INTELLIGENT SOFTWARE AGENTS

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W12.1

INTELLIGENT

SOFTWARE

AGENTS

Intelligent agents (IAs) represent a relatively new technology with the poten-tial to become one of the most important tools of information technology in the twenty-first century (see Murch and Johnson, 1999, Wooldridge, 2000; D’Inverno and Luck 2001; and Bigus et al., 2001). IAs can alleviate the most critical limitation of the Internet—information overload—and facilitate elec-tronic commerce. Before we look at their capabilities, let us determine what we mean by intelligent agents.

As indicated in Chapter 4, intelligent agents are known by several names. Notable are: software agents, wizards, knowbots, and softbots. The names sometime reflect the nature of the agent. The most common names are intelligent and software agents. As stated in Chapter 4, most agents are not intelligent and should be referred to as software agents. However, the term intelligent agent is more popular. We will use both terms interchangeably. Note that the term agent

is derived from the concept of agency, referring to employing someone to act on your behalf.

There are several definitions of what an intelligent agent is. Each reflects the definer’s perspective. Here are two examples:

1. Intelligent agentsare software entities that carry out some set of operations on behalf of a user or another program, with some degree of independence or autonomy, and in so doing, employ some knowledge or representation of the user’s goals or desires. They do so to accomplish a task or a goal (e.g., see Levesque and Lakemeyer, 2001).

2. Autonomous agentsare computational systems that inhabit some complex dy-namic environment, sense and act autonomously in this environment, and by doing so realize a set of goals or tasks for which they are designed (Maes et al., 1999).

While there is no single commonly accepted definition for the term “intelligent agent,” there are several traits or abilities that many people think of when they are discussing intelligent agents. These are summarized in Table W12A.1.1. Note that certain agents lack some of these characteristics.

The following are nine major tasks that can be performed by IAs.

1. Information access and navigationare today’s major applications of intelli-gent aintelli-gents (see Chapter 4).

2. Decision support and empowerment. Knowledge workers need increased support, especially in decision making. IA can facilitate decision making and empower employees, as shown in the IT at Work below and in Huhns and Buell, 2002.

3. Repetitive office activity. There is a pressing need to automate tasks per-formed by administrative and clerical personnel in functional areas, such as sales or customer support, to reduce labor costs and increase office produc-tivity. Today, labor costs are estimated to be as much as 60 percent of the to-tal cost of information delivery, and intelligent agents can help reduce those costs by eliminating some of the need for human labor or by expediting the work, so less labor is needed. (See Perez, 2002).

TABLE W12A.1.1 Characteristics of Intelligent Agents Characteristic Autonomous Proactive response Unobstructive Modular

Dedicated and automated

Interactive

Friendly and dependable

Able to learn

Description

Capable of acting on its own, being goal-oriented and collaborative, able to alter its activity if needed (see Maes et al., 1999).

The agents’ response must be corrective (i.e., they must exhibit goal-directed behavior by taking the initiative).

Must work without constant attention of its “master”; may be offsite (remote executions). Transportable across different systems and networks.

Many agents are not mobile (e.g., Wizards in spreadsheets).

Usually designed to carry on a specific, usually repetitive, normally difficult task. Multifaceted jobs need a multiagent system.

Designed to interact with human agents or software programs (see opening case). This is critical for a multiagent system.

Conditional processing, practice. Using rule-based or pattern-matching logic (supplied by the user), the agent can make decisions in choosing contexts in which they perceive changes in the environment or can send alerts to the user in a timely manner. To be effective, must be believable and exhibit easy

interactivity with people.

Only a few agents can really do some learning, for example, observing the user and making predic-tions on his or her future behavior. Agent must be highly autonomous.

