The dynamic and partial reconfiguration of FPGAs enables the dynamic placement in reconfigurable zones of the tasks that describe an application. However, the dynamic management of the tasks impacts the communications since tasks are not present in the FPGA during all computation time. So, the task manager should ensure the allocation of each new task and their interconnection which is performe...

Hierarchical Task-Network (HTN) based planning techniques have been applied to the problem of composing Web Services, especially when described using the OWL-S service ontologies. Many of the existing Web Services are either exclusively information providing or crucially depend on information-providing services. Thus, many interesting service compositions involve collecting information either d...

This paper describes the results of applying a modified version of hierarchical task-network (HTN) planning to the problem of declarer play in contract bridge. We represent information about bridge in a task network that is extended to represent multi-agency and uncertainty. Our game-playing procedure uses this task network to generate game trees in which the set of alternative choices is deter...

To date, no one planner has demonstrated clearly superior performance. Although researchers have hypothesized that this should be the case, no one has performed a large study to test its limits. In this research, we tested performance of a set of planners to determine which is best on what types of problems. The study included six planners and over 200 problems. We found that performance, as me...

This paper presents a model of motivation in learning agents to achieve adaptive, multi-task learning in complex, dynamic environments. Previously, computational models of motivation have been considered as speed-up or attention focus mechanisms for planning and reinforcement learning systems, however these different models do not provide a unified approach to the development or evaluation of c...

In classical planning it is easy to verify if a given sequence of actions is a solution to a planning problem. It has to be checked whether the actions are applicable in the given order and if a goal state is reached after executing them. In this paper we show that verifying whether a plan is a solution to an HTN planning problem is much harder. More specifically, we prove that this problem is ...

Hierarchical Task Network (HTN) planning is a formalism that can express constraints which cannot easily be expressed by classical (non-hierarchical) planning approaches. It enables reasoning about procedural structures and domainspecific search control knowledge. Yet the cornucopia of modern heuristic search techniques remains largely unincorporated in current HTN planners, in part because it ...

We propose the use of hierarchical (HTN) planning techniques to encode strategies that one or more Bots should execute while acting in highly dynamic environments such as Unreal Tournament© games. Our approach allows the formulation of a grand strategy but retains the ability of Bots to react to the events in the environment while contributing to the grand strategy.

Planning in dynamic continuous environments requires reasoning about nonlinear continuous effects, which previous Hierarchical Task Network (HTN) planners do not support. In this paper, we extend an existing HTN planner with a new state projection algorithm. To our knowledge, this is the first HTN planner that can reason about nonlinear continuous effects. We use a wait action to instruct this ...

In this paper we present a learning method that is able to automatically acquire control knowledge for a hybrid HTN-POP planner called Hybis. Hybis decomposes a problem in subproblems using either a default method or a user-defined decomposition method. Then, at each level of abstraction, generates a plan at that level using a POCL (Partial Order Causal Link) planning technique. Our learning ap...