Simulation Of Agent Behavior Research Articles

TrajGen: Generating Realistic and Diverse Trajectories With Reactive and Feasible Agent Behaviors for Autonomous Driving

Realistic and diverse simulation scenarios with reactive and feasible agent behaviors can be used for validation and verification of self-driving system performance without relying on expensive and time-consuming real-world testing. Existing simulators rely on heuristic-based behavior models for background vehicles, which cannot capture the complex interactive behaviors in real-world scenarios. Meanwhile, existing learning-based methods are typical insufficient, yielding behaviors of traffic participants that frequently collide or drive off the road especially for a long horizon. To address this issue, we propose TrajGen, a two-stage trajectory generation framework, which can capture more realistic and diverse behaviors directly from human demonstration. In particular, TrajGen consists of the multi-modal trajectory prediction stage and the reinforcement learning based trajectory modification stage. In the first stage, we propose a novel auxiliary RouteLoss for the trajectory prediction model to generate multi-modal diverse trajectories in the drivable area. In the second stage, reinforcement learning is used to track the predicted trajectories while avoiding collisions, which can improve the reactivity and feasibility of generated trajectories. In addition, we develop a simulator I-Sim that can provide support for data-driven agent behavior simulation and train reinforcement learning models in parallel based on naturalistic driving data. The vehicle model in I-Sim can guarantee that the generated trajectories by TrajGen satisfy vehicle kinematic constraints. Finally, we give comprehensive metrics to evaluate generated trajectories for simulation scenarios, which shows that TrajGen outperforms either trajectory prediction or inverse reinforcement learning in terms of fidelity, reactivity, feasibility, and diversity.

3D Affordance Field based Crowd Agent Behavior Simulation

3D Affordance Field based Crowd Agent Behavior Simulation

An Approach to Intelligent Traffic Management System Using a Multi-agent System

Intelligent traffic management can be considered one of the most promising solutions to contemporary traffic problems. The traffic in transportation associated with emergency conditions including psychiatric improvement in transport network, allocation of variable traffic flows, reducing the number of the crowded traffic roads and paths as well as its negative effects (such as delays, waiting time, stress of driver, noise and air pollution, and blocking the assistive devices). This research has been used by new multi-agent systems to manage traffic. On the one hand, the proposed algorithm includes traffic flow improvement in emergency conditions until it is considered as real-time traffic information and in other hand, by preventing the increase the volume of a paths have effects in the reduce of crowded traffic situations at a specific time (for example, providing the proposed paths in the shortest time). In this article, the integrated environment is including JACK software for having a virtual agent behavior simulation. In general, we can use the different simulation form in a distribution network to display the crowded and traffic. In this article, the JACK software is used for having the explicit capabilities and supporting the common of this software in modelling the multi-agent systems, such as agents, design, event and capabilities. In addition, designing and analyzing of this interaction is simplest than the existed designs in JACK software. As a result of the proposed model, it seems reasonable that the proposed approach is different than previous works in each case. On the one hand, there are modeling system in the different tasks as intelligent agents dependent to present the simple and effective road network. In this case, it may correct and changes the actions of driver in emergency conditions by the concept of agent cooperation for achieving the common target.

Сложность мировой политики: к вопросу о новой методологии анализа

The article addresses the use of complexity theory in the analysis of international relations. Complexity theory points to the inherent unpredictability of world relations theories. The current international system has reached such complexity level of politics that it cannot be analyzed on the basis of linear rationality used in the international standard, which leads to non-deterministic causality. The article discusses the evolution and basic tenets of the complexity theory, approaches to the world politics analysis established within its framework. The complexity research methodology focuses on actors and their values, interests and beliefs, as well as on the nature of interactions between them. In this regard, complexity theory is closely related to the modern constructivist theory of international relations. Today, the number of international actors is increasing, which increases the complexity of the world system. Therefore, analytical methodology should take into account the role of non-state actors as well as the high complexity of contemporary world politics, which is multi-layered and dynamic. In this respect, the complexity theory is associated with contemporary neoliberalism. Agent-oriented computer-based modeling is the main and a very promising scientific methodology applied to the study of complex adaptive systems, including world politics. In the complexity theory, this modeling implies the simulation of agent behavior (in this regard, agents are international relations actors), based on the simulation of the patterns, according to which agents process the information using adaptive mechanisms or behavior limiting norms and rules. In general, in terms of the complexity theory, foreign policy issues are always multidimensional, decisions have unintended consequences and are never simple. However, complex systems can be controlled, and even their structure can be altered. Still, there are no unambiguous tools of influencing the situation, and all recommendations should be taken with caution. The authors conclude that the complexity theory offers new explanations, research directions and practical perspectives for international relations research. Agent-oriented computer simulation also allows the incorporation into the analysis of a significant part of the knowledge accumulated in the international relations traditional theory framework. Acknowledgements. The article was prepared as a part of the project № 14-18-02973 “Long-Term Prognosis of the International Relations Development” fi nanced by the Russian Scientifi c Foundation. The authors express gratitude to M.M. Chaikovskii, Dr. Sci. (Physics and Mathematics), for the consultations on the mathematical aspects of the complex systems analysis.

Infrastructure to simulate intelligent agents in cloud environments

Nowadays there is a clear trend towards using methods and tools that can help the development of Simulation Systems. In this regard, Multiagent System (MAS) is a key technology that allows simulating user behavior. When the user is the focus of the simulation, Ambient Intelligent (AmI) increases in importance. AmI is an emerging multidisciplinary area based on ubiquitous computing that aims to adapt the environment to the needs of the user. Moreover, Cloud Computing is revolutionizing the services provided through the Internet, continually adapting itself in order to maintain the quality of its services. This study presents a multiagent based middleware for the simulation of agent behavior in cloud environments, and uses this information to adapt to the environment as AmI proposed. The main challenge of this work is the design and development of a new middleware that makes possible the communication between the technology in charge of the development of MAS and the technology in charge of the simulation, visualization and analysis of the behavior of the agents using the potential of cloud computing. The platform makes the computation and communication possible by following the principles proposed by AmI.

A PATH PLANNING METHOD BASED ON CELLULAR AUTOMATA FOR COOPERATIVE ROBOTS

A Cellular Automaton-based technique suitable for solving the path planning problem in a distributed robot team is outlined. Real-time path planning is a challenging task that has many applications in the fields of artificial intelligence, moving robots, virtual reality, and agent behavior simulation. The problem refers to finding a collision-free path for autonomous robots between two specified positions in a configuration area. The complexity of the problem increases in systems of multiple robots. More specifically, some distance should be covered by each robot in an unknown environment, avoiding obstacles found on its route to the destination. On the other hand, all robots must adjust their actions in order to keep their initial team formation immutable. Two different formations were tested in order to study the efficiency and the flexibility of the proposed method. Using different formations, the proposed technique could find applications to image processing tasks, swarm intelligence, etc. Furthermore, the presented Cellular Automaton (CA) method was implemented and tested in a real system using three autonomous mobile minirobots called E-pucks. Experimental results indicate that accurate collision-free paths could be created with low computational cost. Additionally, cooperation tasks could be achieved using minimal hardware resources, even in systems with low-cost robots.