A simulation engine to predict multi-agent work in complex, dynamic, heterogeneous systems

2011 ◽  
Author(s):  
Amy R. Pritchett ◽  
H. Claus Christmann ◽  
Matthew S. Bigelow
Keyword(s):  
Heterogeneous Systems ◽  
Complex Dynamic ◽  
Simulation Engine ◽  
Multi Agent

scholarly journals Agent Based Load Balancing in Grid Computing

2020 ◽  
Author(s):  
Wided Ali ◽  
Fatima Bouakkaz
Keyword(s):  
Load Balancing ◽  
Heterogeneous Systems ◽  
Agent Based ◽  
Selection Policy ◽  
Development Framework ◽  
Policy Selection ◽  
Proposed Model ◽  
Multi Agent ◽  
Location Policy ◽  
System Characteristics

Load-Balancing is an important problem in distributed heterogeneous systems. In this paper, an Agent-based load-balancing model is developed for implementation in a grid environment. Load balancing is realized via migration of worker agents from overloaded resources to underloaded ones. The proposed model purposes to take benefit of the multi-agent system characteristics to create an autonomous system. The Agent-based load balancing model is implemented using JADE (Java Agent Development Framework) and Alea 2 as a grid simulator. The use of MAS is discussed, concerning the solutions adopted for gathering information policy, location policy, selection policy, worker agents migration, and load balancing.

Multi-Agent Based Modeling of Warship Combat Command and Control System

2012 ◽  
Vol 246-247 ◽  
pp. 898-902
Author(s):  
Xing Zhong Wang
Keyword(s):  
Control System ◽  
Role Model ◽  
Heterogeneous Systems ◽  
Command And Control ◽  
Interaction Model ◽  
Design System ◽  
Model Interaction ◽  
Multi Agent ◽  
And Control ◽  
Command And Control System

To solving the inefficiency-problem in warship combat command and control system, this paper builds multi-agent oriented system architecture. It uses CORBA to support communication among heterogeneous systems and uses UML to design system prototype. Taking our two warships chasing an enemy-warship collaboratively for example, this paper illustrates the agent-oriented modeling process, including the role model, interaction model, state model and class model. This approach is able to meet the needs of modeling the warship combat command and control system.

scholarly journals MADRaS : Multi Agent Driving Simulator

2021 ◽  
Vol 70 ◽  
pp. 1517-1555
Author(s):  
Anirban Santara ◽  
Sohan Rudra ◽  
Sree Aditya Buridi ◽  
Meha Kaushik ◽  
Abhishek Naik ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Motion Planning ◽  
Open Source ◽  
Real World ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
Machine Learning Algorithms ◽  
Control Mode ◽  
Simulation Engine ◽  
Multi Agent

Autonomous driving has emerged as one of the most active areas of research as it has the promise of making transportation safer and more efficient than ever before. Most real-world autonomous driving pipelines perform perception, motion planning and action in a loop. In this work we present MADRaS, an open-source multi-agent driving simulator for use in the design and evaluation of motion planning algorithms for autonomous driving. Given a start and a goal state, the task of motion planning is to solve for a sequence of position, orientation and speed values in order to navigate between the states while adhering to safety constraints. These constraints often involve the behaviors of other agents in the environment. MADRaS provides a platform for constructing a wide variety of highway and track driving scenarios where multiple driving agents can be trained for motion planning tasks using reinforcement learning and other machine learning algorithms. MADRaS is built on TORCS, an open-source car-racing simulator. TORCS offers a variety of cars with different dynamic properties and driving tracks with different geometries and surface.  MADRaS inherits these functionalities from TORCS and introduces support for multi-agent training, inter-vehicular communication, noisy observations, stochastic actions, and custom traffic cars whose behaviors can be programmed to simulate challenging traffic conditions encountered in the real world. MADRaS can be used to create driving tasks whose complexities can be tuned along eight axes in well-defined steps. This makes it particularly suited for curriculum and continual learning. MADRaS is lightweight and it provides a convenient OpenAI Gym interface for independent control of each car. Apart from the primitive steering-acceleration-brake control mode of TORCS, MADRaS offers a hierarchical track-position – speed control mode that can potentially be used to achieve better generalization. MADRaS uses a UDP based client server model where the simulation engine is the server and each client is a driving agent. MADRaS uses multiprocessing to run each agent as a parallel process for efficiency and integrates well with popular reinforcement learning libraries like RLLib. We show experiments on single and multi-agent reinforcement learning with and without curriculum

Enabling Artificial Intelligence Studies in Off-Road Mobility Through Physics-Based Simulation of Multi-Agent Scenarios

