MMASS approach to localization problems

Development of complicated products is a project of system engineeringIt involves extensive and complicated knowledge,design meth ods and auxiliary technologyVarious factors affect each otherSo,modern produ ct development is a typical group problem with distributed and dynamic features It is apparent superiority to solve this problem with a multiagent system re presenting various knowledge domainsDistributed artificial intelligence knowle dge being used,the multiagent collaborative design system concept and model ba sed on Internet environment are put forwardThe realizing method of product developing agents,interactive process among multiagents,and organiz ation and implementing of the design project of the multiagent collaborative d esign system are discussed in detailApplication examples are also presented

This paper presents a new modified Contract Net Protocol (CNP) for microgrid operation based on multiagent system. The CNP is a widely used protocol for interactions among distributed problem solving. The Contract Net Interaction Protocol of the Foundation for Intelligent Physical Agents (FIPA-CNIP) is a minor modification of the original CNP for multiagent system applications. In this paper, a modified CNP (MCNP) based on the FIPA-CNIP is proposed for more specialized interactions among agents for microgrid operation. A multiagent system is designed and constructed for microgrid operation. A microgrid operation based on the multiagent system is tested to check the functionality of the proposed MCNP.

Cooperative Distributed Problem Solving refers to a loosely coupled network of problem solvers working together to solve problems that may be beyond the capabilities of individual agents. Individual nodes may not have sufficient expertise, resources or information to solve the entire problem but every node is capable of solving some part. A group of autonomous agents in a Multi-agent System often work in a cooperative manner and form coalitions to obtain higher payoff than they can get individually. The coalition formation framework of Multi-agent Systems can be extended to a Distributed Problem Solving formulation where agents strive to achieve higher group benefits. This paper tries to demonstrate how a group of autonomous agents can solve a package delivery domain problem with reasonably good parameters in a purely distributed manner. Unlike traditional distributed problem solving experiments, here the agents are distributed across the problem domain and all control decisions are decentralized with no agent acting as a central dispatcher/ scheduler.

Constraint-based protocols for distributed problem solving

Understanding and modeling behavior of multi-agent systems is a central step for artificial intelligence. Here we present a deep generative model which captures behavior generating process of multi-agent systems, supports accurate predictions and inference, infers how agents interact in a complex system, as well as identifies agent groups and interaction types. Built upon advances in deep generative models and a novel attention mechanism, our model can learn interactions in highly heterogeneous systems with linear complexity in the number of agents. We apply this model to three multi-agent systems in different domains and evaluate performance on a diverse set of tasks including behavior prediction, interaction analysis and system identification. Experimental results demonstrate its ability to model multi-agent systems, yielding improved performance over competitive baselines. We also show the model can successfully identify agent groups and interaction types in these systems. Our model offers new opportunities to predict complex multi-agent behaviors and takes a step forward in understanding interactions in multi-agent systems.

Speech emotion recognition is a critical component for achieving natural human–robot interaction. The modulation-filtered cochleagram is a feature based on auditory modulation perception, which contains multi-dimensional spectral–temporal modulation representation. In this study, we propose an emotion recognition framework that utilizes a multi-level attention network to extract high-level emotional feature representations from the modulation-filtered cochleagram. Our approach utilizes channel-level attention and spatial-level attention modules to generate emotional saliency maps of channel and spatial feature representations, capturing significant emotional channel and feature space from the 3D convolution feature maps, respectively. Furthermore, we employ a temporal-level attention module to capture significant emotional regions from the concatenated feature sequence of the emotional saliency maps. Our experiments on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) dataset demonstrate that the modulation-filtered cochleagram significantly improves the prediction performance of categorical emotion compared to other evaluated features. Moreover, our emotion recognition framework achieves comparable unweighted accuracy of 71% in categorical emotion recognition by comparing with several existing approaches. In summary, our study demonstrates the effectiveness of the modulation-filtered cochleagram in speech emotion recognition, and our proposed multi-level attention framework provides a promising direction for future research in this field.

The application of intelligent systems for data interpretation and condition monitoring is an advancing field of research. In recent years autonomous intelligent agents and multi-agent systems have gained much attention within different real time applications. This paper introduces the novel idea of COMMAS (Condition Monitoring Multi-Agent System); a hierarchical decentralised multi-agent architecture developed for data interpretation and condition monitoring applications. It employs groups of different kinds of intelligent agents to cope with the variety of application functions by using distributed problem solving and different computational intelligence techniques. The design and functionality of the diversity of agents, along with the key issues of the multi-agent system as a whole are described. This paper demonstrates how agent technology overcomes problems associated with centralised approaches in condition monitoring, and illustrates the new opportunities agents can provide.

