Aspects of Openness in Multi-Agent Systems

In the distributed multi-agent systems discussed in this chapter, heterogeneous autonomous agents interoperate in order to achieve their goals. In such environments, agents can be embedded in diverse contexts and interact with agents of various types and behaviours. Mechanisms are needed for coordinating these multi-agent interactions, and so far they have included tools for the support of conversation protocols and tools for the establishment and management of agent groups and electronic institutions. In this chapter, we explore the necessity of dealing with openness in multi-agent systems and its relation with the agent’s autonomy. We stress the importance to build coordination mechanisms capable of managing complex agent societies composed by autonomous agents and introduce our institutional environment approach, which includes the use of commitments and normative spaces. It is based on a metaphor in which agents may join an open system at any time, but they must obey regulations in order to maintain a suitable reputation, that reflects its degree of cooperation with other agents in the group, and make them a more desired partner for others. Coloured Petri Nets are used to formalize a workflow in the institutional environment defining a normative space that guides the agents during interactions in the conversation space.

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An Intelligent Agents and Multi-Agent Systems Course Involving NetLogo

Multi-Agent Systems for Education and Interactive Entertainment ◽

10.4018/978-1-60960-080-8.ch002 ◽

2011 ◽

pp. 26-50 ◽

Cited By ~ 1

Author(s):

Ilias Sakellariou ◽

Petros Kefalas ◽

Ioanna Stamatopoulou

Keyword(s):

Learning Curve ◽

Intelligent Agents ◽

Multi Agent Systems ◽

Agent Based ◽

Agent Systems ◽

Agent Societies ◽

Agent Communication Language ◽

Complex Agent ◽

Language Communication ◽

Multi Agent

In the context of an Agent and Multi-Agent Systems course, exposing students to problems and issues related to agent-based programming allows them to understand at a deeper level the otherwise theoretical aspects involved in the design of a multi-agent system (MAS). Indeed, educators have reported a variety of environments and techniques they use in order to increase active learning. The authors argue that NetLogo presents an excellent platform for the task, since it provides, among other interesting features, a small learning curve and an easy to setup visualization environment. This chapter records their experience in teaching a MAS course, using NetLogo as the vehicle for practical coursework. In addition, two extra NetLogo libraries that were provided to students are described, one for BDI-like agents (Belief-Desire-Intention, i.e. goal-oriented agents) and one for ACL-like (Agent Communication Language) communication, which were specifically developed for allowing students to implement more complex agent societies than the original NetLogo platform allowed.

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A Programming Approach to Collective Autonomy

Journal of Sensor and Actuator Networks ◽

10.3390/jsan10020027 ◽

2021 ◽

Vol 10 (2) ◽

pp. 27

Author(s):

Roberto Casadei ◽

Gianluca Aguzzi ◽

Mirko Viroli

Keyword(s):

Autonomous Vehicles ◽

Autonomic Computing ◽

Autonomous Agents ◽

Programming Approach ◽

Multi Agent Systems ◽

Agent Systems ◽

Multi Agent ◽

Simulated Case ◽

Complex Settings

Research and technology developments on autonomous agents and autonomic computing promote a vision of artificial systems that are able to resiliently manage themselves and autonomously deal with issues at runtime in dynamic environments. Indeed, autonomy can be leveraged to unburden humans from mundane tasks (cf. driving and autonomous vehicles), from the risk of operating in unknown or perilous environments (cf. rescue scenarios), or to support timely decision-making in complex settings (cf. data-centre operations). Beyond the results that individual autonomous agents can carry out, a further opportunity lies in the collaboration of multiple agents or robots. Emerging macro-paradigms provide an approach to programming whole collectives towards global goals. Aggregate computing is one such paradigm, formally grounded in a calculus of computational fields enabling functional composition of collective behaviours that could be proved, under certain technical conditions, to be self-stabilising. In this work, we address the concept of collective autonomy, i.e., the form of autonomy that applies at the level of a group of individuals. As a contribution, we define an agent control architecture for aggregate multi-agent systems, discuss how the aggregate computing framework relates to both individual and collective autonomy, and show how it can be used to program collective autonomous behaviour. We exemplify the concepts through a simulated case study, and outline a research roadmap towards reliable aggregate autonomy.

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Group Behavior Learning in Multi-Agent Systems Based on Social Interaction Among Agents

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2011.p0896 ◽

2011 ◽

Vol 15 (7) ◽

pp. 896-903 ◽

Cited By ~ 2

Author(s):

Kun Zhang ◽

◽

Yoichiro Maeda ◽

Yasutake Takahashi ◽

Keyword(s):

Social Interaction ◽

Autonomous Agents ◽

Cooperative Behavior ◽

Group Behavior ◽

Learning Ability ◽

Human Beings ◽

Multi Agent Systems ◽

Agent Systems ◽

Multi Agent ◽

State Of The Environment

Research on multi-agent systems, in which autonomous agents are able to learn cooperative behavior, has been the subject of rising expectations in recent years. We have aimed at the group behavior generation of the multi-agents who have high levels of autonomous learning ability, like that of human beings, through social interaction between agents to acquire cooperative behavior. The sharing of environment states can improve cooperative ability, and the changing state of the environment in the information shared by agents will improve agents’ cooperative ability. On this basis, we use reward redistribution among agents to reinforce group behavior, and we propose a method of constructing a multi-agent system with an autonomous group creation ability. This is able to strengthen the cooperative behavior of the group as social agents.

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Bayesian Agencies in Control

Computational Intelligence in Control ◽

10.4018/978-1-59140-037-0.ch010 ◽

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.”

