A standard problem in the field of Earth observation is the scheduling of the observations of an agile satellite constellation. Given a set of end‑user requests over Points of Interest (POIs), the problem consists in selecting observations among the candidate ones, attributing each of them to a satellite, and defining the sequence of observations planned for each satellite under operational constraints. These constraints stem from the visibility windows of the POIs and from the time‑dependent maneuvers required to reorient the satellites between successive POI observations (duration of the maneuvers function depending on their start times). This paper presents how Constraint Programming (CP) can be applied to solve this combinatorial observation dispatching and scheduling problem, the objective being to maximize a sum of collected individual observation rewards. Our main focus is the search for efficient strategies to approximate time-dependent no-overlap constraints given CP solvers that only manage sequence-dependent no-overlap constraints. In particular, we introduce constant-step and variable-step time-discretization methods, together with several approximation parameters. To get actually feasible solutions, the CP model is coupled with a greedy repair strategy that takes time-dependency into account, and a Large Neighborhood Search (LNS) that post-optimizes the solutions. This CP-Repair-LNS pipeline delivers high‑quality solutions compared to a baseline LNS.

This paper addresses the early stage design of a search-and-rescue mission by focusing on the configuration of the agent fleet, i.e., the selection and customization of each autonomous unit. To guide this configuration, two key evaluation criteria are introduced: (i) the performance - effectiveness of the fleet for executing the mission - and (ii) the operability - human workload required to supervise the fleet. We formally define the joint optimization problem that simultaneously selects a fleet layout and assesses these criteria, and we present a unified Constraint Programming model to solve it. The model incorporates three interchangeable cognitive workload sub-models of increasing granularity, allowing designers to test different assumptions about operator workload while balancing the two criteria. Experiments on realistic instances inspired by the RoboCup Rescue Simulation League show that the approach efficiently identifies several fleet configurations, offering a human-centered approach for early mission design.

International audience

In this article, we investigate multi-agent techniques to install autonomy and adaptation in IoT-based smart environment settings, like smart home scenarios. We particularly make use of the smart environment configuration problem (SECP) framework, and map it to a distributed optimization problem (DCOP). This consists in enabling smart objects to coordinate and self-configure as to meet both user-defined requirements and energy efficiency, by operating a distributed constraint reasoning process over a computation graph. As to cope with the dynamics of the environment and infrastructure (e.g., by adding or removing devices), we also specify the k -resilient distribution of graph-structured computations supporting agent decisions, over dynamic and physical multi-agent systems. We implement a self-organizing distributed repair method, based on a distributed constraint optimization algorithm to adapt the distribution as to ensure the system still performs collective decisions and remains resilient to upcoming changes. We provide a full stack of mechanisms to install resilience in operating stateless DCOP solution methods, which results in a robust approach using a fast DCOP algorithm to repair any stateless DCOP solution methods at runtime. We experimentally evaluate the performances of these techniques when operating stateless DCOP algorithms to solve SECP instances.

Network function virtualization (NFV) will simplify deployment and management of network and telecommunication services. NFV provides flexibility by virtualizing the network functions and moving them to a virtualization platform. In order to achieve its full potential, NFV is being extended to mobile or wireless networks by considering virtualization of radio functions. A typical network service setup requires the allocation of a virtual network function-forwarding graph (VNF-FG). A VNF-FG is allocated considering the resource constraints of the lower infrastructure. This topic has been well-studied in existing literature, however, the effects of variations of networks over time have not been addressed yet. In this paper, we provide a model of the adaptive and dynamic VNF allocation problem considering also VNF migration. Then we formulate the optimization problem as an integer linear programming (ILP) and provide a heuristic algorithm for allocating multiple VNF-FGs. The idea is that VNF-FGs can be reallocated dynamically to obtain the optimal solution over time. First, a centralized optimization approach is proposed to cope with the ILP-resource allocation problem. Next, a decentralized optimization approach is proposed to deal with cooperative multi-operator scenarios. We adopt AD3, an alternating direction method of multipliers-based algorithm, to solve this problem in a distributed way. The results confirm that the proposed algorithms are able to optimize the network utilization, while limiting the number of reallocations of VNFs which could interrupt network services.

