what is intelligent agents
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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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Model-based, goal-based agent
Model-based, utility-based 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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