Intelligent Mobile Agents Research Articles

A secure brokerage network for retail banking services

The objective of this paper is to propose and describe a secure and trusted brokerage network for offering retail-banking services. The goal of this network is to collect information about retail-banking service offerings from various commercial banks and make this information available to the public. Facilities that will help prospective clients to decide on the right service according to their needs are also offered. Mobile intelligent agent technology will enhance functionality, efficiency and performance.

Intelligent mobile agents in large distributed autonomous cooperative systems

A large distributed autonomous cooperative system is a system that provides fundamental services for integrating high-level services in a large distributed system with local autonomy for individual system platforms and processes. This paper discusses the role and the functions of Intelligent Mobile Agents (IMAs) in large distributed autonomous cooperative systems. Rather than providing services to a user at the application level, IMAs considered in this paper are deemed an integral part of system level software and perform tasks that are considered central to the distributed system. A variety of solutions to problems that are inherent to the distributed nature of the computing infrastructure may be implemented through a system of IMAs. These problems include, but are by no means limited to, load balancing, scheduling, information retrieval and management, distributed decision support, routing and flow control, security and intrusion detection. In this paper we discuss some of characteristics of IMA based systems central to solving some of these problems. The effectiveness of IMAs in large distributed systems clearly depends on the design and implementation of the underlying IMA architecture. This paper discusses the features that must be provided by the IMA infrastructure in support of IMAs that are an integral part of the large distributed autonomous cooperative system.

Intelligent mobile agents in elderly care

Society is currently facing the ageing of the population which, together with the increasing engagement of both men and women in out of home work activities, demand new solutions for taking care of elderly people. Inexpensive support systems for elderly people staying alone at home, allowing care and health centers to remotely observe and help them, are becoming feasible with today's technology. In this paper, an architecture for such a system based on the Internet and extensively exploiting the multi-agent systems paradigm is presented that includes both stationary and mobile agents. Furthermore, an adaptation mechanism that allows mobile agents to adapt themselves to diversified local environments is presented, including prototype implementation details.

Distributed optimization of tactical actions by mobile intelligent agents

In this article we compare the performance of a learning controller with and without a flexible and incrementally expandable distribution scheme. The learning method is modeled after Holland's learning classifier system, but it uses Q-learning as the reinforcement method instead of the “bucket-brigade.” The method for distributing the learning among sub-processes is modular, so that learning can be accomplished monolithically, as a redundant system of peers, or as a conceptual hierarchy of competence levels with varying degrees of abstraction. They can also communicate using short messages transmitted quickly between modules running in separate processes, on separate machines, or across a network connection. The system is applied to a mobile agent whose task is to defend itself against other mobile agents trying to attack it. As a simulation example, we consider a naval combat scenario wherein a surface vessel is considered as a mobile root with various radar and communications sensors. Its task is to detect other agents such as other ships and planes, identify and classify them, and devise optimal tactical decision policies. The results suggest that the learning method employed is useful in general for any sensor-based autonomous robot in dynamic, unstructured environments. © 1997 John Wiley & Sons, Inc.

PLANGENT: an approach to making mobile agents intelligent

Network environments give computer users the option of employing distributed information and services to complete a task. However, gathering information and using services distributed in networks requires knowing exactly what kinds of information and services are required for a task, where they are, and how they can be obtained or utilized. Tracking down the answers to these questions can be difficult, time consuming tasks for users. Mobile agent technology is expected to release them from having to do so. Instead, intelligent mobile agents will comprehend the user's requirements, search network nodes autonomously for appropriate information and services, and return with the answers. But several problems must be solved before we can expect agents to perform such actions effectively. We focus on the question of intelligence as a prerequisite for agent functions. What sort of intelligence is expected of agents? We have adopted a model based on the ability to make flexible plans. Specifically, we think mobile agents must be able to: understand user requirements; plan actions that will satisfy the requirements act according to the plan; modify the plan according to actual conditions when they differ from those initially expected; and execute the modified plan. We have implemented these functions in the Plangent system and validated their effectiveness in several example applications. We describe how we combined these planning functions with mobile agent facilities, and show how the agents behave intelligently in an example application of personal travel assistance.