scholarly journals Agent, Behaviour, Trace, Repeat: Understanding the Cognitive Processes Involved in Human Stigmergic Coordination

2021 ◽  
Author(s):  
Sabine Topf ◽  
Maarten Speekenbrink
Keyword(s):  
Cognitive Processes ◽  
Future Research ◽  
Shared Environment ◽  
Multi Agent Systems ◽  
Causal Inferences ◽  
Individual Agent ◽  
Intentional Communication ◽  
Agent Systems ◽  
Multi Agent ◽  
Exploration Exploitation

Stigmergy refers to the coordination of agents via artifacts of behaviours (behavioural traces) in the shared environment. Whilst primarily studied in biology and computer science/robotics, stigmergy underlies many human indirect interactions, both offline (e.g., trail building) and online (e.g., development of open-source software). In this review, we provide an introduction to stigmergy and emphasise how and where human stigmergy is distinct from animal or robot stigmergy, such as intentional communication via traces and causal inferences from the traces to the causing behaviour. Cognitive processes discussed on the agent level include attention, motivation, meaning and meta-cognition, as well as emergence/immergence, iterative learning and exploration/exploitation at the interface of individual agent and multi-agent systems. Characteristics of one-agent, two-agent and multi-agent systems are discussed and areas for future research highlighted.

scholarly journals Multi-agent systems for cooperative ITS

2021 ◽  
pp. 42-50
Author(s):  
Roman Dushkin
Keyword(s):  
Transport Systems ◽  
Transport Sector ◽  
Multi Agent Systems ◽  
Automated Vehicles ◽  
Intelligent Transport Systems ◽  
Individual Agent ◽  
Intelligent Transport ◽  
Agent Systems ◽  
Interacting Agents ◽  
Multi Agent

This article presents an original perspective upon the problem of creating intelligent transport systems in the conditions of using highly automated vehicles that freely move on the urban street-road networks. The author explores the issues of organizing a multi-agent system from such vehicles for solving the higher level tasks rather than by an individual agent (in this case – by a vehicle). Attention is also given to different types of interaction between the vehicles or vehicles and other agents. The examples of new tasks, in which the arrangement of such interaction would play a crucial role, are described. The scientific novelty is based on the application of particular methods and technologies of the multi-agent systems theory from the field of artificial intelligence to the creation of intelligent transport systems and organizing free-flow movement of highly automated vehicles. It is demonstrated the multi-agent systems are able to solve more complex tasks than separate agents or a group of non-interacting agents. This allows obtaining the emergent effects of the so-called swarm intelligence of the multiple interacting agents. This article may be valuable to everyone interested in the future of the transport sector.

scholarly journals Automated Verification of Social Laws for Continuous Time Multi-Robot Systems

2019 ◽  
Vol 33 ◽  
pp. 7683-7690 ◽  
Author(s):  
Ronen Nir ◽  
Erez Karpas
Keyword(s):  
Continuous Time ◽  
Automated Verification ◽  
Shared Environment ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Temporal Planning ◽  
Social Law ◽  
Robot Systems ◽  
Multi Agent ◽  
Multi Robot

Designing multi-agent systems, where several agents work in a shared environment, requires coordinating between the agents so they do not interfere with each other. One of the canonical approaches to coordinating agents is enacting a social law, which applies restrictions on agents’ available actions. A good social law prevents the agents from interfering with each other, while still allowing all of them to achieve their goals. Recent work took the first step towards reasoning about social laws using automated planning and showed how to verify if a given social law is robust, that is, allows all agents to achieve their goals regardless of what the other agents do. This work relied on a classical planning formalism, which assumed actions are instantaneous and some external scheduler chooses which agent acts next. However, this work is not directly applicable to multi-robot systems, because in the real world actions take time and the agents can act concurrently. In this paper, we show how the robustness of a social law in a continuous time setting can be verified through compilation to temporal planning. We demonstrate our work both theoretically and on real robots.

scholarly journals DELIBERATIVE EVOLUTION IN MULTI-AGENT SYSTEMS

2001 ◽  
Vol 11 (05) ◽  
pp. 559-581 ◽  
Author(s):  
F. M. T. BRAZIER ◽  
C. M. JONKER ◽  
J. TREUR ◽  
N. J. E. WIJNGAARDS
Keyword(s):  
Multi Agent Systems ◽  
Agent Architecture ◽  
Individual Agent ◽  
New Agents ◽  
Agent Model ◽  
Development Method ◽  
Agent Systems ◽  
Generic Agent ◽  
Multi Agent ◽  
Material Process

