what is intelligent agents

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 What is intelligent agents, how intelligent agents support knowledge base system. Types of intelligent agent s. In artificial intelligence , an intelligent agent (IA) is an autonomous  entity which observes through sensors and acts upon an  environment  using actuators (i.e. it is an  agent) and directs its activity towards achieving goals (i.e. it is  rational ). [1]  Intelligent agents may also  learn or use knowledge  to achieve their goals. They may be very simple or  very complex: a reflex machine such as a thermostat is an intelligent agent , [2]  as is a human being, as is a community of human beings working together towards a goal. Simple reflex agent 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) [citation needed ] 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) ) considered goal-directed behavior as the essence of intelligence and so prefer a term borrowed from economics , "rational agent ". Intelligent agents in artificial intelligence are closely related to  agents in economics , and versions of the intelligent agent paradigm are studied in  cognitive science , ethics, the philosophy of 

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5/17/2018 What is Intelligent Agents - slidepdf.com

http://slidepdf.com/reader/full/what-is-intelligent-agents 1/14

 

What is intelligent agents, how intelligent agents support knowledge base system.

Types of intelligent agents.

In artificial intelligence, an intelligent agent (IA) is an autonomous entity which observesthrough sensors and acts upon an environment using actuators (i.e. it is an agent) and directs its

activity towards achieving goals (i.e. it is rational).[1]

 Intelligent agents may also learn or use

knowledge to achieve their goals. They may be very simple or very complex: a reflex machine

such as a thermostat is an intelligent agent,[2]

 as is a human being, as is a community of humanbeings working together towards a goal.

Simple reflex agent

Intelligent agents are often described schematically as an abstract functional system similar to acomputer program. For this reason, intelligent agents are sometimes called abstract intelligent

agents (AIA)[citation needed ]

to distinguish them from their real world implementations as computersystems, 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)) considered goal-directed behavior as the essence of intelligence and soprefer a term borrowed from economics, "rational agent".

Intelligent agents in artificial intelligence are closely related to agents in economics, and versionsof the intelligent agent paradigm are studied in cognitive science, ethics, the philosophy of 

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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 carries out tasks on behalf of 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 agentby 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".

Contents

[hide] 

  1 A variety of definitions 

  2 Structure of agents 

  3 Classes of intelligent agents 

o  3.1 Other classes of intelligent agents 

  4 Hierarchies of agents 

  5 Applications 

  6 See also 

  7 Notes 

  8 References 

  9 External links 

[edit] A variety of definitions

Intelligent agents have been defined many different ways.[3] According to Nikola Kasabov[4] IA

systems should exhibit the following characteristics:

  accommodate new problem solving rules incrementally

  adapt online and in real time 

  be able to analyze itself  in terms of behavior, error and success.

  learn and improve through interaction with the environment (embodiment) 

  learn quickly from large amounts of  data 

  have memory-based exemplar storage and retrieval capacities

  have parameters to represent short and long term memory, age, forgetting, etc.

[edit] Structure of agents

A simple agent program can be defined mathematically as an agent function[5] 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:

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Agent function is an abstract concept as it could incorporate various principles of decision

making like calculation of  utility of individual options, deduction over logic rules, fuzzylogic, etc.[6] 

The program agent, instead, maps every possible percept to an action.

We use the term percept to refer to the agent's perceptional inputs at any given instant. In thefollowing figures an agent is anything that can be viewed as perceiving its environment

through sensors and acting upon that environment through actuators.

[edit] Classes of intelligent agents

Simple reflex agent

Model-based reflex agent

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A general learning agent

Russell & Norvig (2003) group agents into five classes based on their degree of perceivedintelligence and capability:

[7] 

1.  simple reflex agents

2.  model-based reflex agents

3.  goal-based agents4.  utility-based agents

5.  learning agents

Simple reflex agents

Simple reflex agents act only on the basis of the current percept, ignoring the rest of thepercept history. The agent function is based on the condition-action rule: if condition then

action.

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.

Infinite loops are often unavoidable for simple reflex agents operating in partially observable

environments. Note: If the agent can randomize its actions, it may be possible to escape frominfinite loops.

