Application of a System-Wide Trust Strategy when Supervising Multiple Autonomous Agents

2016 ◽  
Vol 60 (1) ◽  
pp. 133-137 ◽  
Author(s):  
James C. Walliser ◽  
Ewart J. de Visser ◽  
Tyler H. Shaw
Keyword(s):  
Supervisory Control ◽  
System Performance ◽  
Autonomous Agents ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Performance Information ◽  
Agent Systems ◽  
Multi Agent ◽  
And Performance ◽  
And Training

When interacting with complex systems, the manner in which an operator trusts automation influences system performance. Recent studies have demonstrated that people tend to apply trust broadly rather than exhibiting specific trust in each component of the system in a calibrated manner (e.g. Keller & Rice, 2010). While this System–Wide Trust effect has been established for basic situations such as judging gauges, it has not been studied in realistic settings such as collaboration with autonomous agents in a multi-agent system. This study utilized a multiple UAV control simulation, to explore how people apply trust in multi autonomous agents in a supervisory control setting. Participants interacted with four UAVs that utilized automated target recognition (ATR) systems to identify targets as enemy or friendly. When one of the autonomous agents was inaccurate and performance information was provided, participants were 1) less accurate, 2) more likely to verify the ATR’s determination, 3) spent more time verifying images, and 4) rated the other systems as less trustworthy even though they were 100% correct. These findings support previous work that demonstrated the prevalence of system-wide trust and expand the conditions in which system-wide trust strategies are applied. This work suggests that multi-agent systems should provide carefully designed cues and training to mitigate the system-wide trust effect.

scholarly journals A Programming Approach to Collective Autonomy

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.

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

2011 ◽  
Vol 15 (7) ◽  
pp. 896-903 ◽  
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.

Evolutionary Computation as a Paradigm for Engineering Emergent Behaviour in Multi-Agent Systems

2011 ◽  
pp. 118-136 ◽  
Author(s):  
Robert E. Smith ◽  
Claudio Bonacina
Keyword(s):  
Evolutionary Computation ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent System ◽  
Future Directions ◽  
Agent Based ◽  
Agent Systems ◽  
Emergent Behaviour ◽  
Shaping System ◽  
Multi Agent

In the multi-agent system (MAS) context, the theories and practices of evolutionary computation (EC) have new implications, particularly with regard to engineering and shaping system behaviors. Thus, it is important that we consider the embodiment of EC in “real” agents, that is, agents that involve the real restrictions of time and space within MASs. In this chapter, we address these issues in three ways. First, we relate the foundations of EC theory to MAS and consider how general interactions among agents fit within this theory. Second, we introduce a platform independent agent system to assure that our EC methods work within the generic, but realistic, constraints of agents. Finally, we introduce an agent-based system of EC objects. Concluding sections discuss implications and future directions.

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.”

Engineering Software Systems with Social-Driven Templates

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.

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

AI Magazine ◽  
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.

Intelligent Multi-Agent Systems

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.

Social Structure Based Design Patterns for Agent-Oriented Software Engineering

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.

scholarly journals Quantum Diffie–Hellman Extended to Dynamic Quantum Group Key Agreement for e-Healthcare Multi-Agent Systems in Smart Cities

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3940
Author(s):  
Vankamamidi S. Naresh ◽  
Moustafa M. Nasralla ◽  
Sivaranjani Reddi ◽  
Iván García-Magariño
Keyword(s):  
System Architecture ◽  
Key Agreement ◽  
Unitary Operation ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Group Key ◽  
Group Key Agreement ◽  
Agent Systems ◽  
Diffie Hellman ◽  
Multi Agent

Multi-Agent Systems can support e-Healthcare applications for improving quality of life of citizens. In this direction, we propose a healthcare system architecture named smart healthcare city. First, we divide a given city into various zones and then we propose a zonal level three-layered system architecture. Further, for effectiveness we introduce a Multi-Agent System (MAS) in this three-layered architecture. Protecting sensitive health information of citizens is a major security concern. Group key agreement (GKA) is the corner stone for securely sharing the healthcare data among the healthcare stakeholders of the city. For establishing GKA, many efficient cryptosystems are available in the classical field. However, they are yet dependent on the supposition that some computational problems are infeasible. In light of quantum mechanics, a new field emerges to share a secret key among two or more members. The unbreakable and highly secure features of key agreement based on fundamental laws of physics allow us to propose a Quantum GKA (QGKA) technique based on renowned Quantum Diffie–Hellman (QDH). In this, a node acts as a Group Controller (GC) and forms 2-party groups with remaining nodes, establishing a QDH-style shared key per each two-party. It then joins these keys into a single group key by means of a XOR-operation, acting as a usual group node. Furthermore, we extend the QGKA to Dynamic QGKA (DQGKA) by adding join and leave protocol. Our protocol performance was compared with existing QGKA protocols in terms of Qubit efficiency (QE), unitary operation (UO), unitary operation efficiency (UOE), key consistency check (KCC), security against participants attack (SAP) and satisfactory results were obtained. The security analysis of the proposed technique is based on unconditional security of QDH. Moreover, it is secured against internal and external attack. In this way, e-healthcare Multi-Agent System can be robust against future quantum-based attacks.

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