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This paper aims to contribute to the challenging issue of one microscopic simulation round for dynamic traffic assignment. It relays on the selfish behavior of the vehicle agent that benefits from a more accurate estimation of its travel time. The main novelty is that, rather than considering the average travel times in the network links according to the present vehicles, the vehicle must first know at what time it is able to cross the nodes located at both extremities of the road. This is achieved through a negotiation between the vehicle agent and the node agents to book the crossing time. In order to assess this new paradigm, this paper compares it with well-known approaches in an elementary network. The result invites to extend the approach to more general cases. A discussion of the opportunities and limitations of the approach extension is provided in this paper. One of the notable opportunities, is that the proposed approach has a great potential to improve the energy efficiency by exploring mobile navigation applications and connected autonomous vehicles.

Intersections are at the core of congestion in urban areas. After the end of the Second World War, the problem of intersection management has benefited from a growing body of advances to address the optimization of the traffic lights’ phase splits, timing, and offset. These contributions have significantly improved traffic safety and efficiency in urban areas. However, with the growth of transportation demand and motorization, traffic lights show their limits. At the end of the 1990s, the perspective of autonomous and connected driving systems motivated researchers to introduce a paradigm shift for controlling intersections. This new paradigm is well known today as autonomous intersection management (AIM). It harnesses the self-organization ability of future vehicles to provide more accurate control approaches that use the smallest available time window to reach unprecedented traffic performances. This is achieved by optimizing two main points of the interaction of connected and autonomous vehicles at intersections: the motion control of vehicles and the schedule of their accesses. Considering the great potential of AIM and the complexity of the problem, the proposed approaches are very different, starting from various assumptions. With the increasing popularity of AIM, this paper provides readers with a comprehensive vision of noticeable advances toward enhancing traffic efficiency. It shows that it is possible to tailor vehicles’ speed and schedule according to the traffic demand by using distributed particle swarm optimization. Moreover, it brings the most relevant contributions in the light of traffic engineering, where flow–speed diagrams are used to measure the impact of the proposed optimizations. Finally, this paper presents the current challenging issues to be addressed.

Quantum computing offers a new approach to the problem modeling and solving. This paper deals with the quantum modeling of reactive agents. It also proposes a quantum algorithm to implement the subsumption architecture, widely used by reactive agents, particularly in robotics. This work shows the contribution of the formalism proposed by quantum mechanics to the modeling and the proof of certain properties of the agent behavior. After, the definition of the reactive agent state modeling, the paper suggests a behavior modeling approach based on two steps for subsumption architecture. The first one models the preset behavior that links each action to the perception states. The second one determines, among several actuated actions, the one that the robot must achieve. The subsumption architecture raises the challenge of modeling hierarchical priority of actions. To this end, a multipartite entanglement is used in the second step. More precisely, the paper proposes and generalizes a W-state circuit in order to be used for modeling hierarchical priority actions and controlling the robot accordingly. The result of both steps provides a formal model that links the robot’s perception (input) to the actions (output), with respect to the subsumption architecture. The proposed model of agent is simulated using IBM quantum computer. The simulation shows that the model can either be served as a control unit of the robot (CU) to obtain the suitable action or to simulate the robot behavior.

The Vehicle Routing Problem (VRP) is a class of well-known NP-hard combinatorial optimisation problems. The VRP is concerned with the design of the optimal routes, used by a fleet of identical vehicles stationed at a central depot to serve a set of customers with known demands. In the basic version of the problem, known as a Capacitated VRP (CVRP), only capacity restrictions for vehicles are considered and the objective is to minimize the total cost (or length) of routes. The Capacitated Vehicle Routing Problem with Time Windows (CVRPTW), which is a generalization of the CVRP, is one of the most studied variants of the VRP. In the CVRPTW, the vehicles must comply with constraint of time windows associated with each customer in addition to capacity constraints. The study of the VRP is very important. The VRP contributes directly to a real opportunity to reduce costs in the important area of logistics. Logistics can be roughly described as the delivery of goods from one place (supplier) to others (consumers). Transportation management, and more specifically vehicle routing, has a considerable economical impact on all logistic systems. Due to the nature of the problem, it is not viable to use exact methods for large instances of the VRP. Therefore, most approaches rely on heuristics that provide approximate solutions. Some specific methods have been developed to this problem. Another option is to apply standard optimization techniques, such as tabu search, simulated annealing, constraint programming, genetic algorithms and ant systems.Our main interest is about the metaheuristics used to solve the VRP and more particularly about the ant colony system. The first algorithm based on the ant colony system, applied to the CVRP, was proposed by [Bullnheimer & al. 1999] known as « Ant System » (AS), applied first for the TSP in [Dorigo & al. 1996]. The pheromone and the nearest neighbor heuristic are used to build the routes of the vehicles. To improve the routes, a heuristic of 2-OPT is combined with the AS. This algorithm was tested on 40 benchmark but the performances are inferior to those relative to the tabu search algorithm. Yet, in terms of time accomplishment, the AS is a serious rival for the existing metaheuristics.

