Agent-Based Hybrid Intelligent Systems: An Agent-Based Framework for Complex Problem Solving

Pt. I. Fundamentals of hybrid intelligent systems and agents -- 1. Introduction -- 2. Basics of hybrid intelligent systems -- 3. Basics of agents and multi-agent systems -- Pt. II. Methodology and framework -- 4. Agent-oriented methodologies -- 5. Agent-based framework for hybrid intelligent systems --6. Matchmaking in middle agents -- Pt. III. Application systems -- 7. Agent-based hybrid intelligent system for financial investment
planning -- 8. Agent-based hybrid intelligent system for data mining -- Pt. IV. Concluding remarks -- 9. The less the more -- App. Sample source codes of the agent-based financial planning system

Intelligent agent technology within information warfare

Research into Intelligent Agent (IA) technology and how it can assist computer systems in the autonomous completion of common office and home computing tasks is extremely widespread. The use oflA's is becoming more feasible as the functionality moves Into line with what users require for their everyday computing needs. However, this does not mean that IA technology cannot be exploited or developed for use in a malicious manner, such as within an Information Waifare (IW) scenario, where systems may be attacked autonomously by agent system implem-entations. This paper will discuss tne cilrrenlStcite Ofmalicious use of lA's as well as focusing on attack techniques, the difficulties brought about by such attacks as well as security methods, both proactive and reactive, that could be instated within compromised or sensitive systems.

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.

An agent-based hybrid framework for database mining

While knowledge discovery in databases (KDD) is defined as an iterative sequence of the following steps: data pre-processing, data mining, and post data mining, a significant amount of research in data mining has been done, resulting in a variety of algorithms and techniques for each step. However, a single data-mining technique has not been proven appropriate for every domain and data set. Instead, several techniques may need to be integrated into hybrid systems and used cooperatively during a particular data-mining operation. That is, hybrid solutions are crucial for the success of data mining. This paper presents a hybrid framework for identifying patterns from databases or multi-databases. The framework integrates these techniques for mining tasks from an agent point of view. Based on the experiments conducted, putting different KDD techniques together into the agent-based architecture enables them to be used cooperatively when needed. The proposed framework provides a highly flexible and robust data-mining platform and the resulting systems demonstrate emergent behaviors although it does not improve the performance of individual KDD techniques.

A reputation-based trust model for agent societies

Trust and security issues are prevalent in agent societies, where agents are autonomously owned and operated in a networked environment. Nowadays, trust and reputation management is a promising approach to manage them. However, many reputation models su.ered from a major drawback – there is no mechanism to discourage agents from lying information when making a recommendation. Although some works do take into account of this issue, they usually do not penalize an agent for making poor referrals. Worse, some systems actually judge an agents referral reputation based on its service reputation. In situations where this is unacceptable, we need to have a mechanism where agents are not only discouraged from making poor referrals, but are also penalized when doing so. Towards this, we propose a reputation-based trust model that considers an agents referral reputation as a separate entity within the broader sense of an agents reputation. Our objective is not to replace any existing reputation mechanisms, but rather to complement and extend them.

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.

Decision aggregation in an agent-based financial investment planning system

Agent technology provides a new way to model many complex problems like financial investment planning. With this observation in mind, a financial investment planning system was developed from agent perspectives with 12 different agents integrated. Some of the agents have similar problem solving and decision making capabilities. The results from these agents require to be combined. Ordered Weighted Averaging (OWA) operator was chosen to aggregate different results. Details on how OWA was applied as well as appropriate evaluation are presented.

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.

An agent-based hybrid intelligent system for financial investment planning

Many complex problems including financial investment planning require hybrid intelligent systems that integrate many intelligent techniques including expert systems, fuzzy logic, neural networks, and genetic algorithms. However, hybrid intelligent systems are difficult to develop due to complicated interactions and technique incompatibilities. This paper describes a hybrid intelligent system for financial investment planning that was built from agent points of view. This system currently consists of 13 different agents. The experimental results show that all agents in the system can work cooperatively to provide reasonable investment advice. The system is very flexible and robust. The success of the system indicates that agent technologies can significantly facilitate the construction of hybrid intelligent systems.

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.

The application of a reinforcement learning agent to a multi-product manufacturing facility

An intelligent agent-based scheduling system, consisting of a reinforcement learning agent and a simulation model has been developed and tested on a classic scheduling problem. The production facility studied is a multiproduct serial line subject to stochastic failure. The agent goal is to minimise total production costs, through selection of job sequence and batch size. To explore state space the agent used reinforcement learning. By applying an independent inventory control policy for each product, the agent successfully identified optimal operating policies for a real production facility.