HTN planning: Overview, comparison, and beyond

Hierarchies are one of the most common structures used to understand and conceptualise the world. Within the field of Artificial Intelligence (AI) planning, which deals with the automation of world-relevant problems, Hierarchical Task Network (HTN) planning is the branch that represents and handles hierarchies. In particular, the requirement for rich domain knowledge to characterise the world enables HTN planning to be very useful, and also to perform well. However, the history of almost 40 years obfuscates the current understanding of HTN planning in terms of accomplishments, planning models, similarities and differences among hierarchical planners, and its current and objective image. On top of these issues, the ability of hierarchical planning to truly cope with the requirements of real-world applications has been often questioned. As a remedy, we propose a framework-based approach where we first provide a basis for defining different formal models of hierarchical planning, and define two models that comprise a large portion of HTN planners. Second, we provide a set of concepts that helps in interpreting HTN planners from the aspect of their search space. Then, we analyse and compare the planners based on a variety of properties organised in five segments, namely domain authoring, expressiveness, competence, computation and applicability. Furthermore, we select Web service composition as a real-world and current application, and classify and compare the approaches that employ HTN planning to solve the problem of service composition. Finally, we conclude with our findings and present directions for future work. In summary, we provide a novel and comprehensive viewpoint on a core AI planning technique.<br/

Learning preconditions for planning from plan traces and HTN structure

Agreat challenge in developing planning systems for practical applications is the difficulty of acquiring the domain information needed to guide such systems. This paper describes a way to learn some of that knowledge. More specifically, the following points are discussed. (1) We introduce a theoretical basis for formally defining algorithms that learn preconditions for Hierarchical Task Network (HTN) methods. (2) We describe Candidate Elimination Method Learner (CaMeL), a supervised, eager, and incremental learning process for preconditions of HTN methods. We state and prove theorems about CaMeL’s soundness, completeness, and convergence properties. (3) We present empirical results about CaMeL’s convergence under various conditions. Among other things, CaMeL converges the fastest on the preconditions of the HTN methods that are needed the most often. Thus CaMeL’s output can be useful even before it has fully converged

Learning preconditions for planning from plan traces and HTN structure

A great challenge in developing planning systems for practical applications is the difficulty of acquiring the domain information needed to guide such systems. This paper describes a way to learn some of that knowledge. More specifically: • We introduce a theoretical basis for formally defining algorithms that learn preconditions for HTN methods. • We describe CaMeL, a supervised, eager, and incremental learning process for preconditions of HTN methods. We state and prove theorems about CaMeL’s soundness, completeness, and convergence properties. • We present empirical results about CaMeL’s convergence under various conditions. Among other things, CaMeL converges the fastest on the preconditions of the HTN methods that are needed the most often. Thus CaMeL’s output can be useful even before it has fully converged

An operator induction tool supporting knowledge engineering in planning

Within the field 'artificial intelligence' are many disciplines, one of which is planning. Planning seeks to find a suitable sequence of actions to carry out a task specified as a set of initial states for the objects involved in the actions and a required goal state. To do this the system has to have enough knowledge about the 'world' in the form of a planning domain model The process of constructing a planning domain model requires knowledge engineering. The structuring of the knowledge is important and hand-coding a domain model is a tedious and error-prone process. Static knowledge in the domain requires little update but the same cannot be said for the dynamic knowledge.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Coordinating services embedded everywhere via hierarchical planning

The spaces we live in are provided with different devices and technologies, such as sensors for recognising our presence. The aim of such spaces is to improve our comfort, productivity, and even reduce our energy bills. The problem with fulfilling the aim is that devices alone cannot do much to achieve such difficult goals. People would also have problems in manually searching for the best situation accomplishing their needs. A way to deal with this problem is to coordinate devices automatically. For example, our home can autonomously figure out that some lamps can be turned off because the living room has enough natural light and the activity we are currently doing requires a low light level. The benefits are improved comfort and a reasonable amount of energy saved. We therefore explore the possibilities of using a system based on automated planning. This planning produces a set of device services, such as turn off a lamp, that achieves a given goal. We use a method, called hierarchical planning, which enables us to organise the knowledge we have about spaces and devices in hierarchical forms. We show that planning is suitable for this kind of problems by using hierarchical planning to save energy in the Bernoulliborg building at the University of Groningen. The results show energy and money savings, and that people are satisfied with our system. We also improve the system and show that even more money can be saved without sacrificing the well-being of people if we can buy energy from several energy providers