4.Mundane personal activity.In a fast-paced society, time-strapped individuals need new ways to minimize the time spent on routine personal tasks like booking airline tickets so that they can devote more time to professional ac-tivities. One specific form of intelligent agents is the voice-activated interface agent that reduces the burden on the user of having to explicitly command the computer. Agent can be in wearable devices as well (Starner, 2002). 5.Search and retrieval. It is not possible to directly manipulate a distributed

database system in an electronic commerce setting with millions of data ob-jects. Users will have to delegate the tasks of searching and of cost compar-ison to agents. These agents perform the tedious, time-consuming, and repet-itive tasks of searching databases, retrieving and filtering information, and delivering results to the user (see Chapter 4). For knowledge discovery see Shan et al., (2003).

6.Domain experts. It is advisable to model costly expertise and make it widely available. “Expert” software agents could be models of real-world agents, such as translators, lawyers, diplomats, union negotiators, stockbrokers, and even clergy.

7.Mobile agents. Some agents may be either static, residing on the client machine to manage a user interface, for instance, or mobile. Mobility is the

degree to which the agents themselves travel through the network. Mobile agents can move from one Internet site to another and can send data to and retrieve data from the user, who can focus on other work in the meantime. This can be very helpful to users. An example of a mobile agent is one that travels from site to site, looking for information on a certain stock as instructed by the user. When the stock price hits a certain level, or if there is news about the stock, the agent alerts the user. What is unique about a mobile agent is that it is a software application that moves on its own to dif-ferent computers to execute various tasks. (For details see Menczer et al., 2002).

8. Clerical and management activities. Intelligent agents can even be used to assist clerks, professional staff, and managers in performing their activities. Some tasks that an agent can do are: advise, alert, broadcast, browse, cri-tique, distribute, enlist, empower, explain, filter, guide, identify, match, moni-tor, navigate, negotiate, organize, present, query, report, remind, retrieve, schedule, search, secure, solicit, sort, store, suggest, summarize, teach, translate, and watch.

9. Supply Chain Agents.With the growing interest in automating supply chain activities come the interest in helping agents. For details see Kimbrough et al., 2002 and Kim and Lee (2003).

We described some intelligent agent applications in Chapters 4, 5, and 8. Here are additional applications:

User Interface. For many users, a graphical user interface has been consid-ered difficult to learn and use, especially its nonroutine functions. As capabili-ties and applications of computers improve, the user interface needs to accom-modate the increases in complexity. Intelligent agents can help with both these

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people mix and match what they like within the con-straints of what is available and how much they can use. The management of fringe benefits is a very resource-intensive process, especially when thousands of employees are involved. Nike and Signet Bank both installed special software that empowers employees to directly manage their fringe benefits selections. Employees access the hu-man resources databases by computer and conduct activi-ties such as selecting and changing benefits or making charitable contributions through payroll deductions.

The software agent that supports these activities is called Electronic Workforce (from Edify Corp., edify.com/products). It enables employers to delegate to any employee sup-ported by a computer some time-consuming and repetitive tasks that were previously conducted by human resources

(HR) employees. Employees enter and delete data, com-mand the computer to perform certain transactions, and interpret information. If they make mistakes or request benefits they are not eligible for, the agent automatically alerts them to the problem. Previously, paperwork would have to be routed to an employee for corrections and then back to the HR department. The use of the agents enables companies to increase benefits options, and employee sat-isfaction, with the same or even fewer human resources employees.

Source:Compiled from Edify Corporation, edify.com/products(2001).

For Further Exploration:Can you imagine what would happen if there were no IAs to help in the choosing of ben-efits? How can an agent know that an employee made a mistake?

IT

at Work

Networked Intelligent Agents

problems. Intelligent agent technology allows systems to monitor the user’s actions, develop models of user abilities, and automatically help out when inter-face problems arise.

Of special interest is Microsoft’s animated family of agents (e.g., Merlin, Robby, Genie, and Peedy). They can appear in Web pages, displaying everything from simple idling activities such as yawning to attention-grabbing motions. Thus, even social interaction can be incorporated with the user interface. (For details see microsoft.com/msagent; argolink.com/agent; and Barker, 1998).