This work presents a new spring net approach for distributed problem solving in MAS, which is entirely different from the EN for TSP and can describe a variety of complicated social interactive behavior and autonomy of agents. The simulations of task allocation and resource assignment have shown the advantages of the proposed spring net approach for distributed problem solving in MAS.

During requirement specification process of software development activities, many existing systems or business process requirements have been captured using natural language or specialized tools such as UML. However, the capturing of informal requirements into formalized properties has not been taken into attention by software developers due to time and budget constraints. It is critical for the informally captured requirements to be formally specified in order to perform system verification. Formal verification checks whether a system model meets the formal specifications while validation checks whether the developed system fulfills its intended purpose. Therefore, in this paper, we present our studies of formal verification of multi agent system using model checking approach. We have utilized model checking tool in order to execute the formal verification procedures based on a particular basic theory to verify certain kind of properties of requirement specifications. We show an example of how model checking tool could support the verification of Universiti Teknologi Malaysia (UTM) multi agent online application system and conclude that the propose model checking approach will benefit multi agent system.

We’re pleased, indeed relieved, to announce the roughly simultaneous publication of our two books. The first, which was six years in the making, is a general introduction to multiagent systems. In the spirit of the times it has deep coverage of game theory, but is not limited to it, and includes extensive material on distributed problem solving and logical approaches. It is intended to support an introductory yet rigorous course on multiagent systems (indeed, it can support several different such courses). We hope you enjoy it, sorry for the remaining typos and errors, and please give us feedback. Importantly, in the home page for the book (www.MASfoundations.org) we’ll include a page with instructional resources, and we invite you to share whatever presentations, lecture notes, problems (with solutions...), software, or any other resource you find relevant to teaching from this book. Of course any contribution you make will remain attributed to you (you can decide whether this is a promise or a threat...).

Along with distributed problem solving and parallel artificial intelligence, agents and multi-agent systems collectively form one of the three broad areas which fall under distributed artificial intelligence (DAI). In this paper, we explore and review the general concepts of software agent technology; definitions, types, architectures, agent communication, multiagent systems and agent development systems.

Distributed Problem Solving using Evolutionary Learning in Multi-Agent Systems

This research is concerned with the design, development and implementation of a unique reaction-based multi-agent architecture (REAGERE) to integrate and control a manufacturing domain, by combining concepts from distributed problem solving and multi-agent systems. This architecture represents an emerging concept of reifying the parts, equipment, and software packages of the domain as individual agent entities. This research also improves on earlier top- down automated manufacturing systems, that suffered from lack of flexibility, upgradability, overhead difficulties, and performance problems when presented with the uncertainty and dynamics of modern competitive environments. The versatility of the domain is enhanced with the independent development of the agents and the object-oriented events that permit the agents to communicate through the underlying blackboard architecture BB1. This bottom-up concept permits the architecture's integration to rely on the agents' interactions and their perceptions of the current environmental problem(s). Hence the control and coordination of the architecture are adaptable to the agents' reactions to dynamic situations. REAGERE was applied to a simulated predefined automated manufacturing domain for the purpose of controlling and coordinating the internal processes of this domain.

In the area of biotechnology, a micromanipulation is widely used for such purposes as operating on genes and transferring biological materials into cells. For the some experiments, such as biochemical experiment, a large number of cells have to be manipulated in a short time. We have developed an automatic micromanipulation system under the stereoscopic microscope. Micromanipulation system carries out various processes, such as detection of the target, the detection of the needle head, and motor control. By sharing these processes with several computers, the micromanipulation can be performed at high speed. As a result, computer cooperation becomes very important. In this paper, we propose a multi agent micromanipulation system. At first, we developed a multi agent system, which performs image processing, motor control, and management of the micromanipulation processes. Secondarily, we proposed to operate computers cooperative. We use a computer as a single agent. And several computers are connected to a local area network. The multi agent micromanipulation system performed the micromanipulation at a realistic rate through cooperation of multi agents.

This paper, a Multi-Agent System (MAS) of a cooperative energy management system is conceptualized. The idea is based on the assumption that of most of the establishments in the area have an alternative (renewable) energy generation source, proceel as alterto nergy etc and the energy demand of various establishments is independent of each other and reaches peak at different times. The proposed multi agent system consits of several Load Distribution Agents (LDA) that try to by full the demand communicating with each unit of their. The paper demonstrates the use of a multi agent system to control the smart grid in a simulated environment. Simulation results show that the proposed agent multi system can perform the operation of the smart grid efficiently.