Connected and autonomous vehicles (CAV) is the development trend in the field of transportation systems. Recent studies show that the resources sharing between pedestrians and CAV is a big challenge. Considering traffic safety and efficiency at that sharing point not only requires a collision avoidance system but also more communicative behaviors of the CAV. More precisely, pedestrian needs to understand the intention of the incoming CAV whether it will cross first or not according to its speed profile. This paper uses the optimal trajectory control to provide CAV with a communicative behavior. A scenario where CAV and pedestrian cooperate together to cross a conflict zone is studied. A communicative CAV behavior is designed through an objective function. Hamiltonian analysis is used to derive the optimal control for the CAV. Based on Oculus virtual reality platform, the proposed approach is tested and the behavior of cars and pedestrians are studied. The tests show that this approach provides CAVs with a kind of automatic courtesy.

Several approaches exist for the lateral control of autonomous vehicles. Among them are the geometric approaches. They are shown to be robust to disturbances and able to manage complex tracks. Their main advantage lies on the fact that they are explainable, in the sense that their behavior can be analyzed to provide guarantees about their limitations. However, they do not give the quality of results that can be obtained using other control principles, mostly because of design issues. This paper aims to tackle these issues by proposing a novel geometric approach based on Frenet Serret formulas to reach the level of quality proposed by the other approaches, while still benefiting from the advantages of geometric approaches. A numerical analysis of the proposed control approach show its advantage: Simulation results and tests on a real autonomous car are provided.

Connected and autonomous vehicles (CAV) is a key technology for this century. One of the main challenges is to define a smart interaction behavior of CAV with the other road users. The challenge is mainly raised at conflicting points where path of CAV intersects with the other users. Recent studies show interaction with humans is a big challenge. It not only requires a collision avoidance system but also more communicative behaviors of the CAV. More precisely, pedestrian needs to understand the intention of the incoming CAV whether it will cross first or not according to its speed profile. One way to overcome this issue is to design optimal trajectory control of the CAV that matches with the pedestrian expectation. However, such a design faces the non-linearity of the space sharing constraint. This paper uses sequence modeling based on Petri Net in order to overcome the problem. It also uses Hamiltonian analysis to derive the optimal control. Numerical examples of the communicative behavior of CAV at pedestrian crossing show that the proposed approach provides CAV with a kind of automatic courtesy.

International audience; In past years, 3D models of virtual worlds have been used in several applications such as urban planning, simulation and design. In the railway field, that we chose as a field of application to illustrate our proposals in this article, simulation makes sense because of the complexity involved and the risk for personnel during upstream phases of validation. To be able to simulation with enough precision, the model of the virtual universe is a key point. Especially For train component simulation, an accurate and detailed model of the train component is mandatory. For training the drivers within a serious game, a high visual quality is required. Obtaining a single model supporting these two constraints at the same time at the lowest cost is still an open issue and involved many actors. Our contribution is a methodology and a process for creating a virtual universe model, based on automatic model generation, in order to allow the creation of large-scale universes while guaranteeing a level of details appropriate to the need, a model of constant quality and including semantic data necessary for simulation, while reducing the modeling costs and the modeling duration. The proposed process is applied to train simulation.

