Planning is commonly viewed as a task to devise a course of action or a plan that conforms as much as possible to a set of goals before acting. The plan will then be used to guide the activities. Most classic planning systems assume a static environment for the planning agents. In a static environment, states remain unchanged between actions, and the outcomes of actions are assumed to be determ...

This paper proposes a novel technique for autonomous mission plan recovery in order to increase operability of unmanned underwater vehicles. It combines the benefits of knowledge-based ontology representation, autonomous partial ordering plan repair and robust mission execution. The approach is based on a combination of unrefinement and refinement stages on the plan-space domain in order to ada...

This paper presents a friendly visual tool for HAP, a ruleconfigurable planning system, which automatically adapts to each problem, in order to achieve best performance. HAP analyzes the problem and uses a rule system in order to configure the planning parameters in a way that best suites the morphology of the problem. The visual tool enables the user to use the planning system, get advice from...

We describe scripting facilities to create medical animations for intervention planning based on medical volume data and derived segmentation information. A data independent scripting language has been developed to separate animation scripts from imaging data. The scripting facilities are adaptive and allow to reuse one script to create animations for many different patients. With expressive an...

We propose a novel motion planning algorithm based on adaptive random walks. The proposed algorithm turns out to be easy to implement and the solution it produces can be easily and efficiently optimized. Furthermore the algorithm can incorporate adaptive components, so that the developer is not required to specify all the parameters of the random distributions involved, and the algorithm itself...

We present a new connectionist planning method [TML90]. By interaction with an unknown environment, a world model is progressively constructed using gradient descent. For deriving optimal actions with respect to future reinforcement, planning is applied in two steps: an experience network proposes a plan which is subsequently optimized by gradient descent with a chain of world models, so that a...

Ubiquitous Computing environments require dynamic adaptable application and network services because of their need to adapt to rapidly change context e.g. new users and devices being introduced, changes in connectivity, changes in user needs and requirements, etc. To support such environments, management systems need to dynamically adapt service execution and behaviour. One of the approaches be...

Adaptive A* is an incremental version of A* that updates the h-values of the previous A* search to make them more informed and thus future A* searches more focused. In this paper, we show how the A* searches performed by Adaptive A* can reuse part of the path of the previous search and terminate before they expand a goal state, resulting in Path-Adaptive A*. We demonstrate experimentally that P...

Distributed real-time embedded (DRE) systems often operate in open environments where operating conditions, input workload, and resource availability cannot be accurately characterized a priori. Some DRE systems, such as NASA’s Magnetospheric Multi-Scale (MMS) mission, perform sequences of heterogeneous data collection, manipulation, and coordination tasks to meet specified objectives/goals. Th...

The present paper is a contribution to the validation of the newly developed methodological framework, called "Adaptive Control Methodology" (ACM) introduced in a previous paper. ACM is a tool for planning and on-line control of complex socioeconomic processes. It combines the techniques of adaptive control theory with System Dynamics and Group Multicriteria Decision Aid. In the following the u...