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

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

Specific-to-General Learning for Temporal Events with Application to Learning Event Definitions from Video

We develop, analyze, and evaluate a novel, supervised, specific-to-general learner for a simple temporal logic and use the resulting algorithm to learn visual event definitions from video sequences. First, we introduce a simple, propositional, temporal, event-description language called AMA that is sufficiently expressive to represent many events yet sufficiently restrictive to support learning. We then give algorithms, along with lower and upper complexity bounds, for the subsumption and generalization problems for AMA formulas. We present a positive-examples--only specific-to-general learning method based on these algorithms. We also present a polynomial-time--computable ``syntactic'' subsumption test that implies semantic subsumption without being equivalent to it. A generalization algorithm based on syntactic subsumption can be used in place of semantic generalization to improve the asymptotic complexity of the resulting learning algorithm. Finally, we apply this algorithm to the task of learning relational event definitions from video and show that it yields definitions that are competitive with hand-coded ones

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

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

SHOP2: An HTN Planning System

The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning domains

A Game-Theoretic Approach to Timeline-Based Planning with Uncertainty

In timeline-based planning, domains are described as sets of independent, but interacting, components, whose behaviour over time (the set of timelines) is governed by a set of temporal constraints. A distinguishing feature of timeline-based planning systems is the ability to integrate planning with execution by synthesising control strategies for flexible plans. However, flexible plans can only represent temporal uncertainty, while more complex forms of nondeterminism are needed to deal with a wider range of realistic problems. In this paper, we propose a novel game-theoretic approach to timeline-based planning problems, generalising the state of the art while uniformly handling temporal uncertainty and nondeterminism. We define a general concept of timeline-based game and we show that the notion of winning strategy for these games is strictly more general than that of control strategy for dynamically controllable flexible plans. Moreover, we show that the problem of establishing the existence of such winning strategies is decidable using a doubly exponential amount of space

Efficiently Reasoning with Interval Constraints in Forward Search Planning

In this paper we present techniques for reasoning natively with quantitative/qualitative interval constraints in statebased PDDL planners. While these are considered important in modeling and solving problems in timeline based planners; reasoning with these in PDDL planners has seen relatively little attention, yet is a crucial step towards making PDDL planners applicable in real-world scenarios, such as space missions. Our main contribution is to extend the planner OPTIC to reason natively with Allen interval constraints. We show that our approach outperforms both MTP, the only PDDL planner capable of handling similar constraints and a compilation to PDDL 2.1, by an order of magnitude. We go on to present initial results indicating that our approach is competitive with a timeline based planner on a Mars rover domain, showing the potential of PDDL planners in this setting