Autonomous vehicles that are able to communicate together allowed the emergence of a new way of controlling intersections. Recently an active research community stakes on cooperative intersection management. Vehicles and intersection are able to communicate together in order to improve traffic condition at intersections. Many simulations have shown that the cooperative intersection management outperforms traffic lights because there are no limited stages as within traffic light and authorized movements are adapted to the current situation. This paper focusses on speed synchronization at intersections. More precisely, a virtual platoon is formed by vehicles coming from different lanes. When traffic rate is low this allows avoiding useless stops and thus the speed synchronization saves energy and increases the average speed. However, experiments showed that some parameters need to be considered. Hence, as the traffic rate grows, vehicles need to stop and speed synchronization is no more efficient. The main reason of this drawback is that the studied concepts are limited to First Come First Served or to First Out First-Served. This paper extends the virtual platooning, where new rules form new virtual platoon able to adjust dynamically the whole behavior according to the traffic flow growth under safety conditions. Simulations based on real-world experiences show that the proposed approach is very efficient by increasing the average speed while keeping a high capacity of the intersection.

International audience; Microgrid (MG) clusters are small power systems that can help improve the resilience and flexibility of larger systems. In this work, we focus on designing an agent-based decentralized coordination method that healthy MGs to help unhealthy ones (e.g., in the case of insufficient generation). Coordination is expected to eliminate the power imbalance inside MGs, thus limiting the impact for customers and maximizing local generation. Decentralized coordination is especially useful as the resulting system is not subject to single points-of-failure. To ensure the feasibility of the power dispatch resulting from agents interaction, a distributed power flow calculation is used and enables verifying line capacity constraints. The operation of the system is tested with a system including 12 MGs and 33 MGs.

International audience; Microgrids are increasingly popular small-scale power systems with local generation, storage and loads. Interconnecting several microgrids together could provide a flexible structure for power distribution and enable large renewable and distributed penetration levels, as well as coordination between microgrids when disturbances occur. This paper proposes a control strategy for such connected microgrids. Although a microgrid aims to operate independently in normal conditions, several microgrids can support each other in case of contingencies or insufficient generation. Such coordination aims at reducing load shedding and generation curtailment, and is achieved by coordinating the output power of microgrids. Each microgrid is equipped with an agent to achieve self-control and to negotiate with other microgrids, for example to request power to its neighbors to support its loads. The Newton-Raphson and consensus methods are used to calculate the output power of each microgrid. The control strategy is validated using simulations on an IEEE 13-node test feeder.

Among the innovations aimed at tackling the transportation issues in urban areas, one of the most promising solutions is the possibility of making virtual trains of vehicles so as to provide a new kind of transportation system. Even if this kind of solution is now widespread in literature, some difficulties still need to be resolved. For instance, one must find solutions to make the crossing of the train possible while maintaining train composition (trains must not be split) and safety conditions. This paper proposes a multi-level decision process aimed at dealing with this issue. This proposal is based on dynamic adaptation of train parameters which lead to trains crossing without stopping any of them. Results, obtained in simulations, make the comparison with a classical crossing strategy.

RESUME. Le probleme de la patrouille multiagent implique une equipe d’agents qui doivent visiter les lieux strategiques d’un environnement le plus frequemment possible. Ce probleme d’optimisation consiste generalement a determiner une strategie de patrouille multiagent minimisant la pire oisivete d’un graphe, c’est-a-dire la plus grande duree pendant laquelle un nœud n’a pas ete visite. Nous proposons dans cet article d’etudier de maniere theorique l’evaluation des strategies de patrouille cycliques. Une telle strategie est constituee de n couples de pre-cycles et de cycles, n etant le nombre d’agents patrouilleurs. Chaque cycle definit la liste des nœuds qu’un agent doit visiter indefiniment, le premier nœud etant le meme que le dernier nœud. Chaque pre-cycle definit la liste des nœuds visites par un agent pour atteindre son cycle de patrouille. Nous presentons dans cet article les conditions de convergence permettant a un algorithme efficace d’evaluer en un nombre fini d’etapes la pire oisivete de telles strategies. Un algorithme d’evaluation base sur ces resultats theoriques est egalement decrit.

The construction of ad-hoc design processes following the Situational Method Engineering (SME) paradigm is currently carried out by adopting a set of phases for which, until now, no well defined techniques and guidelines have been established. The consequence is that organizations are very dependent on method designers' skills. In this paper, we propose an approach based on SME for constructing customized agent oriented design processes. Our approach adopts the metamodel as the most important factor leading to the selection and assembly of method fragments and an algorithm for establishing the instantiation order of metamodel elements. The algorithm makes the proposed approach repeatable and usable even by not very skilled personnel, thus proposing an improvement to the actual situation. The proposed approach and the algorithm are also experimented through the construction of a design process (ASPECS) for developing dynamic hierarchical societies of agents. The approach we created is general enough to be applied in other development contexts (not only agent-oriented).

