Chapter
14
Intelligent Agents
Chapter Objectives
Explain how intelligent agents are computational entities capable of autonomously achieving
goals by executing needed actions.
Summarize the general characteristics of intelligent agents.
List the different types of intelligent agents.
Describe how data mining models, expert systems, and intelligent agents can be collectively
applied to find solutions to difficult problems.
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CD-59
In Chapter 12 we defined an intelligent agent as a computational entity capable of autonomously
achieving goals by executing needed actions. In Section 14.1 of this
chapter we describe several common characteristics of intelligent agents. In Section
14.2 we categorize intelligent agents by the functions they perform. Finally, in Section
14.3, we describe how intelligent agents, data mining models, and expert systems can
be combined to form intelligent systems for solving difficult problems.
14.1 Characteristics of Intelligent Agents
Each day we encounter situations that require us to allocate time and energy to tasks
we could just as well delegate to someone able to act on our behalf. This individual
would know our likes, dislikes, needs, and desires and would be able to learn and
modify his or her behavior as our needs change over time.A few of the tasks carried
out by such a person might include finding a best price for a product we wish to purchase,
notifying us when our favorite author has just published a new book, buying
and selling stocks for our stock portfolio, and finding us a date for Saturday night.We
can only imagine the salary required to obtain and keep the services of such a useful
person! Even if we paid top dollar, few individuals would be able to make this type of
commitment on our behalf.
Unlike human beings, computer systems are tireless entities that do not complain
about performing countless sequences of mundane tasks. Because of this, computer
programs are viable candidates for satisfying our wants and wishes. Computer programs
able to assist us with everyday tasks and learn from the information we provide
are known as intelligent agents.
Although there seems to be little general agreement on a precise definition of
what constitutes an intelligent agent, four common agent characteristics have been
identified (Sycara, 1998):
1. Situatedness. Agents receive sensory information from their surroundings
and perform actions based on this information.
2. Autonomy. Agents maintain their own internal state and are able to act
without direct intervention from humans or other agents.
3. Adaptivity. Agents are able to react to a changing environment.Agents with
this characteristic can learn from experience and create goals to be achieved.
4. Sociability. Agents have the ability to confer with other agents and/or humans.
Although few, if any, of today’s intelligent agents possess all of the these characteristics,
computer systems exhibiting one or several of these behaviors have been developed.
Let’s take a closer look at the types of intelligent agents that do exist.
CD-60 Chapter 14
• Intelligent Agents
14.2 Types of Agents
Lewis (1998) categorizes three general types of intelligent agents—anticipatory agents,
filtering agents, and semiautonomous agents.As the term implies, anticipatory agents
attempt to anticipate the intentions of a user.A rudimentary example of an anticipatory
agent is Microsoft Word’s animated paperclip.A more complex anticipatory agent might
do our bidding on Ebay once we provide a product description, bidding criteria (e.g.,
only bid on items sold by someone who has sold previous items through Ebay), and a
maximum offering price. Lewis further groups anticipatory agents into those able to infer
a user’s plan of action and those limited to simple concept learning.A common conceptlearning task is to have an agent use e-mail headers to categorize e-mail messages.
Filtering agents are able to carry the categorization process one step further in
that they can evaluate, prioritize, and delete information such as incoming e-mail messages.
MAXIMS (Lashkari, Metral, and Maes, 1994) and MAGI (Payne and Edwards,
1997) are two systems offering these types of filtering capabilities. Semiautonomous
agents deal with tasks requiring complex sequences of actions.The agent is provided
with a goal to achieve by interacting with the user through a form-based interface.A
typical goal is to have the agent search for an item, such as an airline ticket, which
meets a set of constraints. Once found, the agent notifies the user via e-mail.The user is
then responsible for making the actual ticket purchase.
Haag, Cummings, and McCubbrey (2002) classify intelligent agents into one of
four possible categories. Find-and-retrieve agents are semiautonomous agents that
surf the internet to locate requested products or information. User agents are anticipatory
by nature in that they help individuals perform computer-related tasks.
