Computer Sciences

Finding a nash equilibrium and an optimal sharing policy for multi-agent network expansion game

M.Huguet , N.Chaabane , A.Agnetis , C.Briand


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In this work, a multi-agent network flow problem is addressed , aiming at characterizing the properties of stable flows and allowing their computation. Two types of agents are considered: transportation-agents, that carry a flow of products on a given network and another agent, either a producer or a customer, who is willing to ship products. Every transportation-agent controls a set of arcs, each having a capacity that can be increased up to a certain point at a given cost. The other agent is interested in maximizing the flow transshipped through the network. To this aim, we assume it offers the transportation-agents a reward that is proportional to the realized flow value. This particular multi-agent framework is referred to as a Multi-Agent network expansion game. We characterize and find particular stable strategies that are of interest for this game. We particularly focus on the problem of finding a Nash Equilibrium and a sharing policy that maximize the value of the total flow. We prove that this problem is NP-hard in the strong sense and show how such a strategy can be characterized considering paths in specific auxiliary graphs. We also provide a mixed integer linear programming formulation to solve the problem. Computational experiments are provided to prove the effectiveness of our approach and derive some insights for practitioners.

Agents endowed with uncertainty management behaviors to solve a multiskill healthcare task scheduling

A.Quilliot , H.Zgaya , S.Hammadi , S.Othman , A.Martinot , J.Renard


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Health organizations are complex to manage due to their dynamic processes and distributed hospital organization. It is therefore necessary for healthcare institutions to focus on this issue to deal with patients' requirements. We aim in this paper to develop and implement a management decision support system that can help physicians to better manage their organization and anticipate the feature of overcrowding. Our objective is to optimize the Pediatric Emergency Department functioning characterized by stochastic arrivals of patients leading to its services overload. Human resources allocation presents additional complexity related to their different levels of skills and uncertain availability dates. So, we propose a new approach for multi-healthcare task scheduling based on a dynamic multi-agent system. Decisions about assignment and scheduling are the result of a cooperation and negotiation between agents with different behaviors. We therefore define the actors involved in the agents' coalition to manage uncertainties related to the scheduling problem and we detail their behaviors. Agents have the same goal, which is to enhance care quality and minimize long waiting times while respecting degrees of emergency. Different visits to the PED services and regular meetings with the medical staff allowed us to model the PED architecture and identify the characteristics and different roles of the healthcare providers and the diverse aspects of the PED activities. Our approach is integrated in a DSS for the management of the Regional University Hospital Center of Lille. Our survey is included in the French National Research Agency project HOST ).

From grid cells and visual place cells to multimodal place cell: a new robotic architecture

P.Gaussier , N.Cuperlier , A.Jauffret


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Citation: Jauffret A, Cuperlier N and Gaussier P From grid cells and visual place cells to multimodal place cell: a new robotic architecture. Front. Neurorobot. 9:1. In the present study, a new architecture for the generation of grid cells was implemented on a real robot. In order to test this model a simple place cell model merging visual PC activity and GC was developed. GC were first built from a simple " several to one " projection performed on a neural field coding for path integration. Robotics experiments raised several practical and theoretical issues. To limit the important angular drift of PI, head direction information was introduced in addition to the robot proprioceptive signal coming from the wheel rotation. Next, a simple associative learning between visual place cells and the neural field coding for the PI has been used to recalibrate the PI and to limit its drift. Finally, the parameters controlling the shape of the PC built from the GC have been studied. Increasing the number of GC obviously improves the shape of the resulting place field. Yet, other parameters such as the discretization factor of PI or the lateral interactions between GC can have an important impact on the place field quality and avoid the need of a very large number of GC. In conclusion, our results show our GC model based on the compression of PI is congruent with neurobiological studies made on rodent. GC firing patterns can be the result of a modulo transformation of PI information. We argue that such a transformation may be a general property of the connectivity from the cortex to the entorhinal cortex. Our model predicts that the effect of similar transformations on other kinds of sensory information in the entorhinal cortex should be observed. Consequently, a given EC cell should react to non-contiguous input configurations in non-spatial conditions according to the projection from its different inputs.
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