Real World Multi-agent Systems: Information Sharing, Coordination and Planning

2007 ◽  
pp. 154-165 ◽  
Author(s):  
Frans C. A. Groen ◽  
Matthijs T. J. Spaan ◽  
Jelle R. Kok ◽  
Gregor Pavlin
Keyword(s):  
Information Sharing ◽  
Real World ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent

Society of Agents

2020 ◽  
pp. 160-183
Author(s):  
Steven Walczak
Keyword(s):  
Problem Solving ◽  
Information Sharing ◽  
Multi Agent Systems ◽  
New Classification ◽  
Conceptual Foundation ◽  
Agent Systems ◽  
Multi Agent ◽  
Coordination And Collaboration ◽  
Agent Coordination ◽  
New Framework

The development of multiple agent systems faces many challenges, including agent coordination and collaboration on tasks. Minsky's The Society of Mind provides a conceptual view for addressing these multi-agent system problems. A new classification ontology is introduced for comparing multi-agent systems. Next, a new framework called the Society of Agents is developed from Minsky's conceptual foundation. A Society of Agents framework-based problem-solving and a Game Society is developed and applied to the domain of single player logic puzzles and two player games. The Game Society solved 100% of presented Sudoku and Kakuro problems and never lost a tic-tac-toe game. The advantage of the Society of Agents approach is the efficient re-utilization of agents across multiple independent game domain problems and a centralized problem-solving architecture with efficient cross-agent information sharing.

scholarly journals Multi-agent systems for the real world

2010 ◽  
Author(s):  
Rajiv T. Maheswaran ◽  
Craig M. Rogers ◽  
Romeo Sanchez ◽  
Pedro Szekely ◽  
Robert Neches
Keyword(s):  
Real World ◽  
Multi Agent Systems ◽  
The Real ◽  
Agent Systems ◽  
Multi Agent

Information Sharing via Active Sensing in a Multi-Agent System Inspired by Echolocating Bats

2014 ◽  
Author(s):  
Yuan Lin ◽  
Nicole Abaid
Keyword(s):  
Information Sharing ◽  
Sensory Information ◽  
Three Dimensions ◽  
Particle Model ◽  
Model Parameters ◽  
Active Sensing ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Sound Environment ◽  
Multi Agent

Current models for multi-agent systems almost exclusively employ sensory modalities such as vision where agents passively receive information from the environment. Active sensing, defined as acquiring environmental information using self-generated signals, allows widespread sharing of sensory information among agents and thus gives rise to more complex interactions within engineered multi-agent systems using radar or sonar, for example. In nature, bat swarms are animal groups that successfully employ active sensing with each individual broadcasting echolocation pulses in the environment and responding to echoes. Bats flying in groups may cope with the dense sound environment through their behavior; one hypothesized strategy is the cessation of echolocation pulses in the presence of peers and “eavesdropping”, which has been demonstrated in controlled laboratory settings. In this work, we build a self-propelled-particle model with each agent avoiding obstacles in three dimensions by emitting echolocation pulses of a unique frequency. We implement a bat-inspired rule of eavesdropping to take advantage of information sharing via active sensing while reducing the energy expenditure of the group. Through a simulation study, we show that agents indeed capitalize on peers’ pulses and echoes for obstacle avoidance and we find a maximum of this effect for a set of model parameters which relate to the domain size.

scholarly journals Agent organisations: from independent agents to virtual organisations and societies of agents

2020 ◽  
Vol 9 (4) ◽  
pp. 55-70
Author(s):  
Jose Alberto Maestro-Prieto ◽  
Sara Rodríguez ◽  
Roberto Casado ◽  
Juan Manuel Corchado
Keyword(s):  
Real World ◽  
Virtual Organisations ◽  
Multi Agent Systems ◽  
Agent Based ◽  
Agent Systems ◽  
Conflicting Objectives ◽  
Real World Applications ◽  
Multi Agent ◽  
The Way

Real world applications using agent-based solutions can include many agents that needs communicate and interact each other in order to meet their objectives. In open multi-agent systems, the problems may include the organisation of a large number of agents that may be heterogeneous, of unpredictable provenance and where competitive behaviours or conflicting objectives may occur. An overview of the alternatives for dealing with these problems is presented, highlighting the way they try to solve or mitigate these problems.

Swarm and Multi-agent Time-based A* Path Planning for Lighter-Than-Air Systems

2020 ◽  
Vol 08 (03) ◽  
pp. 253-260
Author(s):  
Jason Gibson ◽  
Tristan Schuler ◽  
Loy McGuire ◽  
Daniel M. Lofaro ◽  
Donald Sofge
Keyword(s):  
Path Planning ◽  
Dynamic Simulation ◽  
Real World ◽  
Simulation Environment ◽  
Multi Agent Systems ◽  
Time Step ◽  
External Disturbances ◽  
Agent Systems ◽  
Multi Agent ◽  
Velocity Information

This work develops and implements a multi-agent time-based path-planning method using A*. The purpose of this work is to create methods in which multi-agent systems can coordinate actions and complete them at the same time. We utilized A* with constraints defined by a dynamic model of each agent. The model for each agent is updated during each time step and the resulting control is determined. This results in a translational path that each of the agents is physically capable of completing in synchrony. The resulting path is given to the agents as a sequence of waypoints. Periodic updates of the path are calculated, utilizing real-world position and velocity information, as the agents complete the task to account for external disturbances. Our methodology is tested in a dynamic simulation environment as well as on real-world lighter-than-air robotic agents.