Model-based reflex agents

A model-based agent can handle a partially observable environment. Its current state isstored inside the agent maintaining some kind of structure which describes the part of the

world which cannot be seen. This knowledge about "how the world works" is called a model

of the world, hence the name "model-based agent".

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A model-based reflex agent should maintain some sort of  internal model that depends on the

percept history and thereby reflects at least some of the unobserved aspects of the currentstate. It then chooses an action in the same way as the reflex agent.

Goal-based agents

Goal-based agents further expand on the capabilities of the model-based agents, by using"goal" information. Goal information describes situations that are desirable. This allows the

agent a way to choose among multiple possibilities, selecting the one which reaches a goal

state. Search and planning are the subfields of artificial intelligence devoted to finding action

sequences that achieve the agent's goals.

In some instances the goal-based agent appears to be less efficient; it is more flexible

because the knowledge that supports its decisions is represented explicitly and can bemodified.

Utility-based agents

Goal-based agents only distinguish between goal states and non-goal states. It is possible todefine 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. A more

general performance measure should allow a comparison of different world states according

to exactly how happy they would make the agent. The term utility, can be used to describehow "happy" the agent is.

A rational utility-based agent chooses the action that maximizes the expected utility of theaction outcomes- that is, the agent expects to derive, on average, given the probabilities and

utilities of each outcome. A utility-based agent has to model and keep track of itsenvironment, tasks that have involved a great deal of research on perception, representation,reasoning, and learning.

Learning agents

Learning has an advantage that it allows the agents to initially operate in unknownenvironments and to become more competent than its initial knowledge alone might allow.

The most important distinction is between the "learning element", which is responsible for

making improvements, and the "performance element", which is responsible for selecting

external actions.

The learning element uses feedback from the "critic" on how the agent is doing and

determines how the performance element should be modified to do better in the future. Theperformance element is what we have previously considered to be the entire agent: it takes in

percepts and decides on actions.

The last component of the learning agent is the "problem generator". It is responsible for

suggesting actions that will lead to new and informative experiences.

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[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:

  Decision Agents (that are geared to decision making);

  Input Agents (that process and make sense of sensor inputs  – e.g. neural network  basedagents);

  Processing Agents (that solve a problem like speech recognition);

  Spatial Agents (that relate to the physical real-world);

  World Agents (that incorporate a combination of all the other classes of agents to allowautonomous behaviors).

  Believable agents - An agent exhibiting a personality via the use of an artificial character

(the agent is embedded) for the interaction.

  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.

[edit] Hierarchies of agents

 Main article:  Multi-agent system 

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 acomplete system that can accomplish difficult tasks or goals with behaviors and responsesthat display a form of intelligence.[citation needed ] 

[edit] Applications

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An example of an automated online assistant providing automated customer service on awebpage.

Intelligent agents are applied as automated online assistants, where they function to perceivethe needs of customers in order to perform individualized customer service. Such an agent

may basically consist of a dialog system, an avatar, as well an expert system to provide

specific expertise to the user.[8]

 

[edit] See also

  Agent (disambiguation) 

  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 asingle vocabulary

  Fuzzy agents  – IA implemented with adaptive fuzzy logic 

  GOAL agent programming language 

  Intelligence 

  Intelligent system 

  JACK Intelligent Agents 

  Multi-agent system and multiple-agent system  – multiple interactive agents  PEAS classification of an agent's environment

  Reinforcement learning 

  Semantic Web  – making data on the Web available for automated processing by agents

  Simulated reality 

  Social simulation 

[edit] Notes

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1.  ^ Russell & Norvig 2003, chpt. 2

2.  ^ According to the definition given by Russell & Norvig (2003, chpt. 2), the most popular AI

textbook.

3.  ^ Some definitions are examined by Franklin & Graesser 1996 and Kasobov 1998. 

4.  ^ Kasobov 1998 

5.  ^ Russell & Norvig 2003, p. 33

6.  ^ Salamon, Tomas (2011).  Design of Agent-Based Models. Repin: Bruckner Publishing.pp. 42 – 59. ISBN 978-80-904661-1-1. http://www.designofagentbasedmodels.info/ . 