International audience; Microgrid (MG) networks are expected to help increase grid flexibility and the integration of renewable generation. To improve network cooperation performance, this paper proposes a novel distributed control strategy to manage energy flows using a multi-agent system. Power dispatching is achieved through flexible electricity markets to maximize MG profit. A distributed power flow calculation algorithm is proposed to guarantee that the power flow in the network is within line capacity. Simulation shows that the control strategy manages to ensure MG power balance while maximizing profit. Additionally, the power flow is within line capacity to guarantee system security.

Agent-Based modelling has been around us for quite some time now and has thus become a crucial factor for executing prediction-based planning, such as the transportation models for metropolitan cities. This paper undertakes the fundamental understanding of the agent-based modeling and simulation and its application to the transportation models while discussing the scope of its applications and advantages too. The paper then presents the concepts attributed to the social behaviors in conjunction with the agent-based modelling techniques applied so far. The literature review conducted in lieu of this work has resulted in agreement with the fact that the potential of agent-based modelling is by far greater than ever due to the ever-improving computing speeds and capabilities, while the understanding of complex human behavior will continue to be a challenge for simulations and automation techniques developed so far. (C) 2017 The Authors. Published by Elsevier B.V.

International audience

Carpooling is an emerging alternative transportation mode that is eco-friendly and sustainable as it enables commuters to save time, travel resource, reduce emission and traffic congestion. The procedure of carpooling consists of a number of steps namely; (i) create a motive to carpool, (ii) communicate this motive with other agents, (iii) negotiate a plan with the interested agents, (iv) execute the agreed plans, and (v) provide a feedback to all concerned agents. In this paper, we present a conceptual design of an agent-based model (ABM) for the carpooling a that serves as a proof of concept. Our model for the carpooling application is a computational model that is used for simulating the interactions of autonomous agents and to analyze the effects of change in factors related to the infrastructure, behavior and cost. In our carpooling application, we use agent profiles and social networks to initiate our agent communication model and then employ a route matching algorithm, and a utility function to trigger the negotiation process between agents. We developed a prototype of our agent-based carpooling application based on the work presented in this paper and carried out a validation study of our results with real data collected in Flanders, Belgium.

Agent technology is a software paradigm that permits to impleme nt large and complex distributed applications. In order to assist the development of multi -agent systems, agent-oriented methodologies (AOM) have been created in the last years to support modeling more and more complex applications in many different domains. By defining in a non-ambiguous way concepts used in a specific domain, Meta modeling may represent a step towards such interoperability. In the Transport domain, this paper propose an agent-oriented meta-model that provides rigorous concepts for conduct ing transportation system problem modeling. The aim is to allow analysts to produce a transportation system model that precisely captures the knowledge of an organization so that an agent -oriented requirements specification of the system-to-be and its operational corporate environment can be derived from it. To this end, we extend and adapt an existing meta-model, Extended Gaia, to build a meta -model and an adequate model for transportation problems. Our new agent -oriented meta-model aims to allow the analyst to model and specify any transportation system as a multi -agent system. Basedon the proposed meta-model, we proposes an approach for modeling and evaluating the Transportation System based on Stochastic Activity Network (SAN) components. The proposed process is based on seven steps from “Recognition” phase to “Quantitative Analysis” phase. These analyzes are based on the Dependability models which are built using the formalism Stochastic Activity Network. A real case study of Urban Public Transportatio n System has been conducted to show the benefits of the approach.

Patrolling an environment involves a team of agents whose goal usually consists in continuously visiting its most relevant areas as frequently as possible. Hence, patrolling consists in continuously visiting relevant areas of an environment, in order to efficiently surpervise or control it. The Ant Colony Optimization (ACO) is adopted as the solution approach that efficiently solves the multiagent patrolling problem. Two stratagies are employed: all agents are located on the same node at the initial time, and the agents are dispersed over the graph, they start to patrol from their new locations. This paper mainly describes the formulation problem of event detection by a multi-objective approach, an ACO and multi-agent approach are used to model and simulate the detection of events. Different parameters are evaluated in order to minimize them. The minimization of Worst Idleness, Energy consumption and Communicational Idleness are not compatible. It is therefore necessary to seek compromise solutions . The set of compromise solutions is called Pareto Front. The set of solutions and Pareto Front are presented respectively for the cases of non-dispersion and dispersion of agents.