Evolution of automated systems, in particular evolution of automated agents based on agent deliberation, is the topic of this paper. Evolution is not a merely material process, it requires interaction within and between individuals, their environments and societies of agents. An architecture for an individual agent capable of (1) deliberation about the creation of new agents, and (2) (run-time) creation of a new agent on the basis of this, is presented. The agent architecture is based on an existing generic agent model, and includes explicit formal conceptual representations of both design structures of agents and (behavioural) properties of agents. The process of deliberation is based on an existing generic reasoning model of design. The architecture has been designed using the compositional development method DESIRE, and has been tested in a prototype implementation.

scholarly journals A survey of privacy in multi-agent systems

2013 ◽  
Vol 29 (3) ◽  
pp. 314-344 ◽  
Author(s):  
Jose M. Such ◽  
Agustín Espinosa ◽  
Ana García-Fornes
Keyword(s):  
Information Technologies ◽  
Autonomous Agents ◽  
Personal Information ◽  
Computer Applications ◽  
Future Research ◽  
The Internet ◽  
Multi Agent Systems ◽  
Personal Privacy ◽  
Agent Systems ◽  
Multi Agent

AbstractPrivacy has been a concern for humans long before the explosive growth of the Internet. The advances in information technologies have further increased these concerns. This is because the increasing power and sophistication of computer applications offers both tremendous opportunities for individuals, but also significant threats to personal privacy. Autonomous agents and multi-agent systems are examples of the level of sophistication of computer applications. Autonomous agents usually encapsulate personal information describing their principals, and therefore they play a crucial role in preserving privacy. Moreover, autonomous agents themselves can be used to increase the privacy of computer applications by taking advantage of the intrinsic features they provide, such as artificial intelligence, pro-activeness, autonomy, and the like. This article introduces the problem of preserving privacy in computer applications and its relation to autonomous agents and multi-agent systems. It also surveys privacy-related studies in the field of multi-agent systems and identifies open challenges to be addressed by future research.

scholarly journals JADE Multi-agent Middleware Applied to Contribute to Certificate Management of Students

2015 ◽  
Vol 16 (1) ◽  
pp. 176
Author(s):  
Fatiha Aityacine ◽  
Badr Hssina ◽  
Belaid Bouikhalene
Keyword(s):  
Personal Data ◽  
Distributed Artificial Intelligence ◽  
Shared Environment ◽  
Multi Agent Systems ◽  
Administrative Services ◽  
Agent Systems ◽  
Development Framework ◽  
Ability To Act ◽  
Multi Agent ◽  
Agent Development

In this article, we present a multi-agent approach that aims to design, modeling and implementation of an application "smart school". Indeed Several institutions adopt the computerized management of education to meet the needs of students using multi-agent systems. They have the ability to act simultaneously in a shared environment. The purpose of this approach is to automate some administrative services of education, based on the theory of distributed artificial intelligence (DAI) and multi-agent systems (MAS). This multi-agent application integrates entities called agents that cooperate and communicate them to perform specific tasks. Our system is based on the middleware JADE (Java Agent DEvelopment Framework) used for the implementation and agents management. This model based on multi-agent systems is tested on the personal data of an experiment conducted with the students of Sultan Moulay Slimane University in Beni Mellal.

scholarly journals Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

Processes ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 312 ◽  
Author(s):  
Manuel Herrera ◽  
Marco Pérez-Hernández ◽  
Ajith Kumar Parlikad ◽  
Joaquín Izquierdo
Keyword(s):  
Complex Networks ◽  
Systems Engineering ◽  
Future Research ◽  
Multi Agent Systems ◽  
Control Software ◽  
Research Directions ◽  
Supply Chain Networks ◽  
Agent Systems ◽  
Multi Agent ◽  
Future Research Directions

Systems engineering is an ubiquitous discipline of Engineering overlapping industrial, chemical, mechanical, manufacturing, control, software, electrical, and civil engineering. It provides tools for dealing with the complexity and dynamics related to the optimisation of physical, natural, and virtual systems management. This paper presents a review of how multi-agent systems and complex networks theory are brought together to address systems engineering and management problems. The review also encompasses current and future research directions both for theoretical fundamentals and applications in the industry. This is made by considering trends such as mesoscale, multiscale, and multilayer networks along with the state-of-art analysis on network dynamics and intelligent networks. Critical and smart infrastructure, manufacturing processes, and supply chain networks are instances of research topics for which this literature review is highly relevant.

