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 COMPOSITIONAL SPECIFICATION AND REUSE OF A GENERIC COOPERATIVE AGENT MODEL

2000 ◽  
Vol 09 (03) ◽  
pp. 171-207 ◽  
Author(s):  
FRANCES M. T. BRAZIER ◽  
FRANK CORNELISSEN ◽  
CATHOLIJN M. JONKER ◽  
JAN TREUR
Keyword(s):  
Engineering Design ◽  
Call Center ◽  
Generic Model ◽  
Multi Agent Systems ◽  
Collaborative Engineering ◽  
Agent Model ◽  
Development Method ◽  
Agent Systems ◽  
Multi Agent ◽  
Agent Cooperation

In this paper, one of the informally described models of agent cooperation (Jennings, 1995) has been used to develop and formally specify a generic model of a cooperative agent (GCAM). The compositional development method for multi-agent systems DESIRE supported the principled design of this model of cooperation. To illustrate reusability of the generic model, two application domains have been addressed: collaborative engineering design, and Call Center support.

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.

TOWARDS THE DEVELOPMENT OF AGENT-BASED ORGANIZATIONS THROUGH MDD

2013 ◽  
Vol 22 (02) ◽  
pp. 1350002 ◽  
Author(s):  
JORGE AGÜERO ◽  
CARLOS CARRASCOSA ◽  
MIGUEL REBOLLO ◽  
VICENTE JULIÁN
Keyword(s):  
Virtual Organization ◽  
Point Of View ◽  
Multi Agent Systems ◽  
Extensive Experience ◽  
Agent Based ◽  
Agent Systems ◽  
Model Driven ◽  
Generic Agent ◽  
Multi Agent ◽  
Model Driven Approach

Virtual Organizations are a mechanism where agents can demonstrate their social skills since they can work in a cooperative and collaborative way. Nonetheless, the development of organizations using Multi-Agent Systems (MAS) requires extensive experience in different methodologies and platforms. Model-Driven Development (MDD) is a technique for generating application code that is developed from basic models and meta-models using a variety of automatic transformations. This paper presents an approach to develop and deploy organization-oriented Multi-Agent Systems using a model-driven approach. Based on this idea, we introduce a relatively generic agent-based meta-model for a Virtual Organization, which was created by a comprehensive analysis of the organization-oriented methodologies used in MAS. Following the MDD approach, the concepts and relationships obtained were mapped into two different platforms available for MAS development, allowing the validation of our proposal. In this way, the resultant approach can generate Virtual Organization deployments from unified meta-models, facilitating the development process of agent-based software from the user point of view.

An adaptative agent architecture for holonic multi-agent systems

2008 ◽  
Vol 3 (1) ◽  
pp. 1-24 ◽  
Author(s):  
Vincent Hilaire ◽  
Abder Koukam ◽  
Sebastian Rodriguez
Keyword(s):  
Multi Agent Systems ◽  
Agent Architecture ◽  
Agent Systems ◽  
Multi Agent

scholarly journals From Algorithms to (Sub-)Symbolic Inferences in Multi-Agent Systems

2006 ◽  
Vol 1 (3) ◽  
pp. 5 ◽  
Author(s):  
Boldur E. Bărbat ◽  
Sorin C. Negulescu
Keyword(s):  
Information Technology ◽  
Software Engineering ◽  
Position Paper ◽  
Multi Agent Systems ◽  
Agent Architecture ◽  
Agent Systems ◽  
Symbolic Reasoning ◽  
Multi Agent ◽  
Intelligent Interaction ◽  
Future Work

Extending metaphorically the Moisilean idea of “nuanced-reasoning logic” and adapting it to the e-world age of Information Technology (IT), the paper aims at showing that new logics, already useful in modern software engineering, become necessary mainly for Multi-Agent Systems (MAS), despite obvious adversities. The first sections are typical for a position paper, defending such logics from an anthropocentric perspective. Through this sieve, Section 4 outlines the features asked for by the paradigm of computing as intelligent interaction, based on “nuances of nuanced-reasoning”, that should be reflected by agent logics. To keep the approach credible, Section 5 illustrates how quantifiable synergy can be reached - even in advanced challenging domains, such as stigmergic coordination - by injecting symbolic reasoning in systems based on sub-symbolic “emergent synthesis”. Since for future work too the preferred logics are doxastic, the conclusions could be structured in line with the well-known agent architecture: Beliefs, Desires, Intentions.

