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Abstract No body can deny the important role World Wide Web (WWW) has played in providing users from all around the globe with loads of information and data bases. At the same time the emergence of the web caused a lot of obstacles in searching for the needed information. In this regard AI experts developed agent systems that provide users with assistance and advice for the efficiency of the information search road. Agent based systems evolved to support other and endless applications, also multi agent systems were designed to ensure more accuracy in performing the tasks and to support distributed AI. Like any other invention, problems appeared especially when trying to relate agent system in any application, but at the same time the fast acceleration of those systems development didnt slow down since they are useful in a lot of fields. Now days a number of projects are taking place to improve their functionality and capabilities. 1.
Introduction Online information retrieval at its beginnings used to be done by knowledgeable information seekers called intermediaries. Those intermediaries meet with users and individuals to help them find their desired information since they have the experience in finding user interest areas, but due to the emergence of the World Wide Web (WWW). And the amount of information it provided which caused overload and difficulty in finding the needed data, both experienced and inexperienced users need help in the search process to save time and effort, Borgman (1986). To solve this problem, Artificial Intelligent (AI) experts developed software called intelligent agents or agents. The developed software can help users in browsing the related data bases provided by (WWW) more conveniently, and to ensure the relevance of the retrieved information, Tecuci (1998).
According to Russell and Norvig (2003), agent is anything that responds to the environment where it exists through sensors to receive the signal or command and corresponds to the environment via actuators. This means that agent communicates with its environment by receiving tasks through sensors and by implementing them and displaying results to the environment by actuators. Intelligent agent according to Tecuci (1998) is a knowledgeable based system that can interact with the user through natural language. It helps the user to accomplish tasks without absolute obedience from the agent but with the guidance from the user. It has the ability to distinguish between different tasks and decide which tasks to take.
The main difference between intelligent agent and agent is the word intelligent. In the term agent, the word intelligent means according to Jennings and Wooldridge (1998) the following: Reactivity: this means that intelligent agents can perceive their environment and respond to changes that take place. Pro activeness: intelligent agents exhibit goal-directed behaviour by taking the initiative. Social ability: this means that intelligent agents can communicate with both agents and human to satisfy their design purpose.
According to Sdsu (2002), there are other characteristics for intelligent agents such as: Mobility: means that the agent can travel from one machine to another using the advantage of the internet. Autonomy: the agent can perform a set of tasks without the user notification or confirmation. This means agent can control the actions it takes and adjust itself to the condition that is taking place. Such as night backup. Intelligent agents are agents provided with improved capabilities to perform the purpose behind their design. And the way agents behave depends on the correspondence between external and internal agent domain, D'inferno and Luck (1998). 2.
How agents work The way agent operates and behaves depends on the environment it takes place in. the basic way of their operations is that they help users and individuals in doing their tasks. The main functionality of the agent is based on the correspondence between external application of the agent and internal domain consisting of knowledge base and an interface engine. The knowledge base contains the data structure representing the application domain such as entities, objects, relations. While the interface engine consists of programs to manipulate the data structure in order to solve the problem the agent was designed for.
Tecuci (1998). Agents can learn from users or other agents either directly or by observing their behaviour or by own experience Muller et al (2003). They are called learning agents and are defined by Tecuci (1998: 2): "Agent that is able by itself to acquire and maintain its knowledge." Agents can improve their performance and increase the work they can achieve through learning and this depends on how the agent can communicate and adjust with the nature of the environment it exists in Russell and Norvig (2003). Learning from previous experiences might be crucial and important in some applications and at the same time undesirable in other applications, Wooldridge (2002). This means that an action could be taken if a specific condition existed, but if the condition that caused the action didnt exist in the new situation, then there is no need to repeat or use the experience learned. In order to create a learning agent, it should include together with knowledge base and interface engine, a learning engine that is capable of updating data in the knowledge base.
Learning engine can learn from sources in the environment that surrounds the agent such as other agents, users, data bases or own experience, Klusch (1999). According to Tecuci (1998) if an agent can learn then building the knowledge base is an easy task, but the difficult part is to build and create learning engine due to the lack of understanding of the learning process. So in this case the building of learning agent should at least provide the agent with some knowledge base with the ability to customize and correct during learning process. This means that making a learning agent is very difficult task and learning methods are used to create the knowledge base.
To sum up, agents include knowledge base and interface engine. While learning agents include in addition to what mentioned a learning agent. The main influence on the functionality of the agent is the environment that surrounds it. And the influence from the environment plays a big role in building the agent's knowledge base. 3. Type of Environment Wooldridge (2002) lists the most common environments where agents operate as following: Accessible vs. Inaccessible: in the accessible environment, an agent can get updated information about the status of the environment, but most environments are in accessible.
