A multi-agent framework for financial investment planning

If a company or person wants to invest a lot of money, where, when, and how should the investment go? A multi-agent based Financial Investment Planner may give some reasonable answers to the above question. Good advice is mainly based on adequate information, rich knowledge, and great
skills to use knowledge and information. To this end, this planner consists of four principal components information gathering agents that are responsible for gathering relevant information on the Internet, data mining agents that are in charge of discovering knowledge from retrieved information as well as other relevant databases, group decision making agents that can effectively use available knowledge and appropriate information to make reasonable decisions (investment advice), and a graphical user interface that interacts with users. This paper is focused on the group decision making part. The design and implementation of an agent-based hybrid intelligent system - agent-based soft computing society are detailed.

Insurance services in multi-agent systems

In a multi-agent environment, there is often the need for an agent to cooperate with others so as to ensure that a given task is achieved timely and cost-effectively. Present agent systems currently maximizes this through mechanisms such as trust and risk assessments. In this paper, we extend this mechanism by introducing the concept of insurance, in which the insurance agents act as a bridge between agents who require resources from others. Unlike traditional systems, agents purchase insurance so as to guarantee to have the requested resources during the task execution time and thus minimize the risk in task failure. The novelty of this proposal is that it ensures agents continuously to exchange resources and to seek maximum expected utility in a dynamic environment at the same time. Our experimental results confirm the feasibility of our approach.

Integrating insurance services, trust and risk mechanisms into multi-agent systems

In multi-agent systems, there is often the need for an agent to cooperate with others so as to ensure that a given task is achieved timely and cost effectively. Currently multi-agent systems maximize this through mechanisms such as coalition formation, trust and risk assessments, etc. In this paper, we incorporate the concept of insurance with trust and risk mechanisms in multi-agent systems. The novelty of this proposal is that it ensures continuous sharing of resources while encouraging expected utility to be maximized in a dynamic environment. Our experimental results confirm the feasibility of our approach.

Buying and selling with insurance in open multi-agent marketplace

In this paper, we incorporate the insurance concept in buying and selling model for agents to trade in the open multi-agent marketplace. During buying, agents purchase insurance as a method to search for potential sellers and select their partners based on the information provided by insurance agents. During selling, agents purchase insurance as a method to protect themselves against potential risk. The insurance concept greatly simplifies the trading procedure in the open marketplace. The novelty of this proposal is that it ensures a dynamic trading environment while agents continue to seek maximum utility and being fully protected by insurance. Our experimental results confirm the feasibility of our approach.

Multi-agent model for robotic assembly system

In this paper, a multi-agent based model for a robotic assembly system is presented. Firstly, an organization model is used to construct the multi-agent model. Secondly, a dynamic self-organizing method is then put forward for the multi-agent robotic system to bid and contract the operations. Thirdly, a real multi-agent robotic system is built and assembly experiments are carried out. Finally, the experimental results confirm that the present multi-agent robotic system has flexibility, adaptation and stability.

Applying a reconfigurable multi-agent scheduler to product distribution

More intelligent scheduling methods are required for manufacturing scheduling due to the move to more agile systems. Multi-agent methods are one such approach. This paper describes the application of a reconfigurable multi-agent scheduler to the problem of allocating orders to warehouses in a distribution supply chain. This multi-agent system was originally developed for allocation of orders to machines in a highly reconfigurable manufacturing system and this work was aimed at investigating the ease of applying this same scheduler to other problems. It was found that this new application was readily achieved because of the modular structure of the scheduler. This paper shows how the application to the new problem was achieved.

Artificial formation forces for stable aggregation of multi-agent system

This paper introduces, a robust and stable algorithm based on artificial formation forces, for multi-agent system (MAS) aggregation in 2D space. The MAS model with artificial forces; consists of inter-member collision avoidance element, formation generation element and a velocity based damping element; is analysed for stability and convergence. Computer simulations are used to illustrate stability and convergence, and to demonstrate effectiveness of the algorithm.