7.  ^ Russell & Norvig 2003, pp. 46 – 54

8.  ^ Providing Language Instructor with Artificial Intelligence Assistant . By Krzysz

 

2. 

3. 

 

4. 

 

5. 

 

6. 7.   Author: Chathra Hendahewa 

8.  Chathra Hendahewa is a graduate student at Rutgers –The State University of New Jersey  since fall 2008

 

and currently in her second year of studies. She is studying towards her Doctorate in Computer Science and

her major research interests are Pattern Classification with application to financial systems, Machine

Learning and Cognitive Sciences. She was the first ever International Fulbright Science &Technology 

 

awardscholar from Sri Lanka. Chathra graduated from Faculty of Information Technology, University of 

Moratuwa in year 2007. She is also a passed finalist and a multiple gold medalist of CIMA (Chartered

Institute of Management Accountants –UK). She has professional experience in working as a Business

Systems Analyst for more than a year at IFS R&D and has completed an internship period in developing

modules for‘Sahana-Disaster Management System’. She loves reading biographies, mysteries and science

fiction and listening to music during her free time.

9. 10.  Types of Intelligent Agents11.  11/25/2009 3:28 am By Chathra Hendahewa | Articles: 11

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12.  Last month, we looked at how to link different environments to agents, agent functions and agent

programs. Now, moving forward in the area of intelligent agents let’s understand what are the different

agent types and how they differ from each other type in today’s article. There are mainly fi ve different

types of agents which we can group most of the intelligent agents found nowadays. We would start off 

 with the simplest type and go on to define the most sophisticated agent type which is ultimately the

most desired type of agents in the context of the real life.13. 14. 1.Simple reflex agents

15.  This the simplest type of agent architecture possible. The underlying concept is very simple and lacks

much intelligence. For each condition that the agent can observe from its sensors based on the changes

in the environment in which it is operating in, there is a specific action(s) defined by an agent program.

So, for each observation that it receives from its sensors, it checks the condition-action rules and find

the appropriate condition and then performs the relevant action defined in that rule using its

actuators. This can only be useful in the cases that the environment in fully observable and the agent

program contains all the condition-action rules possible for each observance, which is somewhat not

possible in real world scenarios and only limited to toy simulation based problems. Figure 1depicts the

underlying concept in such a simple reflex type agent.

16. 17.  Figure 1 – Simple reflex agent

18.  In the above case, the agent program contains a look-up table which has been constructed prior to the

agent being functional in the specific environment. The look-up table should consist of all possible

percept sequence mappings to respective actions. (In the case you require to refresh your memory 

about the concept of look-up table, please refer the last month’s article where there is a last section

describes it in detail). Thus based on the input that the agent receives via the sensors (about the

current state of the environment), the agent would access this look-up table and retrieve the respective

action mapping for that percept sequence and inform its actuators to perform that action. This process

is not very effective in a scenario where the environment is constantly changing while the agent is

taking the action because, the agent is acting on a percept sequence that it acquired previously to the

rapid change in the environment and therefore the performed action might not suit the environment’s

state after the change.

19. 20. 2.Model based reflex agents

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21.  This is a more improved version of the first type of agents with the capability of performing an action

 based on how the environment evolves or changes from the current state. As in all agent types, model

 based reflex agents also acquire the percepts about the environment through its sensors. These

percepts would provide the agent with the understanding of what the environment is like now at that

moment with some limited facts based on its sensors. Then the agent would update the internal state of 

the percept history and thus would yield some unobserved facts about the current state of theenvironment. To update the internal state, information should exist about how the world

(environment) evolves independently of the agent’s actions and information about how the agent’s

actions eventually affect the environment. This idea about incorporating the knowledge of evolvement

of the environment is known as a model of the world. This explains how the name Model based was

used for this agent type.

22. 23.  Figure 2 –Model based reflex agent

24.  The above diagram shows the architecture of a Model based reflex agent. Once the current percept is

received by the agent through its sensors, the previous internal state stored in its internal state sectionin connection with the new percepts determines the revised description about the current state.