Operating Systems Agents.Agents can assist in the use of operating systems. For example, Microsoft Corp. has several systems agents (called Wizards) in its NT operating system. Some reside on the NT server, while others are on the workstations. These agents assist in the following tasks: add user accounts, man-age file and folder access, add printer, add/remove programs, obtain licenses, and install new modems. They also assist with group management and network-client administration. (For details, consult microsoft.com.)

Spreadsheet Agents. Spreadsheet agents make the software more friendly. An example of an intelligent agent is the Wizard feature found in Microsoft’s Excel. The Wizard is a built-in package capability that “watches” users and offers suggestions as they attempt to perform tasks by themselves. For example, sup-pose you are trying to format a group of spreadsheet cells or locations in a par-ticular manner. If you are not skilled at using the spreadsheet package, you might try to format each cell individually. A much faster method is to select the entire group of designated cells and then conduct the format once for all the selected locations.

Suppose a friend of yours was watching you format the cells and noted that you worked on the individual cells. Suppose further that your friend was more of an expert on the software package than you were. Presumably, he or she would tell you that you were formatting unproductively. In a similar sense, the Wizard can detect your laborious, repetitive attempts and notify you that there is a better way, or even take the next step and offer to complete the remain-der of the formatting task for you.

Workflow and Task Management Agents. Administrative management includes both workflow management and areas such as computer/telephone integration, where processes are defined and then automated. In these areas, users not only need to make processes more efficient, but also need to reduce the cost of human agents. Intelligent agents can be used to ascertain, then auto-mate, user wishes or business processes.

Negotiation and Shopping Helpers in Electronic Commerce. A challenging system is one in which agents need to negotiate with each other. Such sys-tems are especially applicable to electronic commerce. Consider the vacation arrangement episode in the opening case. There, the scenario can be extended to one in which the user’s agent will negotiate the best price for the car, hotel, airfare, and so on. For details see Zahir (2002), Lee et al. (2002) and Yan et al. (2001).

Although in 2003 most agents were working individually, there is a growing trend to have groups of agents working together, usually in a networked envi-ronment. This is called a multiagent system, an example of which follows:

Intelligent Agents Trim Papermaking Costs. Madison Paper Industries, a 282-employee company located in Maine, was struggling to compete against

larger papermakers. Costs of transportation from suppliers and to customers seemed to be high, paper loss during production was cutting into profits, and scheduling work was difficult and lengthy. A multiagent system, initially devel-oped at Carnegie Mellon University (cs.smu.edu/~softagents/mas_interop.html) and commercialized by IBM’s cooperative decision support group (research.ibm.com/ coopds), was implemented in an attempt to solve the problem. The knowledge for the system was solicited from human schedulers whose experience over the years created a pool of candidate scheduling solutions. A multi-agent negotiation system is decribed by Yan et al. (2001).

The agent-based system evaluates each of the solutions in light of multiple business objectives (cost, speed of delivery, and so on). The human schedulers now work with the system interactively, posting “what-if” questions to help find a solution that best fits a set of multiple business objectives that frequently con-flict with each other. Working as an intelligent assistant, the system frees the schedulers of real-time computational tasks, giving them time to concentrate on more important tasks.

Traditional approaches to scheduling in the paper industry have involved scheduling each process independently, often using different software packages for each step. Because of the lack of interaction between applications, the com-bined schedules have been less than satisfactory in the past. They might, for instance, minimize trim waste at the expense of an inefficient, costly vehicle-loading schedule. In contrast, IBM’s networked IA approach simultaneously considers numerous scheduling objectives and multiple manufacturing and dis-tribution stages in a global multicriteria optimizing framework. The system cut paper trim losses by about 6 tons per day at Madison Paper, as well as 10 per-cent of freight costs, for annual savings of more than $5 million. (Sources: Com-piled from IEEE Intelligent Systems, March–April 1999, and from the Carnegie Mellon University and IBM Web sites, March 2000.)