Research on applications and problem of control of swarm intelligence and robotics

2020 ◽  
Vol 25 (1) ◽  
pp. 44-50
Author(s):  
Baraniuk A.S. ◽  
Keyword(s):  
Swarm Intelligence ◽  
Future Research ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Advantages And Disadvantages ◽  
Multi Agent ◽  
And Robotics

This article provides overview of the swarm intelligence and robotics fields, main characteristics of such systems provided, their advantages and disadvantages as well as differences from other multi-agent systems. Also, main fields of application for swarm systems with examples provided apart from short information on swarm optimizations. The problem of swarms’ control described and possible solutions for it such as algorithm replacement, parameters change, control through environment and leaders. Apart from that fields for possible future research noted.

scholarly journals A Review on Control and Optimisation of Multi-Agent Systems and Complex Networks for Systems Engineering

2020 ◽  
Author(s):  
Manuel Herrera ◽  
Marco Pérez-Hernández ◽  
Ajith Kumar Parlikad ◽  
Joaquín Izquierdo
Keyword(s):  
Complex Networks ◽  
Systems Engineering ◽  
Future Research ◽  
Multi Agent Systems ◽  
Control Software ◽  
Research Directions ◽  
Supply Chain Networks ◽  
Agent Systems ◽  
Multi Agent ◽  
Future Research Directions

Systems Engineering is an ubiquitous discipline of Engineering overlapping industrial, chemical, mechanical, manufacturing, control, software, electrical, and civil engineering. It provides tools for dealing with the complexity and dynamics related to the optimisation of physical, natural, and virtual systems management. This paper presents a review of how multi-agent systems and complex networks theory are brought together to address Systems Engineering and management problems. The review also encompasses current and future research directions both for theoretical fundamentals and applications in Industry. This is made by considering trends such as mesoscale, multiscale, and multilayer networks; along with the state-of-art analysis on network dynamics and intelligent networks. Critical and smart infrastructure, manufacturing processes, and supply chain networks are instances of research topics for which this literature review is highly relevant.

Final Remarks on The Investigation in Neural Trust in Multi-Agent Systems and Possible Future Directions

2017 ◽  
pp. 367-369
Author(s):  
Gehao Lu ◽  
Joan Lu
Keyword(s):  
Big Data ◽  
The Other ◽  
Future Research ◽  
Multi Agent Systems ◽  
Future Directions ◽  
Research Directions ◽  
Agent Systems ◽  
Multi Agent ◽  
Future Research Directions

This chapter provides the book's summary and conclusion on the neural trust in multi-agent systems. It also discusses possible future research directions in the field. The other chapters that make up this book collectively discuss ontology and big data.

Multi-Agent Systems for Urban Planning

2014 ◽  
pp. 29-56 ◽  
Author(s):  
Andrew T. Crooks ◽  
Amit Patel ◽  
Sarah Wise
Keyword(s):  
Urban Planning ◽  
Future Research ◽  
Multi Agent Systems ◽  
Land Use Patterns ◽  
Agent Systems ◽  
Use Patterns ◽  
Multi Agent ◽  
Future Research Directions ◽  
Social And Physical Environment ◽  
Changing Demographics

Cities provide homes for over half of the world's population, and this proportion is expected to increase throughout the next century. The growth of cities raises many questions and challenges for urban planning including which cities and regions are most likely to grow, what the pattern of urban growth will be, and how the existing infrastructure will cope with such growth. One way to explore these types of questions is through the use of multi-agent systems (MAS) that are capable of modeling how individuals interact and how structures emerge through such interactions, in terms of both the social and physical environment of cities. Within this chapter, the authors focus on how MAS can lead to insights into urban problems and aid urban planning from the bottom up. They review MAS models that explore the growth of cities and regions, models that explore land-use patterns resulting from such growth along with the rise of slums. Furthermore, the authors demonstrate how MAS models can be used to model transportation and the changing demographics of cities. Through these examples the authors also demonstrate how this style of modeling can give insights into such issues that cannot be gleamed from other modeling methodologies. The chapter concludes with challenges and future research directions of MAS models with respect to capturing the dynamics of human behavior in urban planning.

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