An agent, in the traditional use of the word, is a person that acts on behalf of another person or group of persons. In information technology, the term agent is broadly used to describe software that carries out a special range of tasks on behalf of either a human user or other pieces of software. Such a concept is not new in computing. Similar things have been said about subroutines, reusable objects, components, and Web services. So what makes agents more than just another computer technology buzzword and research fashion?
Definitions And Classifications
According to Jennings, Sycara, and Wooldridge (1998, p. 8), “An agent is a computer system, situated in some environment that is capable of flexible autonomous action in order to meet its design objectives.” Thus, the determining characteristics of an software agent are:
Reactivity: An agent has profound knowledge of its environment and has the ability to interact directly with it. It can receive input from the outside and can perform reactions with external effects.
Autonomy: An agent is in charge of its own internal status and actions. It can perform independently without the explicit interference of any user or other agents.
Proactivity: An agent has the ability to interpret even minor changes in its environment and can take the initiative to act upon them. It can communicate and interact with entities and can delegate tasks to other agents.
Intelligence: An agent’s degree of intelligence is determined by its capability to apply methods of AI in order optimize its action (Meier, 2006, pp. 20-320).
The research literature discusses many different types of agents, carrying out all sorts of functions with what can be termed primary and secondary characteristics. Primary characteristics include autonomy, cooperation, and learning, while secondary characteristics include aspects like multi-functionality, goodwill, or trustworthiness.
A typology of software agents was proposed by Nwana (1996, pp. 7-38):
Collaborative agents feature a high degree of cooperation and autonomy. They are determined by the idea of distributed artificial intelligence and by the concept of task sharing, cooperation, and negotiation between agents.
Interface agents focus on the characteristics of learning and autonomy. By collaborating with the user and by sharing knowledge with other agents, they learn a user’s behavior and are trained to take the initiative to act appropriately.
Mobile agents are not static but have the ability to travel. This entails non-functional benefits such as freeing local resources, showing more flexibility, and enabling an asynchronous work scenario.
Information or Internet agents emphasize managing enormous amounts of information. Their main task is to know where to search for information, how to retrieve it, and how to aggregate it.
Reactive agents show a stimulus-response manner as opposed to acting deliberatively. Since they are based in the physical world and only react to present changes, their behavior is not predetermined.
Hybrid agents comprise more than one agent philosophy and benefit from the combination of different architectures.
Key Terms in this Chapter
Business Process: A process at the business layer of an organization. Since the 1990s, the focus of any business reengineering project and one of the central inputs for IT design. Also called workflow .
Game Theory: Mathematical theory of rational behavior for situations involving conflicts of interest.
Categorization: The process of deducing, from the content of an artifact, the potentially multiple ways in which the artifact can be classified for the purpose of later retrieval from a database, library, collection, or physical storage system.
Workflow: The automation of a business process, in whole or part, during which documents, information, or tasks are passed from one participant to another for action, according to a set of procedural rules. Also called business process .
Artificial Intelligence: Computer systems that feature automated human-intelligent, rational behavior and employ knowledge representation and reasoning methods.
Data Mining: Integrating statistics, database technology, pattern recognition, and machine learning to generate additional value and strategic advantages.