Citizendia
Your Ad Here

Simple reflex agent
Simple reflex agent
Learning agent
Learning agent

The terms "agent" and "intelligent agent" are used in two different but related senses which are often confused.

In computer science, an intelligent agent (IA) is a software agent that assists users and will act on their behalf, in performing non-repetitive computer-related tasks, in the sense of a "representative agent", like an insurance agent or travel agent. Computer science (or computing science) is the study and the Science of the theoretical foundations of Information and Computation and their In Computer science, a software agent is a piece of software that acts for a user or other program in a relationship of agency. For other uses of the word Agent see Agent (disambiguation This is correct An Agent in Commercial Law is a person who is authorised Intelligent agents are used for operator assistance or data mining (sometimes referred to as bots). While they are often based on fixed pre-programmed rules, "intelligent" in this context is often taken to imply the ability to adapt and learn.

In artificial intelligence, an intelligent agent is used for intelligent actors which observe and act upon an environment, in the sense of a rational agent: an entity that is capable of perception, action and goal directed behavior. A rational agent is an Agent which takes actions based on Information from and Knowledge about the agent's environment Such an agent might be a robot or an embedded real time software system - and is intelligent if it interacts with its environment in a manner that would normally be regarded as intelligent if that interaction were carried out by a human being.

There is no reason why these two notions of intelligent agent need to be related. An intelligent agent in the first sense might be implemented using conventional software techniques and display no more intelligence than a conventional computer program. On the other hand, an intelligent agent in the second sense might be wholly autonomous, carrying out its own agenda, and acting as an agent for no one.

Contents

Intelligent agents in artificial intelligence

See also: rational agent and agent environment

The academic community has been very active in defining the concept of agents [1]. A rational agent is an Agent which takes actions based on Information from and Knowledge about the agent's environment Practically the concept of agents is associated to software entities which have some ability: to perceive (or sense) the environment; to act on the environment; to have some level of autonomy (via a set of internal goals).

In the artificial intelligence sense of the term, there are multiple types of agents and sub-agents. For example:

  1. Physical Agents - A physical agent is an entity which percepts through sensors and acts through actuators.
  2. 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.
  3. 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:

  1. simple reflex agents;
  2. model-based reflex agents;
  3. goal-based agents;
  4. utility-based agents;
  5. learning agents.

1. 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.

2. 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.

3. 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.

4. 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.

5. Learning agents

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[2] IA systems should exhibit the following characteristics:

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.

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:

  1. Temporal Agents (for time-based decisions);
  2. Spatial Agents (that relate to the physical real-world);
  3. Input Agents (that process and make sense of sensor inputs - example neural network based agents neural network);
  4. Processing Agents (that solve a problem like speech recognition);
  5. Decision Agents (that are geared to decision making);
  6. Learning Agents (for building up the data structures and database of other Intelligent agents);
  7. World Agents (that incorporate a combination of all the other classes of agents to allow autonomous behaviors). Traditionally the term neural network had been used to refer to a network or circuit of biological neurons.

Intelligent agents in computer science

A very limited set of agents, that might be classified as semi-intelligent due to their lack of complexity, decision making, extremely limited world view and learning capacity can be found in the reference: Third Canadian Edition of "Management Information Systems for the Information Age". This document suggests that there are only four essential types of Intelligent Agents:[3]

  1. Buyer agents or shopping bots
  2. User or personal agents
  3. Monitoring-and-surveillance agents
  4. Data Mining agents

1. Buyer Agent[4]

Buyer agents travel around a network (i. e. the internet) retrieving information about goods and services. These agents, also known as 'shopping bots', work very efficiently for commodity products such as CDs, books, electronic components, and other one-size-fits-all products. Amazon. com is a good example of a shopping bot. The website will offer you a list of books that you might like to buy on the basis of what you're buying now and what you have bought in the past.

2. User or Personal Agents

User agents, or personal agents, are intelligent agents that take action on your behalf. In this category belong those intelligent agents that already perform, or will shortly perform, the following tasks:

3. Monitoring and Surveillance Agents[5]

These agents, also known as "predictive agents", are intelligent agents that observe and report on equipment. Monitoring and surveillance agents (also known as predictive agents are a type of Intelligent agent software that observes and reports on computer equipment For example, NASA's Jet Propulsion Laboratory has an agent that monitors inventory, planning, and scheduling equipment ordering to keep costs down, as well as food storage facilities. These agents usually monitor complex computer networks that can keep track of the configuration of each computer connected to the network.

4. Data Mining Agents

A data mining agent operates in a data warehouse discovering information. A 'data warehouse' brings together information from lots of different sources. 'Data mining' is the process of looking through the data warehouse to find information that you can use to take action, such as ways to increase sales or keep customers who are considering defecting. 'Classification' is one of the most common types of data mining, which finds patterns in information and categorizes them into different classes. Data mining agents can also detect major shifts in trends or a key indicator and can detect the presence of new information and alert you to it.

See also

References

  1. ^  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
  2. ^  N. A cognitive architecture is a blueprint for Intelligent agents It proposes (artificial Computational processes that act like certain cognitive systems most often Cognitive radio is a paradigm for Wireless Communication in which either a network or a Wireless node changes its Cybernetics is the interdisciplinary study of the Structure of Complex systems especially Communication processes control mechanisms and Feedback Computer science (or computing science) is the study and the Science of the theoretical foundations of Information and Computation and their A data mining agent is a software program built for the primary purpose of finding information efficiently In Artificial intelligence, an embodied agent is an Intelligent agent that interacts with the environment through a physical body within that environment Federated search is the simultaneous search of multiple online databases and is an emerging feature of automated Web-based Library and Information retrieval In Computer science a fuzzy agent is a Software agent that implements Fuzzy logic. Fuzzy logic is a form of Multi-valued logic derived from Fuzzy set theory to deal with Reasoning that is approximate rather than precise Intelligence (also called intellect) is an Umbrella term used to describe a property of the Mind that encompasses many related abilities such as the capacities A multi-agent system ( MAS) is a system composed of multiple interacting Intelligent agents Multi-agent systems can be used to solve problems which are difficult or An agent-based model (ABM is a Computational model for simulating the actions and interactions of autonomous individuals in a network with a view to assessing their Inspired by related psychological theory in Computer science, reinforcement learning is a sub-area of Machine learning concerned with how an agent The Semantic Web is an evolving extension of the World Wide Web in which the Semantics of information and services on the web is defined making it possible for the Simulated reality is the proposition that Reality could be simulated—perhaps by Computer simulation —to a degree indistinguishable from "true" Reality Social simulation is the modeling or Simulation, normally performed using a computer, of social phenomena (e Kasabov, Introduction: Hybrid intelligent adaptive systems. International Journal of Intelligent Systems, Vol. 6, (1998) 453-454.
  3. ^  3, 4, 5 Haag, Stephen. "Management Information Systems for the Information Age", 2006. Pages 224-228

External links

© 2009 citizendia.org; parts available under the terms of GNU Free Documentation License, from http://en.wikipedia.org
 Dapyx Software network: MP3 Explorer | Ebook Manager | Zenithic