Therefore, the agent function updates its internal state every time it receives a new percept. Then based

on the new updated percept sequence based on the look-up table’s matching with that entry would

determine what action needs to be performed and inform the actuators to do so.

25. 26. 3.Goal based agents

27.  This agent is designed so that it can perform actions in order to reach a certain goal. In any agent the

main criteria is to achieve a certain objective function which can in layman’s terms referred to as a

goal. Therefore, in this agent type goal information is defined so that is can determine which suitable

action or actions should be performed out of the available set of actions in order to reach the goal

effectively. For example, if we are designing an automated taxi driver, the destination of the passenger

(which would be fed in as a goal to reach) would provide him with more useful insight to select the

roads to reach that destination. Here the difference with the first two types of agents is that it does not

have hard wired condition-action rule set thus the actions are purely based on the internal state and

the goals defined. This sometimes might lead to less effectiveness, when the agent does not explicitly 

know what to do at a certain time but it is more flexible since based on the knowledge it gathers about

the state changes in the environment, it can modify the actions to reach the goal.

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28. 29.  Figure 3 –Goal based agent

30. 31. 4.Utility based agents

32.  Goals by themselves would not be adequate to provide effective behavior in the agents. If we consider

the automated taxi driver agent, then just reaching the destination would not be enough, but

passengers would require additional features such as safety, reaching the destination in less time, cost

effectiveness and so on. So in order to combine goals with the above features desired, a concept called

utility function is used. So based on the comparison between different states of the world, a utility 

 value is assigned to each state and the utility function would map a state (or a sequence of states) to a

numeric representation of satisfaction. So, the ultimate objective of this type of agent would be to

maximize the utility value derived from the state of the world. The following diagram depicts the

architecture of a Utility based agent.

33. 

 

34.  Figure 4 –Utility based agent

35. 36. 5.Learning agents

37.  In the study of AI, we are mostly interested in finding ways of mimicking human behavior or intelligent

in agents. Learning is one of the major areas where human intelligence is based upon. Therefore, it is

important to see how we can incorporate learning in the intelligent agents to achieve a certain task 

more effectively. This type of learning agents has four conceptual components built in. One is the

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learning element which is the component responsible for making improvements to the existing

knowledge. The second component is the critic which gives the feedback to the learning element based

on the defined performance standard. First the agent’s sensors input the precept sequence received

from its sensors to the critic, which would provide some feedback based on the received percept

sequence and the performance standard required to achieve. The third element is the performance

element which is responsible for handling and determining the external actions to be performed by theactuators of the agent. This part interacts with the learning element to and ultimately determines what

action to perform based on the evolved knowledge. The other component is called the problem

generator whose job is to propose actions that would lead to new and informative experiences. This

helps the agent to explore beyond the usual horizon and try out new actions which would let it learn

more knowledge. In the long run, the learning components which act together and interact would be

able to know what actions are better and what actions are worse based on the environment and

eventually would lead to improves overall performance. The typical structure of a learning agent is

shown in figure 5.

38. 

 

39.  Figure 5 –Learning agent

40. 41.  Wrapping up

42.  In today’s article we discussed about the various ty pes of agents that can be built to achieve a certain

task. Starting off with simple reflex agents we went in to more complex ideas and complex structures in

learning agents. I hope you got some idea about the different types of agents that can be built and it

 would be worthy to know that it is not required always to construct the most complex structure. Insome simple domains, the less complex agent types would be able to perform well, although in real life

scenarios the emphasis should be with learning agents. In the next article we would talk a little bit

more about agents are then move on to problem solving through searching which is one of the key 

topics in AI problems. Hope you all enjoyed reading the article series on AI and have developed an

interest in this field throughout the year of 2009. We hope you would be looking forward to the

continuation of this article series in the coming year as well. Best wishes for a Merry Christmas and

Happy New Year!!!!

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43. 44. References

45.  Artificial Intelligence – A modern Approach, Second edition, Stuart Russell & Peter Norvig

46.  Previous Article 

47. 49.  node 522

 

 

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