Multiagent-based simulations enable us to validate different use-case scenarios in a lot of application domains. The idea is to develop a realistic virtual environment to test particular domain-specific procedures. This paper presents a multilevel model of a physic environment for the simulation of crowd in a virtual 3D building. The major contributions of this paper is the agentification of the model to support multilevel simulation of the environment. Finally, the application of the model inside an airport terminal is presented.

Multiagent-based simulations enable us to validate dierent use-case scenarios in a lot of application domains. The idea is to develop a realistic virtual environment to test particular domain-specic procedures. This paper presents our general framework for interactive multiagent-based simulations in virtual environments. The major contribution of this paper is the integration of the notion of ontology as a core element to the design process of a behavioral simulation. The proposed metamodel describes the concepts of a multiagent simulation using situated agents moving in a semantically enriched 3D environment. The agents perceive the geometric and semantic data in the surrounding environment. They are also able to act in this environment by using high-level actions, which are described by the ontology of the environment. The concepts relating to the environment, the agent, and the entire simulation models are presented. Additionally, guidelines are given to exploit the simulation results to characterize the agents. Finally, a simple application of the metamodel is presented, based upon the use of Industry Foundation Classes.

International audience; Due to their properties such as high energy density, lithium-ion battery packs are more and more widespread for many kinds of applications. Although a classical Battery Management System (BMS) can improve the performance and safety of a battery pack, it cannot deal with issues related to aging and cell faults. This paper presents a fault-response approach for battery packs operating at the cell level. It uses a reconfigurable topology combined with agents, that can increase the resilience of a pack in the face of faults. A cosimulation technique based on Matlab/Simulink and the JADE platform is used to test and validate the proposed approach.

With the fast development of human society, as environmental issues have drawn incomparable attention, energy efficiency is playing a significant role in residential buildings. Meanwhile, spending more time in homes leads people to constantly improve comfort there. Considering the fact that space heating makes a great contribution to residential energy consumption and thermal comfort, this paper presents a novel hybrid intelligent control (HIC) system to manage space heating devices in a smart home with advanced technologies to save energy while to increase thermal comfort level. The approach combines a meta-heuristic algorithm used to compute a setpoint from the Predicted Mean Vote model with a Proportional-Integral-Derivative controller for indoor temperature regulation. In order to validate the system, computer simulations are conducted and analysed. The results indicate the proposed control system can provide better thermal comfort comparing with other conventional and intelligent control methods, and consume less energy when demand response is considered.

Reactive multi-agent systems are increasingly applied to problem solving, modelling and simulation. Among the benefits of this approach, there is the capability to solve complex problems, whereas maintaining functional and conceptual simplicity of involved entities. Many examples have shown that approaches based on multi-agent systems are effective in solving complex problems such as life simulation and multi-robot cooperation for instance. Side-by-side vehicle platoon systems, which are studied in this article, can be adopted as a representative example of this class of applications. Based on preceding works, this study presents a local platoon control approach with obstacle avoidance. The proposed vehicle decision-making process is considered as a multi-agent system, the agents of which make collectively the best decision considering the perceived constraints and the preceding vehicle position and speed.

In complex systems, multiple aspects interact and influence each other. A vast number of entities are present in the system. Traditional modeling and simulation techniques fail to capture interactions between loosely coupled aspects of a complex distributed system. The objective of this work is to formalize and to specify a part of the Agent-oriented Software Process for Engineering Complex Systems methodology (Problem and Agency Domains) for modeling the holarchy of studied system by using a formal specification approch based on two formalisms: Petri Net and Object-Z language. Such a specification style facilitates the modeling of complex systems with both structural and behavioural aspects. Our generic approach is illustrated by applying it to FIRA Robot Soccer and is validated with the Symbolic Analysis Laboratory framework.

