scholarly journals TAO+: Extending the Conceptual Framework TAO to Support Internal Agent Architectures in Normative Multi-Agent Systems

2013 ◽  
Vol 292 ◽  
pp. 57-69 ◽  
Author(s):  
Emmanuel Sávio Silva Freire ◽  
Enyo José Tavares Gonçalves ◽  
Mariela Inés Cortés ◽  
Yrleyjânder Salmito Lopes ◽  
Marcius Gomes Brandão
Keyword(s):  
Conceptual Framework ◽  
Multi Agent Systems ◽  
Agent Architectures ◽  
Agent Systems ◽  
Multi Agent ◽  
Internal Agent

Bayesian Agencies in Control

2011 ◽  
pp. 168-181
Author(s):  
Anet Potgieter ◽  
Judith Bishop
Keyword(s):  
Bayesian Networks ◽  
Autonomous Agents ◽  
Probabilistic Reasoning ◽  
Multi Agent Systems ◽  
Agent Architectures ◽  
Analysis And Design ◽  
Agent Systems ◽  
Interaction Protocols ◽  
Multi Agent ◽  
The Individual

Most agent architectures implement autonomous agents that use extensive interaction protocols and social laws to control interactions in order to ensure that the correct behaviors result during run-time. These agents, organized into multi-agent systems in which all agents adhere to predefined interaction protocols, are well suited to the analysis, design and implementation of complex systems in environments where it is possible to predict interactions during the analysis and design phases. In these multi-agent systems, intelligence resides in individual autonomous agents, rather than in the collective behavior of the individual agents. These agents are commonly referred to as “next-generation” or intelligent components, which are difficult to implement using current component-based architectures. In most distributed environments, such as the Internet, it is not possible to predict interactions during analysis and design. For a complex system to be able to adapt in such an uncertain and non-deterministic environment, we propose the use of agencies, consisting of simple agents, which use probabilistic reasoning to adapt to their environment. Our agents collectively implement distributed Bayesian networks, used by the agencies to control behaviors in response to environmental states. Each agency is responsible for one or more behaviors, and the agencies are structured into heterarchies according to the topology of the underlying Bayesian networks. We refer to our agents and agencies as “Bayesian agents” and “Bayesian agencies.”

MAS-ML 2.0: Supporting the modelling of multi-agent systems with different agent architectures

2015 ◽  
Vol 108 ◽  
pp. 77-109 ◽  
Author(s):  
Enyo José Tavares Gonçalves ◽  
Mariela I. Cortés ◽  
Gustavo Augusto Lima Campos ◽  
Yrleyjander S. Lopes ◽  
Emmanuel S.S. Freire ◽  
...  
Keyword(s):  
Multi Agent Systems ◽  
Agent Architectures ◽  
Agent Systems ◽  
Multi Agent

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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