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...

Interaction with users is a key capability of planning systems that are applied in real-world settings. Such a system has to be able to react appropriately to requests issued by its users. Most of these systems are based on a generated plan that is continually criticised by him, resulting in a mixed-initiative planning system. We present several practically relevant requests to change a plan in...

Classical planning is the problem of synthesizing a sequence of actions to reach the goal state starting from the initial state. Hierarchical task networks (HTNs) have been used as a guidance for solving planning problems where tasks are decomposed into subtasks. Recently, both action-based planning as well as HTNbased planning have been east as SAT. The performance of planning as SAT has been ...

This paper describes a collaboration between two PhD students. The first author is developing a course generator (CG) for a Web-based e-learning environments (WBLE) [3]. A CG generates sequences of learning objects according to the learner’s goals and individual properties. The assembling process is knowledge intensive: assembling courses that implement modern pedagogical theories requires a fr...

Considerable work has focused on enhancing the semantics of Hierarchical Task Networks (HTNs) in order to advance the state-of-the-art in hierarchical planning. For instance, the Hierarchical Goal Network (HGN) formalism operates over a hierarchy of goals to facilitate tighter integration of decompositional planning with classical planning. Another example is the Action Notation Markup Language...

This paper presents an application of Hierarchical Transition Network (HTN) planning to a squad-based military simulation. The hierarchical planner produces collaborative plans for the whole squad in real time, generating the type of highly coordinated behaviours typical for armed combat situations involving trained professionals. Here, we detail the extensions to HTN planning necessary to prov...

In this paper we present the GraphHTN algorithm, a hybrid planning algorithm that does Hierarchical Task-Network (HTN) planning using a combination of HTN-style problem reduction and Graphplan-style planning-graph generation. We also present experimental results comparing GraphHTN with ordinary HTN decomposition (as implemented in the UMCP planner) and ordinary Graphplan search (as implemented ...

We present an extension to the HTN planning paradigm, which provides a more knowledge centred assessment of activities and ordering constraints. Based upon a knowledge-rich model, DART-Network planning applies mixed mode reasoning to determine the need for activities and ordering constraints within a plan. We describe a simple construction problem to demonstrate the limitations of existing HTN ...

Plan generation is important in a number of agent applications, but such applications generally require elaborate domain models that include not only the definitions of the actions that an agent can perform in a given domain, but also information about the most effective ways to generate plans for the agent in that domain. Such models typically take a large amount of human effort to create. To ...