Please do not remove this page Intelligent Agent Concepts in the Modern Library Dent, Valeda https://scholarship.libraries.rutgers.edu/discovery/delivery/01RUT_INST:ResearchRepository/12643389390004646?l#13643533210004646 Dent, V. (2007). Intelligent Agent Concepts in the Modern Library. In Library Hi Tech (Vol. 25, Issue 1, pp. 108–125). Rutgers University. https://doi.org/10.7282/T3B56H29 This work is protected by copyright. You are free to use this resource, with proper attribution, for research and educational purposes. Other uses, such as reproduction or publication, may require the permission of the copyright holder. Downloaded On 2021/09/30 23:21:33 -0400 Intelligent Agent Concepts in the Modern Library Valeda F. Dent Introduction Agent technology, a sub-field of artificial intelligence, is not a new concept. The idea of the intelligent agent was first introduced by John McCarthy in the mid-1950s, while at MIT. Bradshaw (1997) suggests that it was not until after World War II that the forerunners of the software agent began to emerge. Norman (1997) advances that the “most relevant” predecessors of software agents were things like factory control devices, and automatons that controlled the takeoff, landing and piloting of aircraft. Lewis suggested in 1996 that a shift away from the network operating system to Internet-based network computing was taking place. If true, we are now ten years into this shift, perhaps approaching the tail-end of it. There is no doubt that the advent of the Internet has fueled discussion about the use of agent technology, and supported subsequent implementation on a much broader scale. The popularity of agent technology has increased, decreased and increased again over the past few decades (as the wave of professional literature published during the mid 1980s and 1990s indicates), and a number of researchers proclaimed the future belonged to agents, “Agents will be the most important computing paradigm in the next ten years…By the year 2000, every significant application will have some form of agent-enablement” (Janca, 1996). While this has not transpired, many researchers still believe that the technology is here to stay. One of the earliest intelligent agents was called ELIZA, created in 1966 at MIT by Joseph Weizenbaum, to simulate a conversation with a physiotherapist. ELIZA used natural language to communicate with the user, and her software code included only 240 lines. Today’s agents are incredibly flexible software components, used in complex systems such as traffic management and route guidance (Adler, Satapathy, Manikonda, Bowles and Blue, 2005), power grid system control (Flynn and Dodd, 2006), and supply chain management (Xue, Xiaodong, Qiping, and Wang, 2005). One area of intense and ongoing debate is the definition of agents. There are numerous definitions of what an intelligent agent is, and just as many that focus on what an agent is not. 1 There is also rigorous debate about the concept of the truly intelligent agent, whether it exists, or can be designed. In his compilation of essays on agent technology, “Software Agents” (Bradshaw, 1997), James Bradshaw mentions no fewer than seven possible definitions of what an agent is in the opening chapter. A review of literature by Schleiffer (2005); Hostler, Yoon and Guimaraes (2005); Chen and Su (2003); [Jensen (2002) REMOVED] Lieberman et al. (2001); Shang, Shi and Chen (2001); Wooldridge and Jennings (1998); Negroponte (1995) and Maes (1997) reveal additional definitions, each slightly different from the next. Bradshaw (1997) states that coming up with a “once-and-for-all definition of agenthood” is very difficult, because researchers and developers have so many opinions on agents, their function, and their application (p. 5). He also asserts that “there has been an explosion in the use of the term without a corresponding consensus on what it means” (p. 4). According to Bradshaw, this may be the reason that many developers mistake generic software programs for agents – “some programs are called agents simply because they can be scheduled in advance to perform tasks on a remote machine” (p. 4). Along these same lines, as Foner (1993) observes in Bradshaw (1997, p. 6), there is “little justification for most of the commercial offerings that call themselves agents. Most are barely autonomous, unless a regularly scheduled batch-job counts”. Franklin and Graesser (1996) are quick to point out that while all agents are software programs, not all software programs are agents. Petrie (1996) in Bradshaw (1997, p. 10) goes a step further by demonstrating that most of the “current Web-based searching and filtering “agents”, though useful, are essentially one-time query answering mechanisms”. Defining Agent Technology With that in mind, a cursory overview of some of the practical definitions of agents is in order. The term “agent” most often refers to a software program that gathers information or performs some other service without the immediate presence of the user. While some researchers [FININ 1997 removed] express doubt that