Sameh El Hadouaj

Multi-agent simulation model of pedestrians crowd based on psychological theories

The simulation of pedestrian crowd that reflects reality is a major challenge for scientists and research. Several simulation pedestrian crowd models have been proposed such as cellular automata models, agent-based models, regression models, etc. It is very important to note that agent based models are able, over others approaches to provide a natural description of the system and then to capture emergent phenomena and complex human behaviors. Usually, these models include psychological theories in order to obtain a more realistic simulation of pedestrians behavior. However, a great number of them do not cover all the psychological factors necessary for a pedestrian located in a crowd. This causes a lack of realism. Thus, we propose a multi-agent simulation model which is based on psychological theories. It takes into account the major normal conditions of a simple pedestrian situated in a crowd such as his preferences, a realistic perception of the environment in which he is situated, etc. Our objective is to simulate realistically the pedestrian crowd phenomenon towards a simulation of a believable pedestrian behavior. The conducted experiments show that our model is able to produce realistic pedestrian behaviors.

Multi-agent based simulation of animal food selective behavior in a pastoral system

Pastoral systems are considered as complex systems, given the number of entities and the multitude of interactions and levels of granularity. To conduct a study of such system taking into account the interactions and their results, analytical approaches are not adequate. In this paper, we present an agent-based model of the animal behavior in the pastoral system taking into account the selective food aspect. This model has been validated using a multi-agent based simulation implemented on the simulation platform Cormas. The obtained results reflect the importance of this aspect in the animal behavior and its effects on vegetation cover.

PHuNAC Model: Creating Crowd Variation with the MBTI Personality Mode

Several crowd simulators simulate only homogenous pedestrians. In the reality, there are different personalities. This personality variation affects the pedestrian behavior and the fate of the crowd. In this paper, we extend the HuNAC (Human Nature of Autonomous Crowd) model by providing each pedestrian agent with a personality in order to examine how the emergent behavior of the crowd is affected. We use the MBTI personality theory as a basis for agent psychology. The aim of this work is to improve HuNAC model and consequently to improve results. In this context, we have a new version of our model which we called PHuNAC model (Personalities’ Human Nature of Autonomous Crowd). Our PHuNAC model is a multi-agent simulation of pedestrian crowd model .The conducted experiments show that PHuNAC model is able to produce more realistic pedestrian behaviors than the HuNAC model.

A dynamic decentralised coalition formation approach for task allocation under tasks priority constraints

In some real systems, individual agents often need to form coalitions in order to achieve an overall mission that would be impossible for a single robot. Due to communication and computation constraints, it is infeasible for agents to interact directly with all other agents to form coalitions. This problem becomes more complex and challenging when tasks have different priority levels of execution. Toward this end, in this paper, a decentralized dynamic coalition formation approach is presented. The proposed mechanism operates in a neighborhood agent network. Based on self-adaptation principles, this technique enables agents to dynamically join new coalitions with a higher priority at any time without degrading the system. We empirically evaluate our method through a comparison between a centralized and a decentralized approaches. Experimental results demonstrate the good performance of our proposed approach in terms of computation time with respect to the state-of-the-art approach.

PHuNAC Model: Emergence of Crowd’s Swarm Behavior

The swarm behavior of pedestrians in a crowd, generally, causes a global pattern to emerge. A pedestrian crowd simulation system must have this emergence in order to prove its effectiveness. For this reason, the aim of our work is to demonstrate the effectiveness of our model PHuNAC (Personalities’ Human’s Nature of Autonomous Crowds) and also prove that the swarm behavior of pedestrians’ agents in our model allows the emergence of these global patterns. In order to validate our approach, we compared our system with real data. The conducted experiments show that the model is consistent with the various emergent behaviors and thus it provides realistic simulated pedestrian’s behavior.

Distributed Government Architecture Based on Intelligent Agents and Web Services

Over the past few years, web service and multi agent approach has been used frequently in the development of E-government services. Most of the existing architectures use a middleware and hence are centralized ones. This type of solutions can increase transaction time and communication cost. Distributed models can overcome this problem. However, a major part of the existing works presents a coordination approach between Government Entities usually based on workflow systems. Thus, in order to use these approaches, it is necessary to rebuilt or modify the entails IT infrastructure of governmental organizations. This alternative is so difficult and costly especially in developing countries where Government Entities are not necessary equipped with workflow systems. This paper describes a new distributed model for e-government services based on the potential of multi-agent system. Our model is concerned with only inputs and outputs of each Government Entity system. So, the proposed solution does not require equipping each Government Entity by a Workflow. The applicability of our model is illustrated by a case study of an identity card creation in Tunisia.

Multi-agent Modelling for a Regulation Support System of Public Transport

The increasing cost of private transport and the rising pollution of the environment pose serious problems in society, economy and environment. The public transport has become a major challenge of collective and daily life. However, to encourage people to use a public transport system, the offered service have to be with good quality. This paper gives effective solutions to improve the quality of public transport service provided to users. In this paper, we present a Regulation Support System of Public Transport (RSSPT), based on Multi-agents approach that allows supervising and regulating a multimodal public transport. Its purpose is to adjust the vehicle schedules where several disturbances come simultaneously. The adjustment is based on actual traffic conditions. It covers the major criteria that have to be optimized in a traffic regulation: punctuality, regularity and correspondence.

A Reactive Anticipation for Autonomous Robot Navigation

Nowadays, mobile robots are expected to carry out various tasks in all kinds of application fields ranging from manufacturing plants, transportation, nursing service, resource or underwater exploration. In all these applications, robots should navigate autonomously in uncertain and dynamic environments in order to achieve their goals. So, the most current challenge in the development of autonomous robot control systems is making them respond intelligently to changing environments. Navigation in such environments involves many mechanisms such as: object detection, perception, internal building model, decision making, prediction of the future state of the environment and on-line navigation. To attend its goal safely, the robot should minimise interaction with other actors in order to avoid conflict situations. Generally, this problem comes up when many robots and/or actors would have access to the same space at the same time. In this case, the control of autonomous robotic navigation for conflict resolution has been widely studied. Some researchers have been focused on navigation in dynamic environments, where either reactive systems (producing real time behaviour), deliberative systems (introduce reasoning and need much more time to calculate a suitable decision) or hybrid systems (combine deliberative and reactive approaches) have been used in order to attend a known goal. Typically, reactive systems are used to deal with simple problems (detect an obstacle, go away from an obstacle, follow a wall, etc.). Nevertheless, reactive systems are typically less affected by errors and do not require an explicit model of the environment in order to navigate inside an unknown space. Furthermore, they usually deal only with local information that may be captured at real time. However, in reactive systems, the robot can be derived to a conflict situation with other actors because they ignore prediction and reasoning in the decision process. To face this problem, global planning approaches are used. They consist of elaborating a global plan from beginning state to goal state. These approaches need prior complete information about the state of the environment, so they do not take into account the environment uncertainty. So, in more complex situations, hybrid approaches are used. They combine reactive approaches according to a higher level in order to include anticipation of the state of the environment in the decision process. In these cases, low level control operates in a reactive way (local navigation) whereas high level systems tend to be deliberative. It provides, at each step, a partial moving plan to the robot. But these systems are complex because they need much more time to calculate or to update a suitable trajectory toward a predefined goal. In this situation, it is interesting to introduce prediction