2021 ◽  
Author(s):  
Aaron Young ◽  
Jay Taves ◽  
Asher Elmquist ◽  
Radu Serban ◽  
Dan Negrut ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Agents ◽  
Simulation Environment ◽  
Agent Communication ◽  
Testing Environment ◽  
Simulation Engine ◽  
Intelligence Studies ◽  
Multi Agent ◽  
Physics Based Simulation ◽  
Sensor Modeling

Abstract We describe a simulation environment that enables the development and testing of control policies for off-road mobility of autonomous agents. The environment is demonstrated in conjunction with the design and assessment of a reinforcement learning policy that uses sensor fusion and inter-agent communication to enable the movement of mixed convoys of human-driven and autonomous vehicles. Policies are learned on rigid terrain and are subsequently shown to transfer successfully to hard (silt-like) and soft (snow-like) deformable terrains. The enabling simulation environment is developed from the high fidelity, physics-based simulation engine Chrono. Five Chrono modules are employed herein: Chrono::Engine, Chrono::Vehicle, PyChrono, SynChrono and Chrono::Sensor. Vehicle’s are modeled using Chrono::Engine and Chrono::Vehicle and deployed on deformable terrain within the training/testing environment. Utilizing the Python interface to the C++ Chrono API called PyChrono and OpenAI Gym’s supporting infrastructure, training is conducted in a GymChrono learning environment. The GymChrono-generated policy is subsequently deployed for testing in SynChrono, a scalable, cluster-deployable multi-agent testing infrastructure built on MPI. SynChrono facilitates inter-agent communication and maintains time and space coherence between agents. A sensor modeling tool, Chrono::Sensor, supplies sensing data that is used to inform agents during the learning and inference processes. The software stack and the Chrono simulator are both open source. Relevant movies: [1].

scholarly journals Distributed Consensus of USVs under Heterogeneous UAV-USV Multi-Agent Systems Cooperative Control Scheme

2021 ◽  
Vol 9 (11) ◽  
pp. 1314
Author(s):  
Kai Xue ◽  
Tingyi Wu
Keyword(s):  
Finite Time ◽  
Cooperative Control ◽  
Sliding Mode ◽  
Heterogeneous Systems ◽  
Multi Agent Systems ◽  
Distributed Consensus ◽  
Control Approach ◽  
Finite Time Stability ◽  
Leader Following ◽  
Multi Agent

This paper addresses the formation motion control of heterogeneous multi-agent unmanned systems via a distributed consensus approach. The considered heterogeneous system consisted of unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs). A leader-following consensus scheme and APF method are used to construct UAV-USVs Formation task requirements. A fuzzy-based sliding mode control approach is proposed to ensure the formation assembles in a finite time, and the finite-time stability is proved by the Lyapunov stability theorem. To highlight the cooperation within the heterogeneous systems, such as UAV and USV, a novel vision-based path re-planning approach is proposed. Simulation results confirm the efficiency of the proposed approach.

scholarly journals Experimenting with robotic intra-logistics domains

2018 ◽  
Vol 18 (3-4) ◽  
pp. 502-519
Author(s):  
MARTIN GEBSER ◽  
PHILIPP OBERMEIER ◽  
THOMAS OTTO ◽  
TORSTEN SCHAUB ◽  
ORKUNT SABUNCU ◽  
...  
Keyword(s):  
Industrial Revolution ◽  
Experimental Studies ◽  
Answer Set Programming ◽  
Complex Dynamic ◽  
Fourth Industrial Revolution ◽  
Reasoning About Action ◽  
Multi Agent ◽  
Action Change ◽  
Alternative Solutions ◽  
Multi Agent Planning

AbstractWe introduce theasprilo1framework to facilitate experimental studies of approaches addressing complex dynamic applications. For this purpose, we have chosen the domain of robotic intra-logistics. This domain is not only highly relevant in the context of today's fourth industrial revolution but it moreover combines a multitude of challenging issues within a single uniform framework. This includes multi-agent planning, reasoning about action, change, resources, strategies, etc. In return,aspriloallows users to study alternative solutions as regards effectiveness and scalability. Althoughasprilorelies on Answer Set Programming and Python, it is readily usable by any system complying with its fact-oriented interface format. This makes it attractive for benchmarking and teaching well beyond logic programming. More precisely,aspriloconsists of a versatile benchmark generator, solution checker and visualizer as well as a bunch of reference encodings featuring various ASP techniques. Importantly, the visualizer's animation capabilities are indispensable for complex scenarios like intra-logistics in order to inspect valid as well as invalid solution candidates. Also, it allows for graphically editing benchmark layouts that can be used as a basis for generating benchmark suites.

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