A FORMAL HOLONIC FRAMEWORK WITH PROVED SELF-ORGANIZING CAPABILITIES

2007 ◽  
Vol 16 (01) ◽  
pp. 7-25 ◽  
Author(s):  
SEBASTIAN RODRIGUEZ ◽  
VINCENT HILAIRE ◽  
PABLO GRUER ◽  
ABDER KOUKAM
Keyword(s):  
Real World ◽  
Organizational Structures ◽  
The Self ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Real World Problem ◽  
Multi Agent ◽  
Self Similar ◽  
Systems Architectures ◽  
Self Organizing

Numerous works aim to design agents and multi-agent systems architectures in order to enable cooperation and coordination between agents. Most of them use organizational structures or societies metaphor to define the MAS architecture. It seems improbable that a rigid unscalable organization could handle a real world problem, so it is interesting to provide agents with abilities to self-organize according to problem's objectives and environment dynamics. We have chosen the holonic paradigm to provide these abilities to agents. Holons are recursive self-similar entities which are organized in an emergent society — an holarchy. The aim of this paper is to present a formally specified framework for holonic MAS which allows agents to self-organize. The framework is illustrated by an example drawn from a real world problem. Some pertinent properties concerning the self-organizing capabilities of this framework are then proved.

scholarly journals Real-time multi-agent systems: rationality, formal model, and empirical results

2021 ◽  
Vol 35 (1) ◽  
Author(s):  
Davide Calvaresi ◽  
Yashin Dicente Cid ◽  
Mauro Marinoni ◽  
Aldo Franco Dragoni ◽  
Amro Najjar ◽  
...  
Keyword(s):  
Real Time ◽  
Real World ◽  
Intelligent Systems ◽  
Formal Model ◽  
General Purpose ◽  
Timing Constraints ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
The Road ◽  
Multi Agent

AbstractSince its dawn as a discipline, Artificial Intelligence (AI) has focused on mimicking the human mental processes. As AI applications matured, the interest for employing them into real-world complex systems (i.e., coupling AI with Cyber-Physical Systems—CPS) kept increasing. In the last decades, the multi-agent systems (MAS) paradigm has been among the most relevant approaches fostering the development of intelligent systems. In numerous scenarios, MAS boosted distributed autonomous reasoning and behaviors. However, many real-world applications (e.g., CPS) demand the respect of strict timing constraints. Unfortunately, current AI/MAS theories and applications only reason “about time” and are incapable of acting “in time” guaranteeing any timing predictability. This paper analyzes the MAS compliance with strict timing constraints (real-time compliance)—crucial for safety-critical applications such as healthcare, industry 4.0, and automotive. Moreover, it elicits the main reasons for the lack of real-time satisfiability in MAS (originated from current theories, standards, and implementations). In particular, traditional internal agent schedulers (general-purpose-like), communication middlewares, and negotiation protocols have been identified as co-factors inhibiting real-time compliance. To pave the road towards reliable and predictable MAS, this paper postulates a formal definition and mathematical model of real-time multi-agent systems (RT-MAS). Furthermore, this paper presents the results obtained by testing the dynamics characterizing the RT-MAS model within the simulator MAXIM-GPRT. Thus, it has been possible to analyze the deadline miss ratio between the algorithms employed in the most popular frameworks and the proposed ones. Finally, discussing the obtained results, the ongoing and future steps are outlined.

scholarly journals Cooperative Information Sharing to Improve Distributed Learning in Multi-Agent Systems

2005 ◽  
Vol 24 ◽  
pp. 407-463 ◽  
Author(s):  
P. S. Dutta ◽  
N. R. Jennings ◽  
L. Moreau
Keyword(s):  
Information Sharing ◽  
Formal Analysis ◽  
Empirical Evaluation ◽  
Task Completion ◽  
Multi Agent Systems ◽  
Long Distance ◽  
State Information ◽  
Agent Systems ◽  
Systems Research ◽  
Multi Agent

Effective coordination of agents' actions in partially-observable domains is a major challenge of multi-agent systems research. To address this, many researchers have developed techniques that allow the agents to make decisions based on estimates of the states and actions of other agents that are typically learnt using some form of machine learning algorithm. Nevertheless, many of these approaches fail to provide an actual means by which the necessary information is made available so that the estimates can be learnt. To this end, we argue that cooperative communication of state information between agents is one such mechanism. However, in a dynamically changing environment, the accuracy and timeliness of this communicated information determine the fidelity of the learned estimates and the usefulness of the actions taken based on these. Given this, we propose a novel information-sharing protocol, post-task-completion sharing, for the distribution of state information. We then show, through a formal analysis, the improvement in the quality of estimates produced using our strategy over the widely used protocol of sharing information between nearest neighbours. Moreover, communication heuristics designed around our information-sharing principle are subjected to empirical evaluation along with other benchmark strategies (including Littman's Q-routing and Stone's TPOT-RL) in a simulated call-routing application. These studies, conducted across a range of environmental settings, show that, compared to the different benchmarks used, our strategy generates an improvement of up to 60% in the call connection rate; of more than 1000% in the ability to connect long-distance calls; and incurs as low as 0.25 of the message overhead.

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