On the basis of analysis on the scene files of mobility models in Ad Hoc network, the paper presents a network topology snapshots capturing method to obtain the Ad Hoc network topology architecture at any moment. Through analyzing on the Ad Hoc network topology snapshots, some dynamic characteristic parameters of Ad Hoc network, such as the number of network topology in steady state or unsteady state appearing during a certain time, as well as the durative time of network topology in steady state or unsteady state, could be obtained statistically. Furthermore, the probability of the network topology invariability and variability event could be predicated by adopting the discrete time and continuous time Markov stochastic process theory. The simulation result shows that the statistical analysis technique on Ad Hoc network topology dynamic characteristic not only is effective, but also has the general attribute, which could be used in the statistical analysis technique on Ad Hoc network topology dynamic characteristic under any mobility models.

This article introduces a new collision avoidance model enabling the design of efficient realistic virtual pedestrian and cyclist behaviors. It is a force-based model using collision prediction with dynamic time-windows to predict future potential collisions with obstacles and other individuals. It introduces a new type of force called sliding force to allow a smooth avoidance of potential collisions while enabling the pedestrian to continue to progress towards its goal. Unlike most existing models, our forces are not scaled according to the distance to the obstacle but depending on the estimate of the collision time with this obstacle. This inherently integrates obstacles’ velocity. This greatly reduces the compu- tational complexity of the model while ensuring a smooth avoidance. This model is oscillation-free except for concave obstacles. It enables the reproduction of inherent emergent properties of real crowds such as spontaneous organizations of pedestrians into lane lines, etc. This model is computationally efficient and designed for real time simulation of large crowds.

This paper introduces a novel framework with the ability to adjust simulation’s accuracy level dynamically for simplifying the dynamics computation of large particle systems to improve simulation speed. Our new approach follows the overall structure of the well-known Fast Multipole Method (FMM) coming from computational physics. The main difference is that another level of simplification has been introduced by combining the concept of motion levels of detail from computer graphics with the FMM. This enables us to have more control on the FMM execution time and thus to trade accuracy for efficiency whenever possible. At each simulation cycle, the motion levels of detail are updated and the appropriate ones are chosen adaptively to reduce computational costs. The proposed framework has been tested on the simulation of a large dynamical flocking system. The preliminary results show a significant complexity reduction without any remarkable loss in the visual appearance of the simulation, indicating the potential use of the proposed model in more realistic situations such as crowd simulation.

Testing algorithms with real cars is a mandatory step in developing new intelligent abilities for future transportation systems. However, this step is sometimes hard to accomplish especially due to several problems. It is also difficult to reproduce the same scenario several times. Besides, some critical and/or real world forbidden scenarios cannot be tested. Thus, the comparison of several algorithms using the same experimental conditions is hard to realize. Considering that, it seems important to use simulation tools to perform scenarios with realistic conditions. The main problem with these tools is their distance from real conditions, since they deeply simplify the reality. This paper presents the architecture of the simulation/prototyping tool named Virtual Intelligent Vehicle Urban Simulator ( vivus ). The goal of vivus is thus to overcome the general drawbacks of classical solutions by providing the possibility of designing a vehicle virtual prototype with simulated embedded sensors and physical properties. Experiments made on linear platoon algorithms are exposed in this paper in order to illustrate the similarities between simulated results and those obtained with real cars.

This paper introduces an automatic learning algorithm based on genetic programming to derive local and multipole expansions required by the Fast Multipole Method FMM. FMM is a well-known approximation method widely used in the field of computational physics, which was first developed to approximately evaluate the product of particular N×N dense matrices with a vector in O(Nlog N) operations, while direct multiplication requires O(N2) operations. Soon after its invention, the FMM algorithm was applied successfully in many scientific fields such as simulation of physical systems Electromagnetic, Stellar clusters, Turbulence, Computer Graphics and Vision Light scattering and Molecular dynamics. However, FMM relies on the analytical expansions of the underlying kernel function defining the interactions between particles, which are not obvious to derive. This is a major factor that severely limits the application of the FMM to many interesting problems. Thus, the proposed automatic technique in this article can be regarded as a very useful tool helping practitioners to apply FMM to their own problems. Here, we have implemented a prototype system and tested it on various types of kernels. The preliminary results are very promising, and so we hope that the proposed method can be applied successfully to other problems in different application domains.