a consensus about what an agent is will ever be reached, many researchers in the area of agent technology agree that the definition by Shoham (1997) is accurate: “A software entity which functions continuously and autonomously in a particular environment, often inhabited by other agents and processes”. Bradshaw (1997) 2 asserts that agents are able to carry out activities without constant human intervention, while Jafari (2002) sees agents as “a set of independent software tools linked with other applications and databases running within one or several computer environments. The primary function of an intelligent agent is to help a user better use, manage and interact with a computer application” (p. 29). Wooldridge (1997) describes an agent as an encapsulated computer system, situated in a particular environment, that is capable of flexible, autonomous action in that environment in order to meet its design objectives. Autonomy in this case indicates the ability to compute something, and the preferences over how they use that capability (Birmingham, 1995). Laurel (1997) includes librarians as human models for agents, stating “agents provide expertise, skill and labor. They must of necessity be capable of understanding our needs and goals in relation to them, translating those goals into an appropriate set of actions, performing those actions, and delivering results in a form that we can use” (p. 71). In addition to librarians, Laurel goes on to list teachers, secretaries and accountants as a few of the examples of real-life agents who carry out these actions. Hostler, Yoon and Guimaraes (2005) outline several classifications of agents, a typology first presented by Nwana and Ndumu: deliberative, reactive, Internet, Interface, mobile and stationary. Deliberative agents have a pre-set outline of actions to take once an event is activated by the user or system, whereas reactive agents do not store this type of script, and must meet their goals by using a “stimulus/response type of behavior” (Nwana and Ndumu, 1998, p. 30). Internet agents are those that work to reduce information overload of the user, by using spiders and crawlers to efficiently search the Web. The interface agent helps users navigate their online environment by observing actions and improving future outcomes. Agents that have the ability to “roam the network such as the World Wide Web, interacting with foreign hosts, performing the various duties assigned at a remote site, and come back to its user upon completion of the task” are known as mobile agents (Nwana and Ndumu, 1998, p. 36). An agent that does not roam is known as a stationary agent. Dent, Harris, Martinez and Hall (2001) describe how agent terminology helps users to better understand agent systems, stating, “Many of the systems where agents are at work are 3 highly complex, and understanding what tasks are being undertaken by the system can be difficult…One of the benefits of agent technology is that it provides a helpful, abstract way of talking about complicated distributed systems using basic terminology” (p.56). For example, mediator agents, communication agents and query-planning agents (Birmingham, 1995) are easily recognizable terms applied to a complex set of processes that might occur within a given environment. Sundsted (1998) describes certain characteristics of agents that are relatively exclusive: they are autonomous, have the ability to learn, mobile, persistent, goal oriented, communicative, and flexible. He further advances that although agents are small programs and limited in what they can accomplish on their own, when working in concert with other groups of agents, they can perform any number of tasks. Researchers have also tried to further specify the work done by agents. Miller’s model describes an agent architecture which includes the four functions of observation, recognition, planning and/or inference, and action or execution (1997), while Maes (1997) states that agents can help users in a number of different ways: 1. They hide the complexity of difficult tasks 2. They perform tasks on the user’s behalf 3. They can train or teach each other 4. They help different users collaborate 5. They monitor events and procedures Wooldridge and Jennings (1998) address the usefulness of agents, and state that there are two conditions that make new technology useful: the ability to solve problems that have been unsolvable with previously existing technology, and the ability to solve current problems more efficiently (p. 5). It is obvious that there are numerous ways to describe agents, from the practical to the fantastic; the challenge for those in libraries and other information service areas is to separate fact from fiction, and utilize agent technology for support and problem-solving in a way most helpful to users. A Brief Literature Review of Intelligent Agents in Practical Environments 4 A great deal of progress has been documented since the early study of the intelligent agent, and the professional literature is largely reflective of project-based exploration and research.