The favorable gains gleaned from the provision of pre-task planning time (PTP) have struck a chord with SLA researchers as they try to manipulate task features to promote language production and development. In a similar vein, the present study is a two-fold attempt to first compare the effect of the length of pre-task planning time on discourse-analytic measures in narrative written production...

In a robotic soccer team, goalkeeper is an important challenging role, which has different characteristics from the other teammates. This paper proposes a new learning-based behavior model for a soccer goalkeeper robot by using Petri nets. The model focuses on modeling and analyzing, both qualitatively and quantitatively, for the goalkeeper role so that we have a model-based knowledge of the ta...

Supporting intelligent and trustworthy maritime path planning decisions, Int. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Plea...

Large scale operational areas often require multiple service robots for coverage and task parallelism. In such scenarios, each robot keeps its individual map of the environment and serves specific areas of the map at different times. We propose a knowledge sharing mechanism for multiple robots in which one robot can inform other robots about the changes in map, like path blockage, or new static...

This paper describes WORD2HTN, an algorithm for learning hierarchical tasks and goals from plan traces in planning domains. WORD2HTN combines semantic text analysis techniques and subgoal learning in order to generate Hierarchical Task Networks (HTNs). Unlike existing HTN learning algorithms, WORD2HTN learns distributed vector representations that represent the similarities and semantics of the...

To apply hierarchical task network (HTN) planning to real-world planning problems, one needs to encode the HTN schemata and action models beforehand. However, acquiring such domain knowledge is difficult and time-consuming because the HTN domain definition involves a significant knowledge-engineering effort. A system that can learn the HTN planning domain knowledge automatically would save time...

We describe in this paper a new method for extracting knowledge on Hierarchical Task-Network (HTN) planning problems for speeding up the search. This knowledge is gathered by propagating properties through an AND/OR tree that represents disjunctively all possible decompositions of an HTN planning problem. We show how to use this knowledge during the search process of our GraphHTN planner, to sp...

Autonomous mobile robots are promising tools for operations in environments that are difficult to access for humans. When these environments are dynamic and non-deterministic, like in collapsed buildings, the robots must coordinate their actions and the use of resources using planning. This paper presents Planner9, a hierarchical task network (htn) planner that runs on groups of miniature mobil...