Monitor-and-surveillance agents watch over Web sites, large-scale networks, auction
sites, and the like to alert users of changes that may be significant enough to
merit immediate action. For example, a business supporting a Web site for selling exercise
equipment might have a monitor agent watch for price changes at competing
Web sites selling similar or identical products. Finally, data mining agents function
within a data warehouse structure to discover changes in business trends. If the agent
finds new information of potential interest to the business, management is altered and
appropriate actions can be set into place.
Case et al. (2001) uses the term proactive to indicate agents able to initiate actions
without specific direction from the user. He describes cooperative agents as
agents that interact with other agents and perform actions based on the results of
the communication. Cooperative agents can be used to build what he describes as
e-communities, which allow members to locate other relevant people and stored
information. In addition, e-communities support kinship, which makes members
feel a sense of belonging to and identifying with a group. Case et al. (2001) also presents
a personal agent framework (PAF) developed by Btexact Technologies that integrates
several types of intelligent agents.
14.2
• Types of Agents CD-61
Durfee (2001) describes coordination as an agent’s ability to choose its actions in
the context of other agents.The purpose of coordination is to achieve some common
goal. As an example, the faculty within the management department of a university
could each have an agent acting on their behalf during the development of next semester’s
class schedule. Individually assigned agents know which courses their associated
faculty member prefers to teach, is willing to teach, and does not wish to teach.
Likewise, preferred teaching times and room assignments are also made available to
each agent. During the scheduling process, individual agents attempt to obtain a best
class schedule based on the desires of their associated faculty member.At the same time,
each agent will likely find it necessary to compromise some aspects of a best schedule
in order to achieve the common goal of developing a semester class schedule.
14.3 Integrating Data Mining, Expert Systems, and Intelligent
Agents
Intelligent agents, data mining models, and expert systems are similar in that they each
use intelligent techniques to solve difficult problems. However, obvious differences
exist between the three approaches.With data mining, the emphasis is on applying induction
to build models that generalize data. Expert systems also build generalized
models, however, the models are constructed by extracting knowledge from one or
more human experts. Intelligent agents and data mining models are alike in that each
share the ability to learn from their environment. Intelligent agents and data mining
models differ in that intelligent agents are goal directed, whereas data mining models
are used to test and create new hypotheses about data.
Intelligent agents and expert systems have at least three marked differences. First
of all, an expert system contains general problem-solving knowledge, whereas the
knowledge processed by an intelligent agent is personalized. Second, where an expert
system passively responds to questions, an intelligent agent acts on its own accord
based on the sensory information it receives from the environment. Finally, where intelligent
agents carry out everyday tasks, expert systems perform high-level functions
within a specialized domain. As the three approaches differ in their intent, the techniques
are complementary and can be used together to build useful models for solving
difficult problems. Let’s combine the three approaches to help us build an automated
system for performing supervised data mining.
Figure 14.1 displays the proposed model. At the center of the model lies a data
mining agent.The agent is responsible for coordinating various data mining sessions.A
natural language interface allows the user to easily interact with the agent.The user is
responsible for presenting the agent with the data to be mined as well as the goals to
be achieved.The user can interact with the agent by making suggestions about model
choice, model parameter settings, and the like at any time.
When presented with a set of data, the agent sends the data to a rule-based domain
analyzer for initial analysis.The domain analyzer returns a summary report indicating
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CD-62 Chapter 14 Intelligent Agents
the location of missing and possibly noisy data.The report also offers summary statistics
for all categorical and numeric attributes. After the domain analysis, the data mining
agent is responsible for cleaning the data and selecting a set of candidate input attributes.
For example, candidate attributes can be selected by applying the genetic attribute
selection algorithm described in Chapter 5. In addition, the agent eliminates
redundant numeric attributes by computing attribute correlational values.
After a set of attributes have been selected, the agent invokes a rule-based model
selector, such as the one described in Chapter 12, to choose a data mining technique.
Once a technique has been chosen, the agent queries a rule-based parameter selector
to obtain parameter settings for the forthcoming data mining session. A sampling of
plausible rules for the parameter selector were offered in Section 12.2.The rule-based
model and parameter selectors are shown in the top portion of Figure 14.1.
