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In artificial intelligence, an intelligent agent (IA) is an entity which observes and acts 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 learn or use knowledge to 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 (AIA) to distinguish them from their real world implementations as computer systems, biological systems, or organizations. AIA is an entity which exhibits an essence of human-like intelligence and, as an IA, may have numerous other properties resulting from the properties of its carrier physical or software system (A.M. Gadomski, 1993). For this reason IA can be either rational or emotive/irrational or, according to Herbert Simon, it represents bounded rationality.
Some definitions of intelligent agents emphasize their autonomy, and so prefer the term autonomous intelligent agents. Still others (notably Russell & Norvig (2003)) considered goal-directed behavior as the essence of rationality and so preferred the term rational agent.
In order to separate necessary and not necessary properties of IA, in the computational TOGA meta-theory , every cognitive AIA acts on the base of its/his/her available information, possessed preferences and knowledge (IPK model) with a different range, on various abstraction levels, and in different domains of activity. Such agent is called personoid. The quality of application and processing of its information, knowledge and preferences depends on the characteristics of AIA's carrier system, i.e. memory available, velocity and other its structural properties. According to different I, P,K bases, IA may be specialized for numerous roles.
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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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:
- learn and improve through interaction with the environment (embodiment)
- adapt online and in real time
- learn quickly from large amounts of data
- accommodate new problem solving rules incrementally
- have memory based exemplar storage and retrieval capacities
- have parameters to represent short and long term memory, age, forgetting, etc.
- be able to analyze itself in terms of behavior, error and success.
Classes of intelligent agents
Russell & Norvig (2003) describe multiple types of agents and sub-agents. For example:
- Physical Agents - A physical agent is an entity which percepts through sensors and acts through actuators.
- Temporal Agents - A temporal agent may use time based stored information to offer instructions or data acts to a computer program or human being and takes program inputs percepts to adjust its next behaviors.
- Believable agents - An agent exhibiting a personality via the use of an artificial character (the agent is embedded) for the interaction.
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
- model-based reflex agents
- goal-based agents
- utility-based agents
- learning agents
- 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
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Please help improve this article or section by expanding it. Further information might be found on the talk page or at requests for expansion. (August 2008) |
Other classes of intelligent agents
According to other sourceswho?, 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:
- Temporal Agents (for time-based decisions);
- Spatial Agents (that relate to the physical real-world);
- Input Agents (that process and make sense of sensor inputs - example neural network based agents neural network);
- Processing Agents (that solve a problem like speech recognition);
- Decision Agents (that are geared to decision making);
- Learning Agents (for building up the data structures and database of other Intelligent agents);
- World Agents (that incorporate a combination of all the other classes of agents to allow autonomous behaviors).
Agent environments
Environments in which agents operate can be defined in different ways. It is helpful to view the following definitions as referring to the way the environment appears from the point of view of the agent itself.
Observable & partially observable
In order for an agent to be considered an agent, some part of the environment - relevant to the action being considered - must be observable. In some cases (particularly in software) all of the environment will be observable by the agent. This, while useful to the agent, will generally only be true for relatively simple environments.
Deterministic, stochastic & strategic
An environment that is fully deterministic is one in which the subsequent state of the environment is wholly dependent on the preceding state and the actions of the agent. If an element of interference or uncertainty occurs then the environment is stochastic. Note that a deterministic yet partially observable environment will appear to be stochastic to the agent.
An environment state wholly determined by the preceding state and the actions of multiple agents is called strategic.
Episodic & sequential
This refers to the task environment of the agent. If each task that the agent must perform does not rely upon past performance, and will not effect future performance then it is episodic. Otherwise it is sequential.
Static & dynamic
A static environment, as the name suggests, is one that does not change from one state to the next while the agent is considering its course of action. In other words, the only changes to the environment are those caused by the agent itself. A dynamic environment can change, and if an agent does not respond in a timely manner, this counts as a choice to do nothing.
Discrete & continuous
This distinction refers to whether or not the environment is composed of a finite or infinite number of possible states. A discrete environment will have a finite number of possible states, however, if this number is extremely high, then it becomes virtually continuous from the agents perspective.
Single-agent & multiple agent
An environment is only considered multiple agent if the agent under consideration must act cooperatively or competitively with another agent to realise some tasks or achieve goal. Otherwise another agent is simply viewed as a stochastically behaving part of the environment.
Overview of environments
| rising order of complexity → | |
| Observable | Partially observable |
| Deterministic | Stochastic |
| Episodic | Sequential |
| Static | Dynamic |
| Discrete | Continuous |
| Single-agent | Multiple agent |
Hierarchies of agents
To actively perform their functions, Intelligent Agents today are normally gathered in a hierarchical structure containing many “sub-agents”. 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
See also
- Agents
- Cognitive architectures
- Cognitive radio - a practical field for implementation
- Cybernetics, Computer science
- Data mining agent
- Embodied agent
- Federated search - the ability for agents to search heterogeneous data sources using a single vocabulary
- Fuzzy agents - IA implemented with adaptive fuzzy logic
- Intelligence
- Intelligent system
- Multi-agent system and multiple-agent system - multiple interactive agents
- Reinforcement learning
- Semantic Web - making data on the Web available for automated processing by agents
- Simulated reality
- Social simulation
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. ^ Adam Maria Gadomski (1997); Agent and Intelligence; on the server of Italian Research Agency .
3. ^ 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
4. ^ 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
5. ^ N. Kasabov, Introduction: Hybrid intelligent adaptive systems. International Journal of Intelligent Systems, Vol.6, (1998) 453-454.
External links
- Intelligent Agent - from MIT Encyclopedia
- Bridgeport
- Coneural
- Research article that describes how Foresight could be integrated into capital budgeting with Intelligent Agents and fuzzy logic
Wikipedia content modification information:
- This page was last modified on 24 October 2008, at 20:58.
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