Efficiently handling temporal knowledge in an HTN planner (original) (raw)

Design Concepts for a new Temporal Planning Paradigm

2012

Throughout the history of space exploration, the complexity of missions has dramatically increased, from Sputnik in 1957 to MSL, a Mars rover mission launched in November 2011 with advanced autonomous capabilities. As a result, the mission plan that governs a spacecraft has also grown in complexity, pushing to the limit the capability of human operators to understand and manage it. However, the effective representation of large plans with multiple goals and constraints still represents a problem. In this paper, a novel approach to address this problem is presented. We propose a new planning paradigm named HTLN, intended to provide a compact and understandable representation of complex plans and goals based on Timeline planning and Hierarchical Temporal Networks. We also present the design of a planner based on HTLN, which enables new planning approaches that can improve the performance of present real-world domains.

Representation and Control in IxTeT, a Temporal Planner

This paper presents a temporal planner, called IxTeT. It focuses on the representation and control issues, arguing for a compromise between the expressiveness and the ei~ciency of the search. The representation relies on a point-based reified logic, associated to mldtivalued domain attributes. Hieraxchical planning operators ot~er an expressive description, with parallelism, durations, effects and conditions at various moments of the action. Time in the input scenario enables to take into account predicted forthcoming events and to plan in a dynamic world. A compilation procedure checks the consistency of the operators specified by the user. The control relies on the use of causal-links, A, algorithm, and an extended least-commitment strategy. It uses two important procedures, called C~feasibility" and "satisfiability =, dealing respectively with goal decomposition and conflict resolution: GHALLAB 61

On Guiding Search in HTN Temporal Planning with non Temporal Heuristics

arXiv (Cornell University), 2023

The Hierarchical Task Network (HTN) formalism is used to express a wide variety of planning problems as task decompositions, and many techniques have been proposed to solve them. However, few works have been done on temporal HTN. This is partly due to the lack of a formal and consensual definition of what a temporal hierarchical planning problem is as well as the difficulty to develop heuristics in this context. In response to these inconveniences, we propose in this paper a new general POCL (Partial Order Causal Link) approach to represent and solve a temporal HTN problem by using existing heuristics developed to solve non temporal problems. We show experimentally that this approach is performant and can outperform the existing ones.

Exploiting Domain Knowledge with a Concurrent Hierarchical Planner

2000

Based on recent research about coordinating concurrent hierarchical plans (CHiPs), we introduce a sound and complete hierarchical planner that can better reason about precomputed conditions (summary information) of abstract plans to potentially make better re nement decisions than previous approaches. A reasonable criticism of this technique is that the summary information can grow exponentially as it is propagated up a plan hierarchy. This paper analyzes the complexity of the problem to show that in spite of this exponential growth, nding solutions at higher levels is still exponentially cheaper than at lower levels. In addition, this paper o ers heuristics, including \fewest threats rst" (FTF) and \expand most threats rst" (EMTF), that take advantage of summary information to smartly direct the search for a global plan. Experiments show that for a particular domain these heuristics could greatly improve the search for the optimal global plan compared to two other heuristics (FAF and ExCon) that have both been successful in Hierarchical Task Network (HTN) planning.

Managing concurrency in temporal planning using planner-scheduler interaction

Artificial Intelligence, 2009

Metric temporal planning involves both selecting and organising actions to satisfy the goals and also assigning to each of these actions its start time and, where necessary, its duration. The assignment of start times to actions is a central concern of scheduling. In pddl2.1, the widely adopted planning domain description language standard, metric temporal planning problems are described using actions with durations. A large number of planners have been developed to handle this language, but the great majority of them are fundamentally limited in the class of temporal problems they can solve. In this paper, we review the source of this limitation and present an approach to metric temporal planning that is not so restricted. Our approach links planning and scheduling algorithms into a planner, Crikey, that can successfully tackle a wide range of temporal problems. We show how Crikey can be simplified to solve a wide and interesting subset of metric temporal problems, while remaining competitive with other temporal planners that are unable to handle required concurrency. We provide empirical data comparing the performance of this planner, Crikey SHE , our original version, Crikey, and a range of other modern temporal planners. Our contribution is to describe the first competitive planner capable of solving problems that require concurrent actions.

Planning with Inaccurate Temporal Rules

2012 IEEE 24th International Conference on Tools with Artificial Intelligence, 2012

We use a temporal pattern model called Temporal Interval Tree Associative Rules (Tita rules). This pattern model has been introduced in a previous work. The model can express uncertainty, temporal inaccuracy, the usual time point operators, synchronicity, incomplete orders, chaining, disjunctive time constraints and temporal negation. This pattern model is initially designed to be used for temporal learning. In this paper, we use Tita rules as world description models for a Planning and Scheduling task. We present an efficient temporal planning algorithm able to deal with uncertainty, temporal inaccuracy, discontinuous (or disjunctive) time constraints and predictable but imprecisely time located exogenous events. We evaluate our technique by joining a learning algorithm and our planning algorithm into a simple reactive cognitive architecture that we apply on with virtual robot.

Temporal planning with mutual exclusion reasoning

1999

Many planning domains require a richer notion of time in which actions can overlap and have di erent durations. The key to fast performance in classical planners (e.g., Graphplan, ipp, and Blackbox) has been the use of a disjunctive representation with powerful mutual exclusion reasoning. This paper presents tgp, a new algorithm for temporal planning. tgp operates by incrementally expanding a compact planning graph representation that handles actions of di ering duration. The key to tgp performance is tight m utual exclusion reasoning which is based on an expressive language for bounding mutexes and includes mutexes between actions and propositions. Our experiments demonstrate that mutual exclusion reasoning remains valuable in a rich temporal setting.

Planning Through The TRLi Temporal ReasoningSystem

WIT Transactions on Information and Communication Technologies, 1970

In this paper we propose a planning system based on the TRLi temporal reasoning system. The planning algorithm incorporates TRLi as a temporal deduction component with temporal constraint solving capabilities. The innovations of the planning system are the enhanced expressive power, the flexibility and the reusability of the generated plans, as well as the simplification of the action ordering mechanism as it orders temporal references instead of restructuring a whole plan network.

CRIKEY - A Temporal Planner Looking at the Integration of Scheduling and Planning

For many temporal planning domains, the planning and scheduling problems are not tightly coupled and so can be solved separately. However, in some cases, where the problems do interact, this approach will fail. A domain is presented where this is the case. CRIKEY, a planner that separates out the logical and temporal reasoning, is introduced. It detects where they interact and the paper explains both how it detects them and also how, in these cases, CRIKEY solves the problems simultaneously. It will also look at CRIKEY as an architecture that uses a series of relaxations to find a plan. Preliminary results show its potential.

Temporal Planning while the Clock Ticks

2018

One of the original motivations for domain-independent planning was to generate plans that would then be executed in the environment. However, most existing planners ignore the passage of time during planning. While this can work well when absolute time does not play a role, this approach can lead to plans failing when there are external timing constraints, such as deadlines. In this paper, we describe a new approach for time-sensitive temporal planning. Our planner is aware of the fact that plan execution will start only once planning finishes, and incorporates this information into its decision making, in order to focus the search on branches that are more likely to lead to plans that will be feasible when the planner finishes.