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Cooperative Robotic Systems in Agriculture
Published in Dan Zhang, Bin Wei, Robotics and Mechatronics for Agriculture, 2017
Granted that an advanced RA has better performance and is more robust than a simple RA, however, researchers have ratiocinated that a cooperative team of simple RAs has better accuracy in localization, navigation, path planning, and optimal performance. The combination of two or more interacting intelligent agents is referred to as a multi-agent system. A multi-agent system is a swarm intelligence system having a smart team of agents effectively interacting with each other to complete common tasks. Multi-agent systems have the ability to resolve complicated tasks which are difficult or impossible for a single agent to accomplish (Barca and Sekercioglu, 2013). Multi-agent systems in cooperative environments allow more sophisticated agents to share their capabilities with other agents which have limited capabilities (Bailey et al., 2011). Multi-agent systems have the robustness of a single agent; thus, they have been applied successfully to agriculture and manufacturing applications.
Intelligent agents and knowledge dissemination
Published in Jay Liebowitz, Knowledge Management: Learning from Knowledge Engineering, 2001
Furthermore, Sycara (1998) discusses multi-agent systems and the challenges ahead, namely: (1) how to decompose problems and allocate tasks to individual agents, (2) how to coordinate agent control and communications, (3) how to make multiple agents act in a coherent manner, (4) how to make individual agents reason about other agents and the state of coordination, (5) how to reconcile conflicting goals between coordinating agents, and (6) how to engineer practical multi-agent systems. In addition to this list of challenges, many researchers are looking at only autonomous agents, but in many situations, the integration of human collaboration with agent-based interaction will be crucial. Researchers such as Volksen et al. (1996) have developed Cooperation-Ware as a framework for human-agent collaboration.
Privacy Concerns and Trust Issues
Published in Kris MY Law, Andrew WH Ip, Brij B Gupta, Shuang Geng, Managing IoT and Mobile Technologies with Innovation, Trust, and Sustainable Computing, 2021
Brij B Gupta, Pooja Chaudhary, Dragan Peraković, Konstantinos Psannis
Integration of artificial intelligence and Big Data into IoT has revolutionized the lives of human. It has begun an era where everything acts smartly, i.e. system will perform according to users’ needs. IoT devices collect data of the surrounding things and the useful patterns are observed by applying Big Data analysis techniques which, in turn, helps in learning user’s habits, leading to systematic utilization of devices [16]. To make IoT system more comfortable for human, a multi-agent system has been employed so that IoT devices can act smartly without human intervention. Multi-agent system is the advanced version of centralized system or single-agent system where multiple agents are loosely coupled with each other to complete a common task [17].
Recent advances on cooperative control of heterogeneous multi-agent systems subject to constraints: a survey
Published in Systems Science & Control Engineering, 2022
Guangyan Bao, Lifeng Ma, Xiaojian Yi
In the past few decades, the application of multi-agent systems has multiplied in various areas ranging from the manufacturing industry and electrical engineering to military applications and modern medical industry. These beneficial applications in human community are partly attributed to the extensive and profound academical researches. In practice, agents can be diverse and complicated in the real world. However, in the existing literature, agents in MASs are usually assumed to have homogeneous dynamics which is simple but can rapidly promote the development of this field undeniably. Consequently, these proposed methods encounter great difficulties coping with the heterogeneous MASs which are actually more common than their homogeneous counterparts in the real-world. As a result, we can see from recent researches that more and more scholars have devoted to seeking solutions for better control of the heterogeneous MASs by improving existing algorithms or inventing new ones.
A systematic mapping study on agent mining
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2022
Emmanuelle Grislin-Le Strugeon, Kathia Marcal de Oliveira, Marie Thilliez, Dorian Petit
The field of multi-agent, or multiagent, research started with studies in distributed Artificial Intelligence (AI) in the 80s and has grown rapidly since the second part of the 90s; see, for instance, the research by Jennings et al. (1998), Maes (1994), and Nwana (1996). Multi-agent systems are sets of autonomous entities, called agents, interacting in a shared environment (Wooldridge, 2009). The word ‘intelligent’ was often used in the 90s to describe agents, underlining the filiation with AI, but is now rarely used to compose the term ‘intelligent agent’.1 The word ‘software’ is added when there is a need to distinguish pure software agents from robotic (robot-embodied) or human agents, but the full term ‘software agent’ is rarely used in other cases.
Distributed Probabilistic Fuzzy Rule Mining for Clinical Decision Making
Published in Fuzzy Information and Engineering, 2021
Samane Sharif, Mohammad-R. Akbarzadeh-T
Multi-agent systems are one of the most significant paradigms in distributed artificial intelligence. A multi-agent system consists of several autonomous agents that can cooperate and sometimes compete to achieve specific goals. This autonomy, reactivity, and social ability of agents can lead to efficient decision-making. In this section, a new multi-agent approach is proposed for extracting probabilistic fuzzy rules from distributed data, which can be widely used in classification and decision-making problems. As a case study, this paper employs the proposed method for designing a clinical decision support system. In this approach, each agent has access to a part of the whole data and extracts its local knowledge from this local data through a self-organized process. Here, data are randomly and evenly distributed among agents. The structure of the proposed system is shown in Figure 1. All agents have a similar internal structure. Each agent uses a simple learning procedure to partition the input space according to its local data. During the learning process, each agent creates a local rule base from its local data. The confidence level of an agent represents the correctness of its acquired knowledge.