Observer-based control of multi-agent systems under decentralized information structure

This paper addresses the problem of decentralized implementation of a global state feedback controller for multi-agent systems. The system is assumed to be under the constraint of a complete decentralized information structure. The decentralization of the control task is achieved through the construction of low-order decentralized functional observers with the purpose of generating the required corresponding control signal for each local control station. A design procedure is developed for obtaining an approximate solution to the design of the observers. Stability analysis is provided for the global system using the proposed observer-based approach. A numerical example is given to illustrate the design procedure and cases when the observers' order increases from the lowest value.

Smart cluster bombs - control of multi-agent systems for military applications

This paper introduces an aggregation algorithm for airborne swarming guided weapon systems, which can aggregate munitions into a given shape while reaching the surface. The algorithm uses an artificial force based controller to navigate the members of the swarm into the desired geographical position and evenly distribute them inside the shape. Inter-member repulsion forces are used to avoid collisions among members, which is crucial for a weapon deployment system. Moreover, a lower bound for the release height was obtained which guarantee convergence of the complete weapon system into the target area. The proposed swarming guided weapon system was tested using computer simulations.

Information sharing between heterogeneous uncertain reasoning models in a multi-agent environment : a case study

The issue of information sharing and exchanging is one of the most important issues in the areas of artificial intelligence and knowledge-based systems (KBSs), or even in the broader areas of computer and information technology. This paper deals with a special case of this issue by carrying out a case study of information sharing between two well-known heterogeneous uncertain reasoning models: the certainty factor model and the subjective Bayesian method. More precisely, this paper discovers a family of exactly isomorphic transformations between these two uncertain reasoning models. More interestingly, among isomorphic transformation functions in this family, different ones can handle different degrees to which a domain expert is positive or negative when performing such a transformation task. The direct motivation of the investigation lies in a realistic consideration. In the past, expert systems exploited mainly these two models to deal with uncertainties. In other words, a lot of stand-alone expert systems which use the two uncertain reasoning models are available. If there is a reasonable transformation mechanism between these two uncertain reasoning models, we can use the Internet to couple these pre-existing expert systems together so that the integrated systems are able to exchange and share useful information with each other, thereby improving their performance through cooperation. Also, the issue of transformation between heterogeneous uncertain reasoning models is significant in the research area of multi-agent systems because different agents in a multi-agent system could employ different expert systems with heterogeneous uncertain reasonings for their action selections and the information sharing and exchanging is unavoidable between different agents. In addition, we make clear the relationship between the certainty factor model and probability theory.

A study of train group operation multi-agent model oriented RITS

In this paper, aiming at describing interrelationships and communication mechanisms among agents based on a multi-agent framework of Railway Intelligent Transportation System (RITS), we construct the model about stations and trains in the system, which is called Agent-Oriented G-Net Train Group Operation Model (AGNTOM). The framework degrades the complexity of computation and makes the distribution of simulation system easy in design. The simulated experiments prove that the model provides an effective approach for dealing with communication problems in the system.

A logical framework for knowledge sharing in multi-agent systems

The issue of knowledge sharing has been an important topic in multi-agent research. Knowledge sharing leads to that agents analyze, judge and synthesize the told information so as to make agents’ own knowledge. To match these applications, this paper builds a logical framework for knowledge sharing among agents. We develop a multimodal logic for reasoning about both agents’ knowledge and told information. For formalizing the relationship between knowledge and told information, we present a framework of semantics, with respect to which a sound and complete proof theory is given.

Supporting knowledge management infrastructure : a multi-agent approach

Knowledge management has by now exerted a large influence over industrial practice. We propose a multi-agent based system to provide infrastructure service to meet various requirements of knowledge management in view of the complex nature of the task, i.e., covering science and technologies across different disciplines. The paper discusses the use of a multi-agent system to support the knowledge management infrastructure. It will also discuss the design rational and implementation details as well as an application to the problem of feature extraction in weather prediction.

A computation trust model with trust network in multi-agent systems

Trust is a fundamental issue in multi-agent systems, especially when they are applied in e-commence. The computational models of trust play an important role in determining who and how to interact in open and dynamic environments. To this end, a computation trust model is proposed in which the confidence information based on direct prior interactions with the target agent and the reputation information from trust network are used. In this way, agents can autonomously deal with deception and identify trustworthy parties in multi-agent systems. The ontological property of trust is also considered in the model. A case study is provided to show the effectiveness of the proposed model.