Nonuniform Deployment of Autonomous Agents in Harbor-Like Environments

2014 ◽  
Vol 02 (04) ◽  
pp. 377-389 ◽  
Author(s):  
Suruz Miah ◽  
Bao Nguyen ◽  
François-Alex Bourque ◽  
Davide Spinello
Keyword(s):  
Autonomous Agents ◽  
Optimal Trajectories ◽  
Risk Map ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Deployment Strategy ◽  
Area Of Interest ◽  
Multi Agent ◽  
Optimal Deployment ◽  
Theoretical Results

We propose a nonuniform deployment strategy of a group of homogeneous autonomous agents in harbor-like environments. High value units berthed in the area need to be secured against external attacks. Defenders deployed in the area are expected to monitor, intercept, engage, and neutralize threats. In the framework of decentralized coordinated multi-agent systems, we model and simulate the optimal deployment of a group of mobile autonomous agents that accounts for a risk map of the area and the optimal trajectories that minimize the energy consumed to intercept a threat in a given area of interest. Theoretical results are numerically illustrated through simulations in a realistic harbor protection scenario.

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.

Formation-containment control of sampled-data second-order multi-agent systems with sampling delay

2018 ◽  
Vol 40 (16) ◽  
pp. 4369-4381 ◽  
Author(s):  
Baojie Zheng ◽  
Xiaowu Mu
Keyword(s):  
Matrix Theory ◽  
Sufficient Conditions ◽  
Second Order ◽  
Control Problems ◽  
Sampled Data ◽  
Multi Agent Systems ◽  
Containment Control ◽  
Agent Systems ◽  
Multi Agent ◽  
Theoretical Results

The formation-containment control problems of sampled-data second-order multi-agent systems with sampling delay are studied. In this paper, we assume that there exist interactions among leaders and that the leader’s neighbours are only leaders. Firstly, two different control protocols with sampling delay are presented for followers and leaders, respectively. Then, by utilizing the algebraic graph theory and matrix theory, several sufficient conditions are obtained to ensure that the leaders achieve a desired formation and that the states of the followers converge to the convex hull formed by the states of the leaders, i.e. the multi-agent systems achieve formation containment. Furthermore, an explicit expression of the formation position function is derived for each leader. An algorithm is provided to design the gain parameters in the protocols. Finally, a numerical example is given to illustrate the effectiveness of the obtained theoretical results.

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.

Kernel Design and Distributed, Self-Triggered Control for Coordination of Autonomous Multi-Agent Configurations

Robotica ◽  
2018 ◽  
Vol 36 (7) ◽  
pp. 1077-1097 ◽  
Author(s):  
Levi DeVries ◽  
Aaron Sims ◽  
Michael D. M. Kutzer
Keyword(s):  
Experimental Validation ◽  
Graph Transformation ◽  
Multi Agent Systems ◽  
Graph Topology ◽  
Agent Communication ◽  
Agent Systems ◽  
Cloud Server ◽  
Multi Agent ◽  
Kernel Design ◽  
Theoretical Results

SUMMARYAutonomous multi-agent systems show promise in countless applications, but can be hindered in environments where inter-agent communication is limited. In such cases, this paper considers a scenario where agents communicate intermittently through a cloud server. We derive a graph transformation mapping the kernel of a graph's Laplacian to a desired configuration vector while retaining graph topology characteristics. The transformation facilitates derivation of a self-triggered controller driving agents to prescribed configurations while regulating instances of inter-agent communication. Experimental validation of the theoretical results shows the self-triggered approach drives agents to a desired configuration using fewer control updates than traditional periodic implementations.

Sampled intermittent control for consensus of multi-agent systems with variable activated intervals

2017 ◽  
Vol 40 (5) ◽  
pp. 1521-1528
Author(s):  
Yan Wang ◽  
Hong Zhou ◽  
Zhi-Wei Liu ◽  
Wenshan Hu ◽  
Wei Wang
Keyword(s):  
Sufficient Conditions ◽  
Nonnegative Matrix ◽  
Switching Topology ◽  
Intermittent Control ◽  
Multi Agent Systems ◽  
Time Intervals ◽  
Agent Systems ◽  
Multi Agent ◽  
Velocity Information ◽  
Theoretical Results

In this paper, a new kind of intermittent control is proposed to study consensus problems of multi-agent systems with second-order dynamics. In particular, we consider the case that the information transmission occurs at sampling instants and the velocity information is not available for feedback. The proposed control only regulates the velocity of agents in a given sequence of disconnected time intervals, called activated intervals, after sampling instants. Remarkably, both the sampling and activated intervals are not required to be identical. By adopting algebraic graph theory and nonnegative matrix, some sufficient conditions are obtained for guaranteeing the consensus of the multi-agent systems under the switching topology. Finally, the numerical examples are included to illustrate the theoretical results.

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