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Engineering Software Systems with Social-Driven Templates

Methodological Advancements in Intelligent Information Technologies - Advances in Intelligent Information Technologies ◽

10.4018/978-1-60566-970-0.ch001 ◽

2010 ◽

pp. 1-29

Author(s):

Manuel Kolp ◽

Yves Wautelet ◽

Sodany Kiv ◽

Vi Tran

Keyword(s):

Architectural Design ◽

Autonomous Agents ◽

Knowledge Bases ◽

Software Systems ◽

Multi Agent Systems ◽

Business Systems ◽

Agent Systems ◽

Social Patterns ◽

Engineering Software ◽

Multi Agent

Multi-Agent Systems (MAS) architectures are gaining popularity over traditional ones for building open, distributed, and evolving software required by today’s corporate IT applications such as e-business systems, Web services or enterprise knowledge bases. Since the fundamental concepts of multi-agent systems are social and intentional rather than object, functional, or implementation-oriented, the design of MAS architectures can be eased by using social-driven templates. They are detailed agent-oriented design idioms to describe MAS architectures as composed of autonomous agents that interact and coordinate to achieve their intentions, like actors in human organizations. This paper presents social patterns, as well as organizational styles, and focuses on a framework aimed to gain insight into these templates. The framework can be integrated into agent-oriented software engineering methodologies used to build MAS. We consider the Broker social pattern to illustrate the framework. The mapping from system architectural design (through organizational architectural styles), to system detailed design (through social patterns), is overviewed with a data integration case study. The automation of patterns design is also overviewed.

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I Have a Robot, and I’m Not Afraid to Use It!

AI Magazine ◽

10.1609/aimag.v33i3.2422 ◽

2012 ◽

Vol 33 (3) ◽

pp. 66

Author(s):

Gal A. Kaminka

Keyword(s):

Autonomous Agents ◽

Multi Agent Systems ◽

Agent Systems ◽

Multi Agent

Robots (and roboticists) increasingly appear at the Autonomous Agents and Multi-Agent Systems (AAMAS) conferences because the community uses robots both to inspire AAMAS research as well as to conduct it. In this article, I submit that the growing success of robotics at AAMAS is due not only to the nurturing efforts of the AAMAS community, but mainly to the increasing recognition of an important, deeper, truth: it is scientifically useful to roboticists and agent researchers to think of robots as agents.

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Intelligent Multi-Agent Systems

Encyclopedia of Information Communication Technology ◽

10.4018/978-1-59904-845-1.ch061 ◽

2009 ◽

pp. 464-469

Author(s):

Uros Krcadinac ◽

Milan Stankovic ◽

Vitomir Kovanovic ◽

Jelena Jovanovic

Keyword(s):

Autonomous Agents ◽

Research Community ◽

Object Oriented Programming ◽

Multi Agent Systems ◽

Autonomous Action ◽

Agent Systems ◽

Intelligent Software ◽

Intelligent Software Agents ◽

Object Based ◽

Multi Agent

Since the AAAI (http://www.aaai.org) Spring Symposium in 1994, intelligent software agents and agentbased systems became one of the most significant and exciting areas of research and development (R&D) that inspired many scientific and commercial projects. In a nutshell, an agent is a computer program that is capable of performing a flexible, autonomous action in typically dynamic and unpredictable domains (Luck, McBurney, Shehory, & Willmott, 2005). Agents emerged as a response of the IT research community to the new data-processing requirements that traditional computing models and paradigms were increasingly incapable to deal with (e.g., the huge and ever-increasing quantities of available data). Agent-oriented R&D has its roots in different disciplines. Undoubtedly, the main contribution to the field of autonomous agents came from artificial intelligence (AI) which is focused on building intelligent artifacts; and if these artifacts sense and act in some environment, then they can be considered agents (Russell & Norvig, 1995). Also, object-oriented programming (Booch, 2004), concurrent object-based systems (Agha, Wegner, & Yonezawa, 1993), and human-computer interaction (Maes, 1994) are fields that have constantly driven forward the agent R&D in the last few decades.

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Social Structure Based Design Patterns for Agent-Oriented Software Engineering

Software Applications ◽

10.4018/978-1-60566-060-8.ch049 ◽

2009 ◽

pp. 773-796

Author(s):

Manuel Kolp ◽

Stéphane Faulkner ◽

Yves Wautelet

Keyword(s):

Software Engineering ◽

Design Patterns ◽

Autonomous Agents ◽

Knowledge Bases ◽

Multi Agent Systems ◽

Business Systems ◽

Agent Systems ◽

Social Patterns ◽

Agent Oriented Software Engineering ◽

Multi Agent

Multi-agent systems (MAS) architectures are gaining popularity over traditional ones for building open, distributed, and evolving software required by today’s corporate IT applications such as e-business systems, Web services, or enterprise knowledge bases. Since the fundamental concepts of multi-agent systems are social and intentional rather than object, functional, or implementationoriented, the design of MAS architectures can be eased by using social patterns. They are detailed agent-oriented design idioms to describe MAS architectures composed of autonomous agents that interact and coordinate to achieve their intentions, like actors in human organizations. This article presents social patterns and focuses on a framework aimed to gain insight into these patterns. The framework can be integrated into agent-oriented software engineering methodologies used to build MAS. We consider the Broker social pattern to illustrate the framework. An overview of the mapping from system architectural design (through organizational architectural styles), to system detailed design (through social patterns), is presented with a data integration case study. The automation of creating design patterns is also discussed.

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