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...

while learners’ ability to speak academic language develops over time in content-based programs, the development of their nonacademic language appears to lag behind. students’ speaking in content-based (cb) classrooms is confined to an academic discourse style. in the researchers’ point of view, the gap of less amount of nonacademic speaking can be bridged by the application of the conceptual f...

We discuss a general framework for coordinating self-interested agents in the pre-planning phase that can be used to decompose multi-agent task based planning problems into independent subproblems. The decomposition allows the agents to solve their part of the problem without the need to interact with other agents and such that the resulting plans can be easily combined into a joint plan. We il...

Purpose: The purpose of this study was to compare the effects of two different approaches of dual-task training and executive training on lower limb muscular activity in older adults with balance impairment. Methods: This was a quasi-experimental study performed with the pre-test and post-test method. Thirty older adults (age= 73.8±4.6, height= 1.65±0.06, weight= 69.17±12.67) were randomly ass...

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...

AI Planning & Scheduling techniques are being widely used to adapt learning paths to the special features and needs of students both in distance learning and lifelong learning environments. However, instructors strongly rely on Planning & Scheduling experts to encode and review the domains for the planner/scheduler to work. This paper presents an approach to automatically extract a fully operat...