Paris Dauphine University

About: Paris Dauphine University is a education organization based out in Paris, France. It is known for research contribution in the topics: Context (language use) & Population. The organization has 1766 authors who have published 6909 publications receiving 162747 citations. The organization is also known as: Paris Dauphine & Dauphine.

Scale of Viability and Minimal Time of Crisis

Abstract: In this paper, we introduce and study the minimal time of a crisis map which measures the minimal time spent outside a given closed domain of constraints by trajectory solutions of a differential inclusion The interest of such a notion is basically to tackle simultaneously viability and target issues The main mathematical result characterizes the epigraph of the crisis map in terms of a viability kernel of an augmented problem This allows the obtaining of the numerical schemes we specify and to derive an equivalent Hamilton–Jacobi formulation A simple economic example illustrates the results

Approaches to a real-world train timetabling problem in a railway node

Abstract: We consider the Train Timetabling Problem (TTP) in a railway node (i.e. a set of stations in an urban area interconnected by tracks), which calls for determining the best schedule for a given set of trains during a given time horizon, while satisfying several track operational constraints. In particular, we consider the context of a highly congested railway node in which dierent Train Operators wish to run trains according to timetables that they propose, called ideal timetables. The ideal timetables altogether may be (and usually are) conicting, i.e. they do not respect one or more of the track operational constraints. The goal is to determine conict-free timetables that dier as little as possible from the ideal ones. The problem was studied for a research project funded by Rete Ferroviaria Italiana (RFI), the main Italian railway Infrastructure Manager, who also provided us with real-world instances. We present an Integer Linear Programming (ILP) model for the problem, which adapts previous ILP models from the literature to deal with the case of a railway node. The Linear Programming (LP) relaxation of the model is used to derive a dual bound. In addition, we propose an iterative heuristic algorithm that is able to obtain good solutions to real-world instances with up to 1500 trains in short computing times. The proposed algorithm is also used to evaluate the capacity saturation of the railway nodes.

A Recursive Model for Distributed Planning

Abstract: Distributed planning is fundamental to the generation of cooperative activities in Multi-Agent Systems. It requires both an adequate plan representation and efficient interacting methods allowing agents to coordinate their plans. This paper proposes a recursive model for the representation and the handling of plans by means of Recursive Petri Nets (RPN) which support the specification of concurrent activities, reasoning about simultaneous actions and continuous processes, a theory of verification and mechanisms of transformation (e.g. abstraction, refinement, merging) . The main features of the RPN formalism are domain independence, broad coverage of interacting situations and operational coordination. This paper also provides an approach to the interleaving of execution and planning which is based on the RPN semantics and gives some significant methods allowing plan management in distributed planning. It goes on to show how this approach can be used to coordinate agents' plans in a shared and dynamic environment.

Preference handling in combinatorial domains: From AI to social choice

Abstract: In both individual and collective decision making, the space of alternatives from which the agent (or the group of agents) has to choose often has a combinatorial (or multi-attribute) structure. We give an introduction to preference handling in combinatorial domains in the context of collective decision making, and show that the considerable body of work on preference representation and elicitation that AI researchers have been working on for several years is particularly relevant. After giving an overview of languages for compact representation of preferences, we discuss problems in voting in combinatorial domains, and then focus on multiagent resource allocation and fair division. These issues belong to a larger field, known as computational social choice, that brings together ideas from AI and social choice theory, to investigate mechanisms for collective decision making from a computational point of view. We conclude by briefly describing some of the other research topics studied in computational social choice.

Corporate Sustainable Innovation and Employee Behavior

Abstract: Corporate sustainable innovation is a major driver of institutional change, and its success can be largely attributed to employees. While some scholars have described the importance of intrinsic motivations and flexibility to facilitate innovation, others have argued that constraints and extrinsic motivations stimulate innovation. In the context of sustainable innovation, we explore which employee work practices are more conducive to firm-level innovation in corporate sustainability. Our results, based on a sample of 4640 French employees from 1764 firms, confirm the positive impact of intrinsic motivations (through employee social interactions), and the negative impact of job strain (through high imposed work pace), on corporate sustainable innovation. We also find that extrinsic rewards, through pay satisfaction, counteract the negative effect of job strain to promote sustainable innovation. This indicates that intrinsic and extrinsic rewards can work in tandem to facilitate sustainable innovation.