Real-time retrieval for case-based reasoning in interactive multiagent-based simulations

The aim of this paper is to present the principles and results about case-based reasoning adapted to real-time interactive simulations, more precisely concerning retrieval mechanisms. The article begins by introducing the constraints involved in interactive multiagent-based simulations. The second section presents a framework stemming from case-based reasoning by autonomous agents. Each agent uses a case base of local situations and, from this base, it can choose an action in order to interact with other autonomous agents or users’ avatars. We illustrate this framework with an example dedicated to the study of dynamic situations in football. We then go on to address the difficulties of conducting such simulations in real-time and propose a model for case and for case base. Using generic agents and adequate case base structure associated with a dedicated recall algorithm, we improve retrieval performance under time pressure compared to classic CBR techniques. We present some results relating to the performance of this solution. The article concludes by outlining future development of our project.     

Group decision making problems in a linguistic and dynamic context

The aim of this paper is to present a new model of decision support system for group decision making problems based on a linguistic approach and dynamic sets of alternatives. The model incorporates a mechanism that allows to manage dynamic decision situations in which some information about the problem is not constant in time. We assume that the set of alternatives can change during the decision making process. The model is presented in a mobile and dynamic context where the experts’ preferences can be incomplete. The linguistic approach is used to represent both the experts’ preferences about the alternatives and the agreement degrees to manage the change of some alternatives. A prototype of such mobile decision support system in which the experts use mobile devices to provide their linguistic preferences at anytime and anywhere has been implemented. In such a way, we provide a new linguistic group decision making framework that is mobile and dynamic.     

A proposal for student modeling based on ontologies and diagnosis rules

The advances in the educational field and the high complexity of student modeling have provoked it to be one of the aspects more investigated in Intelligent Tutoring Systems (ITSs). The Student Models (SMs) should not only represent the student’s knowledge, but rather they should reflect, as faithfully as possible, the student’s reasoning process. To facilitate this goal, in this article a new approach to student modeling is proposed that benefits from the advantages of Ontological Engineering, advancing in the pursue of a more granular and complete knowledge representation. It’s focused, mainly, on the SM cognitive diagnosis process, and we present a method providing a rich diagnosis about the student’s knowledge state – especially, about the state of learning objectives reached or not. The main goal is to achieve SMs with a good adaptability to the student’s features and a high flexibility for its integration in varied ITSs.     

iLogDemons: A Demons-Based Registration Algorithm for Tracking Incompressible Elastic Biological Tissues

Tracking soft tissues in medical images using non-linear image registration algorithms requires methods that are fast and provide spatial transformations consistent with the biological characteristics of the tissues. LogDemons algorithm is a fast non-linear registration method that computes diffeomorphic transformations parameterised by stationary velocity fields. Although computationally efficient, its use for tissue tracking has been limited because of its ad-hoc Gaussian regularisation, which hampers the implementation of more biologically motivated regularisations. In this work, we improve the logDemons by integrating elasticity and incompressibility for soft-tissue tracking. To that end, a mathematical justification of demons Gaussian regularisation is proposed. Building on this result, we replace the Gaussian smoothing by an efficient elastic-like regulariser based on isotropic differential quadratic forms of vector fields. The registration energy functional is finally minimised under the divergence-free constraint to get incompressible deformations. As the elastic regulariser and the constraint are linear, the method remains computationally tractable and easy to implement. Tests on synthetic incompressible deformations showed that our approach outperforms the original logDemons in terms of elastic incompressible deformation recovery without reducing the image matching accuracy. As an application, we applied the proposed algorithm to estimate 3D myocardium strain on clinical cine MRI of two adult patients. Results showed that incompressibility constraint improves the cardiac motion recovery when compared to the ground truth provided by 3D tagged MRI.     

Generic Self-calibration of Central Cameras from Two Rotational Flows

We address the self-calibration of a smooth generic central camera from only two dense rotational flows produced by rotations of the camera about two unknown linearly independent axes passing through the camera centre. We give a closed-form theoretical solution to this problem, and we prove that it can be solved exactly up to a linear orthogonal transformation ambiguity. Using the theoretical results, we propose an algorithm for the self-calibration of a generic central camera from two rotational flows.     

Accelerating network applications by distributed interfaces on heterogeneous multiprocessor architectures

Hosts with several, possibly heterogeneous and/or multicore, processors provide new challenges and opportunities to accelerate applications with high communications bandwidth requirements. Many opportunities to scale these network applications with the increase in the link bandwidths are related to the exploitation of the available parallelism provided by the presence of several processing cores in the servers, not only for computing the workload of the user application but also for decreasing the overhead associated to the network interface and the system software.     

A parameterized shared-memory scheme for parameterized metaheuristics

This paper presents a parameterized shared-memory scheme for parameterized metaheuristics. The use of a parameterized metaheuristic facilitates experimentation with different metaheuristics and hybridation/combinations to adapt them to the particular problem we are working with. Due to the large number of experiments necessary for the metaheuristic selection and tuning, parallelism should be used to reduce the execution time. To obtain parallel versions of the metaheuristics and to adapt them to the characteristics of the parallel system, a unified parameterized shared-memory scheme is developed. Given a particular computational system and fixed parameters for the sequential metaheuristic, the appropriate selection of parameters in the unified parallel scheme eases the development of parallel efficient metaheuristics.     

Eliciting Preferences on Multiattribute Societies with a Choquet Integral

This paper reexamines the construction of indicators of standards of living, by focussing on the challenges raised by the subjectivity and the multidimensionality of living conditions. For that purpose, we apply Choquet integral-based multiattribute value theory to the elicitation, from rankings of multiattribute hypothetical societies, of individual preferences on different dimensions of living conditions. A simple application of the proposed approach highlights that preferences on multiattribute societies cannot, in general, be represented by an additive value model, as there exist complementarities and redundancies between different dimensions of standards of living. Our elicitation exercise reveals also a strong heterogeneity of individual preferences on hypothetical societies. Finally, we explore how elicited preferences can be used to cast a new light on the ranking of actual societies.     

Autonomous motion planning of a hand-arm robotic system based on captured human-like hand postures

The paper deals with the problem of motion planning of anthropomorphic mechanical hands avoiding collisions and trying to mimic real human hand postures. The approach uses the concept of “principal motion directions” to reduce the dimension of the search space in order to obtain results with a compromise between motion optimality and planning complexity (time). Basically, the work includes the following phases: capturing the human hand workspace using a sensorized glove and mapping it to the mechanical hand workspace, reducing the space dimension by looking for the most relevant principal motion directions, and planning the hand movements using a probabilistic roadmap planner. The approach has been implemented for a four finger anthropomorphic mechanical hand (17 joints with 13 independent degrees of freedom) assembled on an industrial robot (6 independent degrees of freedom), and experimental examples are included to illustrate its validity.