Vehicle platoon systems can be considered as good alternative solutions to traffic problems encountered in urban environment. Indeed, they allow to propose new public transportation models based on size adaptable trains. In other fields, such as military operation theaters and agricultural fields, platoon systems can also solve specific problems. The goal of this paper is to propose an agent based decision process that allow to handle the control of vehicles platoon. The decision process is composed of 5 interconnected layers each dealing with one specific aspect of the decision process including the perception interpretation, the spatial configuration choice and the control of the vehicles. This model has been tested in simulation by taking into account several geometrical configurations.

We consider the dynamic vehicle routing problem (dynamic VRP). In this problem, new customer demands are received along the day. Hence, they must be serviced at their locations by a set of vehicles in real time. The approach to address the problem is a hybrid method which manipulates the self-organizing map (SOM) neural network into a population based evolutionary algorithm. The method, called memetic SOM, illustrates how the concept of intermediate structure, also called elastic net or adaptive mesh concept, provided by the original SOM can naturally be applied into a dynamic setting. The experiments show that the heuristic outperforms the approaches that were applied to the Kilby et al. 22 problems with up to 385 customers. It performs better with respect to solution quality than the ant colony algorithm MACS-VRPTW, a genetic algorithm, and a multi-agent oriented approach, with a computation time used roughly 100 times lesser.

This article introduces a novel approach to increase the performances of multi-agent based simulations. We focus on a particular kind of multi-agent based simulation where a collection of interacting autonomous situated entities evolve in a situated environment. Our approach combines the fast multipole method coming from computational physics with agent-based microscopic simulations. The aim is to speed up the execution of a multi-agent based simulation while controlling the precision of the associated approximation. This approach may be considered as the first step of a larger effort aiming at designing a generic kernel to support efficient large-scale multi-agent based simulations. This approach is illustrated in this paper by the simulation of large scale flocking dynamical systems.

This paper presents the AMF framework that aims at supporting the design of metaheuristics using an organizational and multiagent approach. It aims at comparing and analyzing existing algorithms, and also facilitating the design of hybrid and new heuristics. This framework is based on an organizational model which describes the metaheuristics in terms of roles and interactions. In addition, we provide some methodological guidelines which favor the design of distributed and auto-adaptive metaheuristics.

The natural immune system is a subject of great research interests because it provides powerful and flexible information processing capability as a decentralized intelligent system. The immune system constitutes an excellent model of adaptive cooperation at the local level and of emergent behaviour at the global level. These concepts can be applied in the Multi-Agent Systems field where autonomous agents interact in order to solve a common goal. There exists several theories to explain immunological phenomena and software models to simulate various components in the immune system. This paper presents a formal specification of the idiotypic network theory viewed as an agent architecture. The specification gives a precise and non ambiguous description of this architecture which is validated through the automatic generation of traces and interesting properties are proven. This specification constitutes a starting point for understanding, reuse and implementations of this architecture. The approach is illustrated with the robot soccer simulation example.

We present a self-adaptive and distributed metaheuristic called Coalition-Based Metaheuristic (CBM). This method is based on the Agent Metaheuristic Framework (AMF) and hyper-heuristic approach. In CBM, several agents, grouped in a coalition, concurrently explore the search space of a given problem instance. Each agent modifies a solution with a set of operators. The selection of these operators is determined by heuristic rules dynamically adapted by individual and collective learning mechanisms. The intention of this study is to exploit AMF and hyper-heuristic approaches to conceive an efficient, flexible and modular metaheuristic. AMF provides a generic model of metaheuristic that encourages modularity, and hyper-heuristic approach gives some guidelines to design flexible search methods. The performance of CBM is assessed by computational experiments on the vehicle routing problem.

Holonic multiagent systems (HMAS) offer a promising software engineering approach for developing complex open software systems. However the process of building Multi-Agent Systems (MAS) and HMAS is mostly different from the process of building more traditional software systems as it introduces new design and development challenges. This paper introduces an agent-oriented software process for engineering complex systems called ASPECS. ASPECS is based on a holonic organisational metamodel and provides a step-by-step guide from requirements to code allowing the modelling of a system at different levels of details using a set of refinement methods. This paper details the entire ASPECS development process and provides a set of methodological guidelines for each process activity. A complete case study is also used to illustrate the design process and the associated notations. ASPECS uses UML as a modelling language. Because of the specific needs of agents and holonic organisational design, the UML semantics and notation are used as reference points, but they have been extended by introducing new specific profiles.

