Нечеткая кластеризация в задаче управления беспилотными транспортными средствами
This work is devoted to the development of an evolutionary algorithm for fuzzy clustering of an ensemble of interacting conventional and unmanned vehicles in order to identify the relationship between stable groups of agents and initial modeling parameters.
The series Lecture Notes in Computer Science (LNCS), including its subseries Lecture Notes in Artificial Intelligence (LNAI) and Lecture Notes in Bioinformatics (LNBI), has established itself as a medium for the publication of new developments in computer science and information technology research and teaching - quickly, informally, and at a high level.
The two-volume set LNCS 11508 and 11509 constitutes the refereed proceedings of of the 18th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2019, held in Zakopane, Poland, in June 2019.
The 122 revised full papers presented were carefully reviewed and selected from 333 submissions. The papers included in the first volume are organized in the following five parts: neural networks and their applications; fuzzy systems and their applications; evolutionary algorithms and their applications; pattern classification; artificial intelligence in modeling and simulation.
The papers included in the second volume are organized in the following five parts: computer vision, image and speech analysis; bioinformatics, biometrics, and medical applications; data mining; various problems of artificial intelligence; agent systems, robotics and control.
Continuous stochastic agent-based model of human behavior in a confined space with a given geometry is presented in the paper. An “exit front” is defined, also the flow characteristics of agents is studied, in particular, its intensity.
Agent-based modeling and simulation was applied to investigate a set of problems in the energy context. The paper shows advantages of the agent based modeling approach. The method to define agents-consumers in simulation tool AnyLogic and the approach to simulating investment project risk are suggested.
The paper represents an application of agent based approach for simulation modeling as the new way to create epidemic models. It is much differed from common disease spreading simulation technique, which uses differential equations. The AnyLogic 6 agent based computer simulation model of the influenza spreading was created. The model allows making a short-range sickness rate forecast based on current morbidity statistics.
Nowadays simulation modeling is applied for solving a wide range of problems. There are simulations which require significant performance and time resources. To decrease overall simulation time a model can be converted to a distributed system and executed on a computer network. The goal of this project is to create a library enabling clear and rapid development parallel discrete event models in AnyLogic. The library is aimed for professionals in computer simulation and helps to reduce code amount. The project includes a research on different synchronization algorithms. In this paper we present techniques which can be used in creating distributed models. We present comparison of a single threaded model with a distributed model implementing optimistic algorithm. The comparison shows a significant improvement in wallclock time achieved by separating the model into independent submodels with minimal communications.