OBJECTIVES To evaluate the preliminary results obtained using diffusion-weighted magnetic resonance imaging and the apparent diffusion coefficient for planning computed tomography-guided biopsies of selected mediastinal lesions. METHODS Eight patients with mediastinal lesions suspicious for malignancy were referred for computed tomography-guided biopsy. Diffusion-weighted magnetic resonance i...

The increasing interest in combined positron emission tomography (PET) and computed tomography (CT) to guide lung cancer radiation therapy planning has been well documented. Motion management strategies during treatment simulation PET/CT imaging and treatment delivery have been proposed to improve the precision and accuracy of radiotherapy. In light of these research advances, why has translati...

The basal ganglia (BG) are thought to play a critical role in motor planning and movement sequencing. While electrophysiological and imaging studies have shown that the dorso-lateral prefrontal cortex (DLPFC) is involved in working memory (WM), the involvement of the BG in this process is not well understood. We used a motor sequencing task to investigate the differential role of BG nuclei in m...

Non-determinism arises naturally in many real-world applications of action planning. Strong plans for this type of problems can be found using AO* search guided by an appropriate heuristic function. Most domain-independent heuristics considered in this context so far are based on the idea of ignoring delete lists and do not properly take the non-determinism into account. Therefore, we investiga...

Planning and scheduling for agents operating in heterogeneous, multi-agent environments is governed by the nature of the environment and the interactions between agents. Significant efficiency and capability gains can be attained by employing planning and scheduling mechanisms that are tailored to particular agent roles. This paper presents such a framework for a global sensor web that operates...

Counterexample-guided abstraction refinement (CEGAR) is a method for incrementally computing abstractions of transition systems. We propose a CEGAR algorithm for computing abstraction heuristics for optimal classical planning. Starting from a coarse abstraction of the planning task, we iteratively compute an optimal abstract solution, check if and why it fails for the concrete planning task and...

Counterexample-guided abstraction refinement (CEGAR) is amethodological framework for incrementally computing abstractions of transition systems. We propose a CEGAR algorithm for computing abstraction heuristics for optimal classical planning. Starting from a coarse abstraction of the planning task, we iteratively compute an optimal abstract solution, check if and why it fails for the concrete ...

This paper presents an adaptive planning system, called HAPRC, which automatically fine-tunes its planning parameters according to the morphology of the problem in hand, through a combination of Planning, Machine Learning and Knowledge-Based techniques. The adaptation is guided by a rule-based system that sets planner configuration parameters based on measurable characteristics of the problem i...

This paper describes the work on the Collaborative Planning Model in Project 12. An hypothesis is presented to attempt to unify the different planning doctrines under a common model. This has guided the development of a generic planning OWL ontology containing aspects of constraint-based reasoning and teamwork. The ontology also models “reasoning steps” and the dependencies between expertise, i...

In previous works [14], models and processes enabling to make interacting a project planning process with the system design one have been presented. Based on the idea that planning and design processes can be guided by consulting past experiences, an integrated case-based approach for coupled planning project and system design processes is proposed in this article. The proposal is firstly based...