Current networking technology and the ready availability of vast amounts of data and information on the Internet-based Infosphere present great opportunities for bringing to decision makers and decision support systems more abundant and accurate information. The use of the Internet has accelerated at an unprecedented pace. However, effective use of the Internet by humans or decision support machine systems has been hampered by some dominant characteristics of the Infosphere. First, information available from the net is unorganized, multi-modal, and distributed on server sites all over the world. Second, the number and variety of data sources and services is dramatically increasing every day. Furthermore, the availability, type and reliability of information services are constantly changing. Third, the same piece of information can be accessible from a variety of different information sources. Fourth, information is ambiguous and possibly erroneous due to the dynamic nature of the information sources and potential information updating and maintenance problems. Therefore, information is becoming increasingly more di cult for a person or machine system to collect, filter, evaluate, and use in problem solving. As a result, the problem of locating information sources, accessing, filtering, and integrating information in support of decision making, as well as coordinating information retrieval and problem solving e orts of information sources and decision-making systems has become a very critical task.
The notion of ?Intelligent Software Agents? has been proposed to address this challenge. Although a precise definition of an intelligent agent is still forthcoming, the current working notion is that Intelligent Software Agents are programs that act on behalf of their human users in order to perform laborious information gathering tasks, such as locating and accessing information from various on-line information sources, resolve inconsistencies in the retrieved information, filter away irrelevant or unwanted information, integrate information from heterogeneous information sources, and adapt over time to their human users' information needs and the shape of the Infosphere.
INTRODUCTION
A software agent that uses Artificial Intelligence (AI) in the pursuit of the goals of its clients. Artificial Intelligence is the imitation of human intelligence by mechanical means. Clients, then, can reduce human workload by delegating to ISAs tasks that normally would require human-like intelligence.
Many researchers that formerly referred to their work as AI are now actively engaged in "agent technology". Thus the word "agent" by itself generally connotes ISAs in the terms of the present-day research community.a
Delegacy for ISAs is far more absolute. ISAs have the capability to generate and implement novel rules of behavior which human beings may never have the opportunity or desire to review. As ISAs can engage in extensive logical planning and inferencing, the relationship of trust between the client and the agent is or must be far greater, especially when the consumption of client resources is committed for reasons unexplained or multiple complex operations are actuated before human observers can react.
Competency as practiced by ISAs adds higher order functionality to the mix of capabilities. In addition to communicating with their environment to collect data and actuate changes, ISAs can often analyze the information to find non-obvious or hidden patterns, extracting knowledge from raw data. Environmental modes of interaction are richer, incorporating the media of humans such as natural language text, speech, and vision.
Amenability in ISAs can include self-monitoring of achievement toward client goals combined with continuous, online learning to improve performance. Adaptive mechanisms in ISAs mean that they are far less brittle to changes in environment and may actually improve. In addition, client responsiveness may go so far as to infer what a client wants when the client himself does not know or cannot adequately express the desired goals in definitive terms.
Mobile Agents:
Also known as traveling agents, these programs will shuttle their being, code and state, among resources. This often improves performance by moving the agents to where the data reside instead of moving the data to where the agents reside. The alternative typical operation involves a client-server model. In this case, the agent, in the role of the client, requests that the server transmit volumes of data back to the agent to be analyzed. Oftentimes the data must be returned by the agent to the server in a processed form. Significant bandwidth performance improvements can be achieved by running the agents within the same chassis as the data. Mobile agent frameworks are currently rare, however, due to the high level of trust required to accept a foreign agent onto one's data server. With advances in technologies for accountability and immunity, mobile agent systems are expected to become more popular.
Distributed Agents:
Load-balancing can be achieved by distributing agents over a finite number of computational resources. Some mobile agents are self-distributing, seeking and moving to agent platforms that can offer the higher computational resources at lower costs.
Multiple Agents:
Some tasks can be broken into sub-tasks to be performed independently by specialized agents. Such agents are unaware of the existence of the others but nonetheless rely upon the successful operations of all.
Collaborative Agents:
Collaborative agents interact with each other to share information or barter for specialized services to effect a deliberate synergism. While each agent may uniquely speak the protocol of a particular operating environment, they generally share a common interface language which enables them to request specialized services from their brethren as required.
Social Agents:
Anthropomorphism is seen by some researchers as a key requirement to successful collaboration between humans and agents. To this end, agents are being developed which can both present themselves as human-like creations as well as interpret human-generated communications such as continuous speech, gestures, and facial expressions.
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