Multi Agent Systems Applications

2003

As agent-oriented paradigm is reaching a significant level of acceptance by software developers, there is a lack of integrated high-level abstraction tools for the design and development of agent-based applications. In an effort to mitigate this deficiency, we introduce Agent Academy, an integrated development framework, implemented itself as a multi-agent system, that supports, in a single tool, the design of agent behaviours and reusable agent types, the definition of ontologies, and the instantiation of single agents or multi-agent communities. In addition to these characteristics, our framework goes deeper into agents, by implementing a mechanism for embedding rule-based reasoning into them. We call this procedure “agent training” and it is realized by the application of AI techniques for knowledge discovery on application-specific data, which may be available to the agent developer. In this respect, Agent Academy provides an easy-to-use facility that encourages the substitution of existing, traditionally developed applications by new ones, which follow the agent-orientation paradigm.

Multi-agent systems is a subfield of Distributed Artificial Intelligence that has experienced rapid growth because of the flexibility and the intelligence available solve distributed problems. In this chapter, a brief survey of multi-agent systems has been presented. These encompass different attributes such as architecture, communication, coordination strategies, decision making and learning abilities. The goal of this chapter is to provide a quick reference to assist in the design of multi-agent systems and to highlight the merit and demerits of the existing methods.

IFIP International Federation for Information Processing

Agent-based computing has the potential to improve the theory and practice of modelling, designing, and implementing complex systems. The paper presents that basic notions of intelligent agents and multi-agent systems and focus on some applications of multi-agent systems in different domains. In the last decade, Intelligent Agents and more recently, Multi-Agent Systems appeared as new software technologies that integrate a variety of Artificial Intelligence techniques from different subfields (reasoning, knowledge representation, machine learning, planning, coordination, communication and so on), and which offer an efficient and more natural alternative to build intelligent systems, thus giving a solution to the current complex real world problems that need to be solved. For example, a complex system could be decomposed in components and again the components in sub-components and so on, till some primitive entities are obtained. Some of these primitive entities could be viewed as being agents that solve their local problems and interact between them in order to solve the goal of the initial complex system. However, most of the real world complex systems are only nearly decomposable, and a solution would be to endow the components with the ability of making decisions about the nature and the scope of their interactions at run time. Still, from this simplistic view, we could figure out a new type of computing, based on agents. In [1], Jennings argued that agent-intelligent agents; multi-agent systems; coordination; negotiation; learning; agent-oriented methodologies; applications.

Ibn AL- Haitham Journal For Pure and Applied Sciences, 2020

Multi-agent systems are subfield of Artificial Intelligence that has experienced rapid growth because of its flexibility and intelligence in order to solve distributed problems. Multi-agent systems (MAS) have got interest from various researchers in different disciplines for solving sophisticated problems by dividing them into smaller tasks. These tasks can be assigned to agents as autonomous entities with their private database, which act on their environment, perceive, process, retain and recall by using multiple inputs. MAS can be defined as a network of individual agents that share knowledge and communicate with each other in order to solve a problem that is beyond the scope of a single agent. It is imperative to understand the characteristics of the individual agent or computing entity to distinguish a simple distributed system and multi-agent system. To solve each task, the agent can take the appropriate actions, decisions, and can cooperate with other agents to fulfill its go...

1996

Multi-agent systems can be successfully modelled and specified as interacting complex components in a compositional architecture. Compared to the specification of single agent compositional systems, the specification of multi-agent systems requires additional design (or modelling) choices. In this paper, design choices regarding interaction and autonomy of compositional models of intelligent agents are investigated. The choices made have implications for the specification framework, especially for its mechanisms for information exchange and task control. The influence of the modelling choices is discussed for the modelling framework DESIRE, a specific framework for compositional systems in particular for the specification of intelligent autonomous agents. Alternatives for the choices made in DESIRE are also presented.

2000

Agent design is a complex human activity involving active and proactive modules of software that may present knowledge representation and learning aspects. In this study we review some Agent-Oriented Methodologies including extensions of object oriented and knowledge engineering methodologies. Moreover, we present a case study of a multi-agent system for requirement engineering formal specification that was designed using "the Agent Modelling Technique for Systems of BDI agent" . Finally, a discussion about the use of the methodology is presented.

A software agent should be autonomous, flexible, robust and social. The increasing importance of the Multi-Agent System (MAS) has led to the greater availability of tools designed to facilitate their creation and use. To devise agents is considered a real challenge which faces analysts and designers during development of any multi-agent systems. This paper proposes a methodology to determine, clarify and differentiate among agents during development of multi-agent systems using goals and functionalities grouping approach. A step by step case of study is presented to explain the proposed methodology for the creation of an Online Intelligent System (OIS) for Board of Directors guide using Multi-Agent System to facilitate online communication between the board members and their president to monitor the policies and productivity of the company.

Expert Systems, 1994

Abstract: A language for multi-agent system design (MAPS) is presented and discussed in this paper. Any agent in MAPS is thought of as an expert system standing on its own. It can communicate through synchronous and asynchronous message sending. Dedicated behaviours are provided which specify how incoming messages are processed. Inter-agent cooperation is controlled via production rules. Two pre-defined agent classes are provided, which are given specific problem-solving roles: Knowledge Server (KS) agents are meant to maintain and transmit knowledge about problem-solving states, while Knowledge Processor (KP) agents are meant to process these elements in order to progress towards a solution. The duality between agent and resource modelling levels on the one hand and between KS and KP modelling styles on the other is shown to allow the specification of various control strategies. The environment is currently running on HP, SUN and DEC workstations.

The purpose of this chapter is to discuss the use of multi-agent systems to develop virtual reality training systems. We first review these systems and then investigate the architectures used. We demonstrate an example of our own (RiskMan) and then discuss the advantages and drawbacks of using multi-agent agent approaches in the development of virtual reality training systems. The chapter describes the system architecture of a multi-agent system for risk management (RiskMan) to help train police officers to handle high-risk situations. RiskMan has been developed using a high-level scripting language of a game engine, Unreal Tournament 2004. The major modules are a scenario-based expert system, a narrative engine, a game engine, and a graphics engine. The system integrates a simulation agent, trainee agent, communication agent, interface agent, and scripted agents communicating using games technology.

2010

Large Intelligent Systems are so complex these days that an urgent need for designing such systems in best available way is evolving. Modeling is the useful technique to show a complex real world system into the form of abstraction, so that analysis and implementation of the intelligent system become easy and is useful in gathering the prior knowledge of system that is not possible to experiment with the real world complex systems. This paper discusses a formal approach of agent-based large systems modeling for intelligent systems, which describes design level precautions, challenges and techniques using autonomous agents, as its fundamental modeling abstraction. We are discussing Ad-Hoc Network System as a case study in which we are using mobile agents where nodes are free to relocate, as they form an Intelligent Systems. The designing is very critical in this scenario and it can reduce the whole cost, time duration and risk involved in the project.