Multi-Agent Systems

This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019

User-Based Solutions for Increasing Level of Service in Bike-Sharing Transportation Systems

International audienceBike-sharing transportation systems have been well studied from a top-down viewpoint, either for an optimal conception of the system , or for a better statistical understanding of their working mechanisms in the aim of the optimization of the management strategy. Yet bottom-up approaches that could include behavior of users have not been well studied so far. We propose an agent-based model for the short time evolution of a bike-sharing system, with a focus on two strategical parameters that are the role of the quantity of information users have on the all system and the propensity of user to walk after having dropped their bike. We implement the model in a general way so it is applicable to every system as soon as data are available in a certain format. The model of simulation is parametrized and calibrated on processed real time-series of bike movements for the system of Paris. After showing the robustness of the simulations by validating internally and externally the model, we are able to test different user-based strategies for an increase of the level of service. In particular, we show that an increase of user information can have significant impact on the homogeneity of repartition of bikes in docking stations, and, what is important for a future implementation of the strategy, that an action on only 30% of regular users is enough to obtain most of the possible amelioration

Application of Genetic Algorithms to a Multi-Agent Autonomous Pilot of Motorcycles

Thesis (M.S) -- Indiana University South Bend, 2005.The physics behind motorcycle driving are well understood and implemented by studying the laws of kinetics and kinematics behind the operation of the single track motor vehicle. In this thesis I worked with an application which is currently using OpenGL and implements an interactive motorcycle simulator which is based on the laws of physics. This application involves a multi-agent pilot capable of autonomously driving the vehicle using some configurable equations. I have applied genetic algorithms to find suitable values for the parameters of the pilot by testing it in a non graphical environment, and I visually verified the results of the genetic algorithms with the graphical interface application. The performance of the pilot derived by the genetic algorithms is also compared with the manually configured pilot

Feedback control for exergames

The concept of merging exercise equipment with video games, known as exergaming, has the potential to be one of the main tools used in addressing the current rising obesity epidemic. Existing research shows that exergaming can help improve fitness and additionally motivate people to become more active. The two key elements of attractiveness - how much people want to play or use the exergaming system; and effectiveness – how effective the exergaming system is in actually increasing or maintaining physical fitness, need to be maximised to obtain the best outcomes from an exergaming system; we put this forward as the Dual Flow Model. As part of the development of our exergame system we required the use of a heart rate response simulator. We discovered that there was no existing quantitative model appropriate for the simulation of heart rate responses to exercise. In order to overcome this, we developed our own model for the simulation of heart rate response. Based on our model, we developed a simulation tool known as the Virtual Body Simulator, which we used during our exergame development. Subsequent verification of the model using the trial data indicated that the model accurately represented exergame player heart rate responses to a level that was more than sufficient for exergame research and development. In our experiment, attractiveness was controlled by manipulation of the game difficulty to match the skill of the player. The balance of challenge and skills to facilitate the attainment of the flow state, as described by Csikszentmihalyi (1975), is widely accepted as a motivator for various activities. Effectiveness, in our experiments was controlled through exercise intensity. Exercise intensity was adjusted based on the player‟s heart rate to maintain intensity within the limits of the ASCM Guidelines (ACSM, 2006) for appropriate exercise intensity levels. We tested the Dual Flow Model by developing an exergame designed to work in four different modes; created by selectively varying the control mechanisms for exercise workout intensity and game mental challenge. We then ran a trial with 21 subjects who used the exergame system in each of the different modes. The trial results in relation to the Dual Flow Model showed that we developed an excellent intensity control system based on heart rate monitoring; successfully managing workout intensity for the subjects. However, we found that the subjects generally found the intensity controlled sessions less engaging, being closer to the flow state in the sessions where the intensity was controlled based on heart rate. The dynamic difficulty adjustment system developed for our exergame also did not appear to help facilitate attainment of the flow state. Various theories are put forward as to why this may have occurred. We did find that challenge control had an impact on the actual intensity of the workout. When the intensity was not managed, the challenge control modes were generally closer to the desired heart rates. While the difference was not statistically very large, there was a strong correlation between the intensity of the different modes. This correlation was also present when looking at the players‟ perception of intensity, indicating that the difference was enough to be noticed by the subjects