scholarly journals Fairness in multi-agent systems

2008 ◽  
Vol 23 (2) ◽  
pp. 153-180 ◽  
Author(s):  
STEVEN DE JONG ◽  
KARL TUYLS ◽  
KATJA VERBEECK
Keyword(s):  
Behavioral Economics ◽  
Computational Models ◽  
Computer Software ◽  
Multi Agent Systems ◽  
Agent Model ◽  
Agent Systems ◽  
Multi Agent ◽  
Classical Game ◽  
Descriptive Models ◽  
Classical Game Theory

AbstractMulti-agent systems are complex systems in which multiple autonomous entities, called agents, cooperate in order to achieve a common or personal goal. These entities may be computer software, robots, and also humans. In fact, many multi-agent systems are intended to operate in cooperation with or as a service for humans. Typically, multi-agent systems are designed assuming perfectly rational, self-interested agents, according to the principles of classical game theory. Recently, such strong assumptions have been relaxed in various ways. One such way is explicitly including principles derived from human behavior. For instance, research in the field of behavioral economics shows that humans are not purely self-interested. In addition, they strongly care aboutfairness. Therefore, multi-agent systems that fail to take fairness into account, may not be sufficiently aligned with human expectations and may not reach intended goals. In this paper, we present an overview of work in the area of fairness in multi-agent systems. More precisely, we first look at the classical agent model, that is, rational decision making. We then provide an outline of descriptive models of fairness, that is, models that explain how and why humans reach fair decisions. Then, we look at prescriptive, computational models for achieving fairness in adaptive multi-agent systems. We show that results obtained by these models are compatible with experimental and analytical results obtained in the field of behavioral economics.

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.

The Agent-Oriented Methodology MAS-CommonKADS

2011 ◽  
pp. 445-468 ◽  
Author(s):  
Carlos A. Iglesias ◽  
Mercedes Garijo
Keyword(s):  
Knowledge Engineering ◽  
Object Oriented ◽  
Design Model ◽  
Communication Model ◽  
Multi Agent Systems ◽  
Agent Model ◽  
Agent Systems ◽  
Coordination Model ◽  
Multi Agent ◽  
Generic Component

This chapter introduces the main concepts of the methodology MAS-CommonKADS that extends object-oriented and knowledge engineering techniques for the conceptualisation of multi-agent systems. MAS-CommonKADS defines a set of models (Agent Model, Task Model, Expertise Model, Coordination Model, Communication Model, Organisation Model, and Design Model) that together provide a model of the problem to be solved. Each of the components of the model is a generic component for the sake of reusability. Readers familiar with object-oriented analysis will find it easy to apply most of the techniques of MAS-CommonKADS in the development of multi-agent systems and will be introduced to the application of knowledge engineering techniques for specifying the knowledge of the agents.

ADE — AN ARCHITECTURE DEVELOPMENT ENVIRONMENT FOR VIRTUAL AND ROBOTIC AGENTS

2006 ◽  
Vol 15 (02) ◽  
pp. 251-285 ◽  
Author(s):  
VIRGIL ANDRONACHE ◽  
MATTHIAS SCHEUTZ
Keyword(s):  
Single Agent ◽  
Short Review ◽  
Multi Agent Systems ◽  
Agent Architecture ◽  
Development Environment ◽  
Agent Architectures ◽  
Agent Systems ◽  
Development Tools ◽  
Multi Agent ◽  
Architecture Development

In this paper we present the agent architecture development environment ADE, intended for the design, implementation, and testing of distributed agent architectures. After a short review of architecture development tools, we discuss ADE's unique features that place it in the intersection of multi-agent systems and development kits for single agent architectures. A detailed discussion of the general properties of ADE, its implementation philosophy, and its user interface is followed by examples from virtual and robotic domains that illustrate how ADE can be used for designing, implementing, testing, and running agent architectures.

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