As more interactive and multimedia-rich applications are migrating to the cloud, end-user satisfaction and her Quality of Experience (QoE) will become a determinant factor to secure success for any Software as a Service (SaaS) provider. Yet, in order to survive in this competitive market, SaaS providers also need to maximize their Quality of Business (QoBiz) and minimize costs paid to cloud providers. However, most of the existing works in the literature adopt a provider-centric approach where the end-user preferences are overlooked. In this article, we propose the AQUAMan mechanism that gives the provider a fine-grained QoE-driven control over the service acceptability rate while taking into account both end-users' satisfaction and provider's QoBiz. The proposed solution is implemented using a multi-agent simulation environment. The results show that the SaaS provider is capable of attaining the predefined acceptability rate while respecting the imposed average cost per user. Furthermore, the results help the SaaS provider identify the limits of the adaptation mechanism and estimate the best average cost to be invested per user.

The IEEE International Conference on Self-Adapting and Self-Organizing Systems (SASO) is the main forum for studying and discussing the foundations of a principled approach to engineering systems, networks, and services based on self-adaptation and self-organization. Over the past decade, it has consolidated as the primary scientific conference for sharing ideas on algorithms, technologies, tools, and applications across a wide range of scientific fields. In 2016, the conference was hosted by the University of Augsburg, in Augsburg, Germany; its scientific program comprised full papers, short papers, poster and demo presentations, workshops, doctoral symposium and tutorials. This special issue of ACM TAAS champions some of the most solid research results of SASO 2016, presenting selected, revised, and extended best articles.

In the real world as in the virtual one, trust is a fundamental concept. Without it, humans can neither act nor interact. So unsurprisingly, this concept received in the last years a growing interest from researchers in security and distributed artificial intelligence that gave rise to numerous mod els. The principal aim of these models was to assist users in making safe decisions at the individual level. However, studies have shown that the behavior of an individual within collective structures (e.g., a group, a community, a coalition or an organization) is affected (directly or indirectly) by the behavior of other members, creating a social influence dynamics within these structures. In this article, we study the impact of social influence phenomena when they are applied to trust management within open distributed communities of self-organized and self-governed agents.

This paper proposes a new approach for the the incremental evaluation of RDF graph streams over sliding windows. Our system, called "SPECTRA", combines a novel formof RDF graph summarisation, a new incremental evaluation method and adaptive indexing techniques. We materialise the summarised graph from each event using vertically partitioned views to facilitate the fast hash-joins for all types of queries. Our incremental and adaptive indexing is a byproduct of query processing, and thus provides considerable advantages over offline and online indexing. Furthermore, contrary to the existing approaches, we employ incremental evaluation of triples within a window. This results in considerable reduction in response time, while cutting the unnecessary cost imposed by recomputation models for each triple insertion and eviction within a defined window. We show that our resulting system is able to cope with complex queries and datasets with clear benefits. Our experimental results on both synthetic and real-world datasets show up to an order of magnitude of performance improvements as compared to state-of-the-art systems.

The success of virtual communities (VCs) relies on collaboration and resource sharing principles, making trust a priority for each member. The work presented in this paper addresses the problem of trust management in open and decentralised virtual co

Proposed in this paper is a multi-agent model that defines a set of global functioning rules for a flexible governance, adapted to parking management within a city. This is designed to aid drivers in finding a parking place, which satisfies a group of criteria, predefined in profiles, providing a better parking service to the public. The Multi-Agent model developed is integrated in the platform SensCity, which is dedicated to the development and deployment of Machine-to-Machine (M2M) systems. The city is divided into a number of parking areas that are equipped with sensors, which are responsible for transferring data from and to the parking places. Therefore, the agents can work to interpret and manipulate the governance principles modeled and implemented by the multi-agent model, independently from drivers and parking spaces. Moreover, this paper proposes an intelligent end-to-end management of parking system using the MOISE organization framework.