We study the hybridization of the self-organizing map (SOM) in an evolutionary algorithm to solve the Euclidean traveling salesman problem (TSP). The evolutionary dynamics consist of interleaving the SOM execution with a mapping operator, a fitness evaluation and a selection operator. SOM and mapping operators have a similar structure based on closest point findings and simple moves performed in the plane. We evaluate the approach on standard TSP test problems and show that it performs better, with respect to solution quality and/or computation time, than other neural network approaches given previously in the literature. Experiments are conducted on 91 publicly available TSP instances with up to 85900 cities.

Simulation, which creates abstractions of the system is an appropriate approach for studying complex systems that are inaccessible through direct observation and measurement. The problem with simulation of great numbers of interacting entities is that it is difficult to create a reliable and tractable abstraction of the real system. Indeed, simulating large numbers of entities requires great computing resources. A solution to avoid this problem is to use macroscopic models. However, this type of model may be unavailable or not reliable for the problem at hand and it does not allow the observation of individual behaviours. in this paper, a multilevel simulation model is proposed to allow the use of both microscopic and macroscopic techniques. This model is based upon Holonic Multi-Agent Systems which offer a promising approach for developing applications in complex domains characterised by a hierarchical structure. The proposed approach provides a generic scheduling model for multilevel Simulations: dynamically adapting the level of simulated behaviours while being as faithful as possible to the simulated model. it does not only manage the level of entities' behaviour but also of behaviours classically assigned to the environmental part of a simulation. A set of physics-based indicators is also introduced to dynamically determine the most suitable level for each entity and to maintain the best trade-off between simulation accuracy and constraints (dependent on the model or the experimental context). (C) 2008 Elsevier B.V. All rights reserved

The article presents the memetic SOM, an evolutionary algorithm embedding selforganizing maps as operators to address the Euclidean traveling salesman problem (TSP) and a vehicle routing problem with time windows (VRPTW). We show that the approach competes with other neural network applications on the TSP, with respect to solution quality. Then, we show that it allows extending the self-organizing map to deal with a version of the vehicle routing problem with time windows where the number of vehicles is an input, and by adding some walking distance from customers to bus stops.

Self-organized multi-agent systems (MAS) are still difficult to engineer, because, to deal with real world problems, a self-organized MAS should exhibit complex adaptive organizations. In this respect the holonic paradigm provides a solution for modelling complex organizational structures. Holons are defined as self-similar entities that are neither parts nor wholes. The organizational structure produced by holons is called a holarchy. A holonic MAS (HMAS) considers agents as holons that are grouped according to holarchies. The goal of this article is to introduce an architecture that allows holons to adapt to their environment. The metaphor is based upon the immune system and considers stimulations/requests as antigens and selected antibodies as reactions/answers. Each antibody is activated by specific antigens and stimulated and/or inhibited by other antibodies. The immune system rewards (respectively penalizes) selected antibodies, which constitutes a good (respectively wrong) answer to a request. This mechanism allows an agent to choose from a set of possible behaviors, the one that seems the best fit for a specific context. In this context, each holon, atomic or composed, encapsulates an immune system in order to select a behavior. For composed holons, each sub-holon is represented by the selected antibody of its immune system. The super-holon's immune system therefore contains one antibody per sub-holon. This recursive architecture corresponds with the recursive nature of the holarchy. This architecture is presented with an example of simulated robot soccer. From experiments under different conditions we show that this architecture has interesting properties.

The paper presents an extension of the self- organizing map (SOM) by embedding it into an evolutionary algorithm to solve the Vehicle Routing Problem (VRP). We call it the memetic SOM. The approach is based on the standard SOM algorithm used as a main operator in a population based search. This operator is combined with other derived operators specifically dedicated for greedy insertion moves, a fitness evaluation and a selection operator. The main operators have a similar structure based on the closest point findings and local moves performed in the plane. They can be interpreted as performing parallels and massive insertions, simulating the behavior of agents which interact continuously, having localized and limited abilities. This self-organizing process is intended to allow adaptation to noisy data as well as to confer robustness according to demand fluctuation. Selection is intended to guide the population based search toward useful solution compromises. We show that the approach performs better, with respect to solution quality and/or computation time, than other neural network applications to the VRP presented in the literature. As well, it substantially reduces the gap to classical Operations Research heuristics, specifically on the large VRP instances with time duration constraint.