This paper reports on the development of a planning system for an important industrial planning problem. This planner, which is one of the modules in an integrated design-and-planning system called EDAPS, provides an integrated approach to process planning in both the electronic and mechanical domains, specifically in the manufaeture of microwave transmit-receive O/R) modules. Our planner is ba...

SHOP (Simple Hierarchical Ordered Planner) is a domain-independent H T N planning system with the following characteristics. • SHOP plans for tasks in the same order that they will later be executed. This avoids some goalinteraction issues that arise in other HTN planners, so that the planning algorithm is relatively simple. • Since SHOP knows the complete world-state at each step of the planni...

Hierarchical Task Networks (HTNs) are a common model for encoding knowledge about planning domains in the form of task decompositions. We present a novel algorithm that uses invariant analysis to construct an HTN from the PDDL description of a planning domain and a single representative instance. The algorithm defines two types of composite tasks that interact to achieve the goal of a planning ...

This paper is focused on how a general-purpose hierarchical planning representation, based on the HTN paradigm, can be used to support the representation of oncology treatment protocols. The planning algorithm used is a temporally extended HTN planning process capable of interpreting such representation and generating oncology treatment plans that have been proven to support clinical decisions ...

We present here an overview of several planning techniques in robotics. We will not be concerned with the synthesis of abstract mission and task plans, using well known classical and other domain-independent planning techniques. We will mainly focus on to how refine such abstract plans into robust sensory-motor actions and on some planning techniques that can be useful for that. The paper intro...

This paper provides techniques for hierarchical task network (HTN) planning with durative actions. HTNs can provide useful heuristic guidance to planners, express goals that cannot be expressed in simple first principles planners, and allow plan generation to be limited by external constraints. The decomposition information in HTN plans can also help guide plan execution. This paper provides a ...

This paper provides a decentralized solution to multirobot coordination in partially-known environments where the problems of task assignment, trajectory planning and safe control are concurrently solved in the presence of communication constraints. We assume that the robots are homogeneous (any robot is capable of completing any assigned task), can localize themselves and have access to a know...

An important area in AI Planning is the expressiveness of planning domain specification languages such as PDDL, and their aptitude for modelling real applications. This paper presents OCLplus, an extension of a hierarchical object centred planning domain definition language, intended to support the representation of domains with continuous change. The main extension in OCLplus provides the capa...

The field of deterministic AI planning can roughly be divided into two approaches — classical statebased planning and hierarchical task network (HTN) planning. The plan existence problem of the former is known to be decidable while it has been proved undecidable for the latter. When extending HTN planning by allowing the unrestricted insertion of tasks and ordering constraints, one obtains a fo...

Hybrid Planning combines Hierarchical Task Network (HTN) planning with concepts known from Partial-Order Causal-Link (POCL) planning. We introduce novel heuristics for Hybrid Planning that estimate the number of necessary modifications to turn a partial plan into a solution. These estimates are based on the task decomposition graph that contains all decompositions of the abstract tasks in the p...