Abstract
It is probably quite rare for a software technology to seize the imagination of the computer science
community at large. And yet this is precisely what has happened with intelligent agents and multi-
agent systems. In the past few years, interest in agents has grown at an astonishing rate, and while
the current hype means that there is sure to be a backlash eventually, this backlash is not yet in
evidence.
The aim of this article is to survey some key research issues and developments in the area
of intelligent agents and multi-agent systems. While an article like this cannot hope to act as
an introduction to all the issues in a ?eld as rich and diverse as multi-agent systems, the aim is
nevertheless to point the reader at the main areas of interest. Note that the article is intended as
an introduction, not as a specialist, advanced survey.
The article starts ? inevitably ? by asking the question what is an agent? This leads to
a brief discussion on the topic of what sort of computer systems are most appropriately con-
ceived and implemented as multi-agent systems. A crude classi?cation scheme is then intro-
duced, whereby the issues relating to the design and implementation of multi-agent systems are
divided into micro (agent-level) issues and macro (society-level) issues. In section 2, micro-level
issues (essentially, what software structure should an agent have?) are discussed in more detail.
Traditional symbolic AI architectures for agents are reviewed, as well as alternative, reactive
architectures, and ?nally, various hybrid architectures. One particularly well-known agent archi-
tecture is discussed in detail: the Procedural Reasoning System (PRS) [50]. Section 3 considers
macro, or society-level aspects of multi-agent systems. It begins by identifying a research area
known as cooperative distributed problem solving (CDPS), and goes on to consider the issues of
coordination and coherence. This section also mentions issues such as communication and ne-
gotiation. Section 4 discusses the applications to which the technology of intelligent agents and
multi-agent systems seems likely to be applied. Finally, in section 5, some concluding remarks are presented.
Another similar one is : Agent-based traffic management systems can use the autonomy, mobility, and adaptability of mobile agents to deal with dynamic traffic environments. Cloud computing can help such systems cope with the large amounts of storage and computing resources required to use traffic strategy agents and mass transport data effectively. This article reviews the history of the development of traffic control and management systems within the evolving computing paradigm and shows the state of traffic control and management systems based on mobile multi agent technology. Intelligent transportation clouds could provide services such as decision support, a standard development environment for traffic management strategies, and so on. With mobile agent technology, an urban-traffic management system based on Agent-Based Distributed and Adaptive Platforms for Transportation Systems (Adapts) is both feasible and effective. However, the large-scale use of mobile agents will lead to the emergence of a complex, powerful organization layer that requires enormous computing and power resources. To deal with this problem, we propose a prototype urban-traffic management system using intelligent traffic clouds.
System Diagram
Proposed System
Agent-based computing and mobile agents were proposed to handle this vexing problem. Only requiring a runtime environment, mobile agents can run computations near data to improve performance by reducing communication time and costs. This computing paradigm soon drew much attention in the transportation field. From multi agent systems and agent structure to ways of negotiating between agents to control agent strategies, all these fields have had varying degrees of success.
Cloud computing provides on demand computing capacity to individuals and businesses in the form of heterogeneous and autonomous services. With cloud computing, users do not need to understand the details of the infrastructure in the ?clouds;? they need only know what resources they need and how to obtain appropriate services, which shields the computational complexity of providing the required services.
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