With the data mining model and all parameter settings selected, the agent initiates
a data mining session.When the session is complete, the agent evaluates and interprets
the results by examining test set classification correctness, quality of production rules,
as well as any available measures of class quality. If the results satisfy the specified goals,
14.3
• Integrating Data Mining, Expert Systems, and Intelligent Agents CD-63
Figure 14.1
• An agent-based model for data mining
Data Mining Session
Data Mining Agent
Rule-Based
Parameter Selector
Rule-Based
Model Selector
Rule-Based
Domain Analyzer
Natural
Language
Interface
User
Summary Report
Parameter
Settings
Cleaned
&
Transformed
Data
Data
Mining
Tool
Results
Input Data
Previous
Settings
Model
Choice
Attribute
Information
New
Settings
Summary Report
Selected Data
a summary report is written and the process terminates. If the results are not acceptable,
the agent decides on one or more of the following actions:
• Invoke the parameter selector to make suggestions for resetting model parameter values
• Choose a new data mining model
• Modify the selection of input attributes
• Conclude that the domain is not amenable for data mining
If one or more of the first three selections are chosen, the agent makes the changes
described by the choice(s) and initiates a new data mining session.This process continues
until an acceptable result is seen or the agent decides to terminate the process. Finally, to
support agent learning, the agent keeps a record of each data mining session.When the
entire data mining process terminates—either successfully or unsuccessfully—the agent
learns by modifying the confidence values for the rules applied during each mining session.
Rules that lead to improved results have their confidence factors increased, whereas
rules that lead to negative results are given decreased confidence scores.
14.4 Chapter Summary
Several types of intelligent agents have been defined; however, all agents ideally share
four common characteristics. These characteristics include the ability to receive and
act upon information from their environment, the ability to act without direct human
intervention, the skill to react to a changing environment, and the talent to confer
with other agents and/or humans.
Intelligent agents and data mining models are alike in that each share the ability
to learn from their environment. However, intelligent agents are goal directed,
whereas data mining models are used to test and create new hypotheses about data.
Unlike expert systems, designed for high-level problem solving in specialized domains,
intelligent agents contain general problem-solving knowledge for performing
everyday tasks. As data mining models, expert systems, and intelligent agents differ in
their intent, the techniques are complementary and can be used together to build useful
models for solving difficult problems.
14.5 Key Terms
Anticipatory agent. An intelligent agent that attempts to anticipate the intentions
of a user.
Cooperative agent. An agent that interacts with other agents and performs actions
based on the results of the communication.
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CD-64 Chapter 14 Intelligent Agents
Coordination. An agent’s ability to choose its actions in the context of other agents.
Data mining agent. An intelligent agent that resides inside a data warehouse and
attempts to discover interesting business trends.
E-community. An electronic community that allows members to locate other people
and stored information.
Filtering agent. An intelligent agent that is able to categorize and prioritize information.
Find-and-retrieve agent. Semiautonomous agents that surf the internet to locate
requested products or information.
Intelligent agent. Computational entities capable of autonomously achieving goals
by executing needed actions.
Monitor-and-surveillance agent. An intelligent agent that watches Web sites, networks,
and the like and alerts users of significant changes likely to require immediate
action.
Proactive agent. An intelligent agent that initiates actions without direction from
the user.
Semiautonomous agent. An intelligent agent that, when provided with a goal to
achieve, is able to carry out a sequence of tasks to achieve the goal.
User agent. An intelligent agent that helps users with computer-related tasks.
14.6 Exercises
Review Questions
1. Several types of agents were described Section 14.2. For each type of agent,
decide which one of the four common characteristics of intelligent agents
listed in Section 14.1 is the most important agent feature. State your reason
for each selection.
2. Does an agent’s ability to coordinate with other agents require agent cooperation?
Why or why not?
3. List differences and similarities between the purposes and tasks of data mining
models, expert systems, and intelligent agents.
4. Use the find-and-retrieve agent located at www.mysimon.com to search for one
or more items. Have the buyer agent at www.bottomdollar.com search for the
same item(s). Compare the results obtained by the two buyer agents.
14.6
• Exercises CD-65