Intelligent agent
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Please improve this article if you can. (July 2008)
In artificial intelligence, an intelligent agent (IA) is an entity which can observe and act upon an environment (i.e. it is an agent) and directs its activity towards achieving goals (i.e. it is rational). Intelligent agents may also use learning and knowledge to help them achieve their goals. They may be very simple or very complex: a reflex machine is an intelligent agent, as is a human being, as is a community of human beings working together towards a goal.
Intelligent agents are often described schematically as an abstract functional system, similar to a computer program. For this reason, intelligent agents are sometimes called abstract intelligent agents to distinguish them from their real world implementations as computer systems, biological systems, or organizations. Some definitions of intelligent agents emphasize their autonomy, and so prefer the term autonomous intelligent agents. Still others (notably Russell & Norvig (2003)) consider goal-directed behavior as the essence of rationality and so prefer the term rational agent.
Intelligent agents are closely related to agents in economics, and versions of the intelligent agent paradigm are studied in cognitive science, ethics, the philosophy of practical reason, as well as in many interdisciplinary socio-cognitive modeling and computer social simulations.
Intelligent agents are also closely related to software agents (an autonomous software program that assists users). In computer science, the term intelligent agent may be used to refer to a software agent that has some intelligence, regardless if it is not a rational agent by Russell and Norvig's definition. For example, autonomous programs used for operator assistance or data mining (sometimes referred to as bots) are also called "intelligent agents".
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[edit] A variety of definitions
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Intelligent agents have been defined many different ways.
In some literature, IAs are also referred to as autonomous intelligent agents, which means they act independently, and will learn and adapt to changing circumstances. According to Nikola Kasabov IA systems should exhibit the following characteristics:
[edit] Classes of intelligent agents
- See also: rational agent and agent environment
Russell & Norvig (2003) describe multiple types of agents and sub-agents. For example:
A simple agent program can be defined mathematically as an agent function which maps every possible percepts sequence to a possible action the agent can perform or to a coefficient, feedback element, function or constant that affects eventual actions:
f:P * − > A
The program agent, instead, maps every possible percept to an action.
It is possible to group agents into five classes based on their degree of perceived intelligence and capability:
- Simple reflex agents
Simple reflex agents acts only on the basis of the current percept. The agent function is based on the condition-action rule:
if condition then action rule
This agent function only succeeds when the environment is fully observable. Some reflex agents can also contain information on their current state which allows them to disregard conditions whose actuators are already triggered.
- Model-based reflex agents
Model-based agents can handle partially observable environments. Its current state is stored inside the agent maintaining some kind of structure which describes the part of the world which cannot be seen. This behavior requires information on how the world behaves and works. This additional information completes the “World View†model.
- Goal-based agents
Goal-based agents are model-based agents which store information regarding situations that are desirable. This allows the agent a way to choose among multiple possibilities, selecting the one which reaches a goal state.
- Utility-based agents
Goal-based agents only distinguish between goal states and non-goal states. It is possible to define a measure of how desirable a particular state is. This measure can be obtained through the use of a utility function which maps a state to a measure of the utility of the state.
- Learning agents
[edit] Other classes of intelligent agents
According to other sources[who?], some of the sub-agents (not already mentioned in this treatment) that may be a part of an Intelligent Agent or a complete Intelligent Agent in themselves are:
[edit] Agent environments
Russell & Norvig 2004 also define agents in terms of the environments they are expected to operate in.
[edit] Hierarchies of agents
To actively perform their functions, Intelligent Agents today are normally gathered in a hierarchical structure containing many “sub-agentsâ€. Intelligent sub-agents process and perform lower level functions. Taken together, the intelligent agent and sub-agents create a complete system that can accomplish difficult tasks or goals with behaviors and responses that display a form of intelligence.[citation needed]
[edit] See also
[edit] References
1. ^ Russell, Stuart J. & Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, NJ: Prentice Hall, ISBN 0-13-790395-2, <http://aima.cs.berkeley.edu/> , chpt. 2
2. ^ Stan Franklin and Art Graesser (1996); Is it an Agent, or just a Program?: A Taxonomy for Autonomous Agents; Proceedings of the Third International Workshop on Agent Theories, Architectures, and Languages, Springer-Verlag, 1996
3. ^ Adam Maria Gadomski, Jan M. Zytkow,Abstract Intelligent Agents: Paradigms, Foundations and Conceptualization Problems, in "Abstract Intelligent Agent, 2". Printed by ENEA, Rome 1995, ISSN/1120-558X
4. ^ N. Kasabov, Introduction: Hybrid intelligent adaptive systems. International Journal of Intelligent Systems, Vol.6, (1998) 453-454.
