals, PSPACE-completeness by admitting only intervals of the forms [0,a] and [b,$\infty$[.Comment: In Proceedings GandALF 2018, arXiv:1809.0241

Beyond the Frontiers of Timeline-based Planning

Any agent, either biological or artificial, understands how to behave in its environment according to its prior knowledge and to its prior experience. The process of deciding which actions to undertake and how to perform them so as to achieve some desired objective is called deliberation. In particular, planning is an abstract and explicit deliberation process that chooses and organizes actions, by anticipating their expected outcomes, with the aim to achieve, as best as possible, some pre-stated objectives called goals. Among the most widespread approaches to automated planning, the classical approach broadly pursues to the following definition of planning: starting from a description of the initial state of the world, a description of the desired goals, and a description of a set of possible actions, the planning problem consists in synthesizing a plan, i.e., a sequence of actions, that is guaranteed, when applied to the initial state, to generate a state, called a goal state, which contains the desired goals. In order to cope with computational complexity, however, the classical approach to planning introduces some restrictive assumptions. Among them, for example, there is no explicit model of time and concurrency is treated only roughly. Additionally, goals are specified as a set of goal states, therefore, objectives such as states to be avoided and constraints on state trajectories or utility functions are not handled. In order to relax these restrictions, some alternative approaches have been proposed over the years. The timeline-based approach to planning, in particular, represents an effective alternative to classical planning for complex domains requiring the use of both temporal reasoning and scheduling features. This thesis focuses on timeline-based planning, aiming at solving some efficiency issues which inevitably raise as a consequence of the drop out of these restrictions. Regardless of the followed approach, indeed, it turns out that automated planning is a rather complex task from a computational point of view. Furthermore, not all of the approaches proposed in literature can rely on effective heuristics for efficiently tackling the search. This is particularly true in the case of the more recent and hence less investigated timeline-based formulation. Most of the timeline-based planners, in particular, have usually neglected the advantages triggered in classical planning from the use of Graphplan and/or modern heuristic search, namely the capability of reasoning on the whole domain model. This thesis aims at reducing the performance gap between the classical approach at planning and the timeline-based one. Specifically, the overall goal is to improve the efficiency of timeline-based reasoners taking inspiration from techniques applied in more classical approaches to planning. The main contributions of this thesis, therefore, are a) a new formalism for timeline-based planning which overcomes some limitations of the existing ones; b) a set of heuristics, inspired by the classical approach, that improve the performance of the timeline-based approach to planning; c) the introduction of sophisticated techniques like the non-chronological backtracking and the no-good learning, commonly used in other fields such as Constraint Processing, into the search process;d) the reorganization of the existing solver architectures, of a new solver called ORATIO, that allows to push the reasoning process beyond the sole automated planning, winking at emerging fields like, for example, Explainable AI and e) the introduction of a new language for expressing timeline-based planning problems called RIDDLE