Advances in Agent Oriented Software Engineering have focused on the provision of frameworks and toolkits to aid in the creation of Multi Agent Systems (MASs). However, despite the need to address the inherent complexity of such systems, little progress has been made in the development of tools to allow for the debugging and understanding of their inner workings. This paper introduces a novel performance analysis system, named AgentSpotter, which facilitates such analysis. AgentSpotter was developed by mapping conventional profiling concepts to the domain of MASs. We outline its integration into the Agent Factory multi agent framework.
Other analysis tools exist for aiding the development of MASs. However, these tend to be narrower in their focus, concentrating only on specific aspects of debugging MASs and typically being only applicable to a specific agent platform. The Agent Factory Debugger @cite_11 is an example of a tool that is typical of most multi agent frameworks. Its principal function is inspecting the status and mental state of an individual agent: its goals, beliefs, commitments and the messages it has exchanged with other agents. Tools such as this give limited information about the interaction between agents and the consequences of these interactions.
@cite_11
1544271477 2787719459 1568975357 376136620
The ability to effectively debug agent-oriented applications is vital if agent technologies are to become adopted as a viable alternative for complex systems development. Recent advances in the area have focussed on the provision of support for debugging agent interaction where tools have been provided that allow developers to analyse and debug the messages that are passed between agents. One potential approach for constructing agent-oriented applications is through the use of agent programming languages. Such languages employ mental notions such as beliefs, goals, commitments, and intentions to facilitate the construction of agent programs that specify the high-level behaviour of the agent. This paper describes how debugging has been supported for one such language, namely the Agent Factory Agent Programming Language (AFAPL). Multi-agent Systems (MASs) have found a variety of industrial applications from economics to robotics, owing to their high adaptability, scalability and applicability. However, with the increasing complexity of MASs, multi-agent control has become a challenging problem to solve. Among different approaches to deal with this complex problem, game theoretic learning recently has received researchers' attention as a possible solution. In such learning scheme, by playing a game, each agent eventually discovers a solution on its own. The main focus of this paper is on enhancement of two types of game-theoretic learning algorithms: log linear learning and reinforcement learning. Each algorithm proposed in this paper, relaxes and imposes different assumptions to fit a class of MAS problems. Numerical experiments are also conducted to verify each algorithm's robustness and performance. Multi-agent systems (MAS) are a special kind of distributed systems in which the main entities are autonomous in a proactive sense. These systems are special distributed systems because of their complexity and hence their unpredictability. Agents can spontaneously engage in complex interactions, guided by their own goals and intentions. When developing such kinds of system, there are many problems the developer has to face. All these problems make it virtually impossible to totally debug a quite complex multi-agent system (i.e. a MAS in which hundreds or even thousands of agents are involved). In this article we present a debugging tool we have developed for the JADE agents platform. Hence, it is a FIPA technology based tool and seeks to alleviate typical debugging problems derived from distribution and unpredictability. The JADE Platform and Experiences with Mobile MAS Applications.- A-globe: Agent Development Platform with Inaccessibility and Mobility Support.- Supporting Agent Development in Erlang through the eXAT Platform.- Living Systems(R) Technology Suite.- Multi Agent System Development Kit.- An Integrated Development Environment for Electronic Institutions.- Jadex: A BDI-Agent System Combining Middleware and Reasoning.- Component Agent Framework for Non-Experts (CAFnE) Toolkit.- The WSDL2Agent Tool.- WS2JADE: A Tool for Run-time Deployment and Control of Web Services as JADE Agent Services.- A System for Analysis of Multi-Issue Negotiation.- FuzzyMAN: An Agent-based E-Marketplace with a Voice and Mobile User Interface.- Efficient Agent Communication in Wireless Environments.- AMETAS - the Asynchronous MEssage Transfer Agent System.- Tracy: An Extensible Plugin-Oriented Software Architecture for Mobile Agent Toolkits.- The Packet-World: A Test Bed for Investigating Situated Multi-Agent Systems.- Decommitment in a Competitive Multi-Agent Transportation Setting.- TeamWorker: An Agent-Based Support System for Mobile Task Execution.