Deterministic vs. Non-deterministic: this means that every action has a guaranteed effect. But at the same time there is no prediction of the action result. Static vs. Dynamic: the static environment changes only by the agent action and work it performs, while dynamic have other processes and is out of complete agent control. Discrete vs.
Continuous: discrete environment has a fixed number of actions and reactions (effects). While in continuous environment there is no limit of action or reactions. Episodic vs. non-Episodic: in episodic environment, the agent experience is divided into episodes. Each episode includes the agents receiving and then acting. Episodic environment are simple since agents doesnt need to think ahead because the experience is divided through the whole task and next actions to be taken. 4.
Multi-Agent System (MAS) According to Ferber (1999), MAS is a system where all agents communicate with each other by sending different messages or signals. In MAS different agents can share the same goal and interact together or each agent can have a special interest area, Air (2000). Agents can communicate with each other whether they were positioned in environment or not. If agents communicate without existing environment, the system is called purely communicating MAS.
While if agents communicate and were positioned in environment, the system is called situated MAS, Muller et al (2003). Beer et al (1999) discussed the form of interaction between agents in multi agent system. The interaction depends on negotiations between agents. Agents should agree on a goal or a plan. Agents can have influence on each other to convince each other to do so and so. The negotiations depend on three factors which are: Negotiation Protocol: protocols are set of rules that organize negotiations between agents.
It decides the negotiators and the related third party. Also actions to be taken together with events that cause change are decided. Negotiation Objects: this decides the situations that should be discussed by which agreement is reached. This means the structure of agreement is fixed together with the level of acceptance or rejection. Here the action to be taken is discussed and all agents decide to agree on this action or not.
Agents Reasoning Models: shows the decision making structure through which agents will achieve what was agreed. It means that the steps of action agreed on are displayed. 5. Applications of agents According to Wooldridge (2002), the basic and main applications of agents can be classified as following: Distributed Systems: in this case agents work as nodes in distributed system. This is most commonly used in multi agent systems. Personal Software Assistants: agents work as assistants to users in some applications. This is commonly used in individual agent systems.
The widely distributed applications where agents are used are as following in more details: Agents for Business Process Management: in this case agents interact to achieve organizational objectives on behalf of individuals or companies. In the agent based business process management, the organization is treated as a society of agents providing services. It is based on transforming each department in the organization into an agent, and each employee in the department is considered as an agent. These agents communicate between each other to discuss the best way of providing services inside the institution Jennings (1998). Agents for Distributed Sensing: it is considered as the main application of multi agent systems. The main idea was to have a number of sensors (agents) that track a certain event to make the work easier.
The sensors (agents) can communicate with each other and provide predictions to inform each other when the event will occur. Agents for Information Retrieval and Management: the agent that is responsible of helping users to track and search desired information is called information agent. The user types a query, and the information agent searches various browsers and data bases since it has authority to access various information on (WWW). According to Wooldridge (2002), agents can help in this task because of the following characteristics that the web has: o The web allows access to plenty of information resources without restrictions on any user. o The web has specific interface for different multimedia.
o The web allows different documents to be linked in a good and meaningful way. o The web interface is user friendly that is simple and professional. Despite all these characteristics, users usually get bored from browsing the web due to information traffic jam and overload. Different search engines tried to solve this problem by arranging documents and then retrieving them if the query matches metadata. But still it is not an efficient way. While other websites tried to attract people by providing overwhelming animations and colours, but that doesnt change the fact that users are looking for specific information rather than the interface and appearance.
Durfee et al. (1997) stated that in order to solve those problems, different agents are used such as: Personal Information Agents: Maes (1994 a) developed E-mail assistant software that takes actions according to the user usual actions. The agent learns from users and repeats their actions when a new event happens. In the case of new event that didnt occur before, the agent compares the event with previous actions and tries to guess what the user will do in this case. If the agent is confident of what to do, it will perform the suggested action. Else it will suggest an action to the user. Web Agents: Etzioni and Weld (1995) proposed the idea of web-based agents.
Now web agents are seen in different ways as: o Tour Guides: this kind of agents usually answers questions as where to go next. It either depends on learning from user preference or suggests a link that is related to the previous link. o Indexing Agents: the idea here is to use the information provided by search engine and the user goals or interests to display a customized service to the user. o FAQ Finders: it aims to direct users to frequently asked questions to get their answers.
Since FAQ's are learning intensive, there will be a lot of potential to find automated FAQ finder. o Expertise Finder: tries to find what users really want in order to provide better information retrieval service. Despite all that, search...
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Research essay sample on Solve This Problem Intelligent Agents
