THEME ARTICLES

Artificially intelligentconversational agents in libraries

Victoria L. Rubin and Yimin ChenFaculty of Information and Media Studies, University of Western Ontario,

London, Canada, and

Lynne Marie ThorimbertMarigold Library System, Strathmore, Canada

Abstract

Purpose – Conversational agents are natural language interaction interfaces designed to simulateconversation with a real person. This paper seeks to investigate current development and applicationsof these systems worldwide, while focusing on their availability in Canadian libraries. It aims to arguethat it is both timely and conceivable for Canadian libraries to consider adopting conversational agentsto enhance – not replace – face-to-face human interaction. Potential users include library web site tourguides, automated virtual reference and readers’ advisory librarians, and virtual story-tellers. Toprovide background and justification for this argument, the paper seeks to review agents from classicimplementations to state-of-the-art prototypes: how they interact with users, produce language, andcontrol conversational behaviors.

Design/methodology/approach – The web sites of the 20 largest Canadian libraries weresurveyed to assess the extent to which specific language-related technologies are offered in Canada,including conversational agents. An exemplified taxonomy of four pragmatic purposes thatconversational agents currently serve outside libraries – educational, informational, assistive, andsocially interactive – is proposed and translated into library settings.

Findings – As of early 2010, artificially intelligent conversational systems have been found to bevirtually non-existent in Canadian libraries, while other innovative technologies proliferate (e.g. socialmedia tools). These findings motivate the need for a broader awareness and discussion within the LIScommunity of these systems’ applicability and potential for library purposes.

Originality/value – This paper is intended for reflective information professionals who seek agreater understanding of the issues related to adopting conversational agents in libraries, as this topicis scarcely covered in the LIS literature. The pros and cons are discussed, and insights offered intoperceptions of intelligence (artificial or not) as well as the fundamentally social nature ofhuman-computer interaction.

Keywords Academic libraries, Public libraries, Canada, Intelligent agents, User interfaces,Information retrieval

Paper type Research paper

IntroductionConversational agents: origins and typesAs modern libraries continue to evolve in the information age, the problem of how tobest access information and take advantage of technological advances withoutintroducing new barriers remains a compelling puzzle. The impact of internet, andcomputer technologies on the library paradigm is unmistakable: even small ruralCanadian libraries may offer web sites and an online public access catalogue (OPAC).

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0737-8831.htm

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Received 12 April 2010Accepted 23 May 2010

Library Hi TechVol. 28 No. 4, 2010pp. 496-522q Emerald Group Publishing Limited0737-8831DOI 10.1108/07378831011096196

Where to next? We make the case that Natural Language Interaction (NLI) systems,human-computer interfaces designed to simulate conversation with a real person, arean effective, appropriate complement to many existing library services and may be thekey to unlocking solutions to future interactions with information.

NLI is a part of the broader fields of Natural Language Processing (NLP) andArtificial Intelligence (AI). Most internet users have, perhaps unknowingly, alreadyhad some contact with NLP technologies in the form of search engines and machinetranslation tools and with AI – through video games and financial tools. NLP studiesthe structure, function, and use of language, and organizes it into computationalmodels to design and develop language-related software applications (Joshi, 1999). Thefield of AI focuses on intelligent computer systems, those that exhibit thecharacteristics we typically associate with intelligence in human behavior such asunderstanding language, learning, and problem solving (Barr and Feigenbaum, 1982).As a synthesis of these two fields, the promise of NLI is the ability to providebelievable, personalized, and human-like interaction with computers in naturallanguages like English or French.

For our purposes, we identify two realms within NLI: “chatbots” (also known astext-based conversational agents, artificial conversation entities, chatterboxes, orsimply bots), where interaction is limited purely to text input and output; and“embodied conversational agents” (ECA) 1, where the computer interface “isrepresented as a human body, that uses its face and body in a human-like way inconversation with the user” (Foster, 2007, p. 828) and incorporates animations that aresynchronized with the system’s linguistic behaviors. Historically, text-based chatbotssimply aimed at free-flowing conversations with the user, while ECAs generally servea particular purpose, such as providing information to users or aiding them with atask. Due to animation, ECAs can also express believable human behavior duringconversations to enhance the social quality and enjoyment of human-computerinteraction.

Survey of language technology availability in Canadian librariesTo position our research in the context of the broader use of language-relatedtechnologies in libraries, we surveyed the current state of their availability in thelargest libraries in Canada. Using the latest available Canadian Public LibraryStatistical Report and Rankings as the basis (Public Library Statistics, 2008), weselected the ten largest public libraries by holdings (Public Libraries, see the Appendix,Table AI). By the same parameter from the Maclean’s, 2009 Rankings of CanadianUniversities (Maclean’s, 2009, 16 November), the top ten academic libraries werechosen (Academic Libraries, see the Appendix). The rationale was to obtain a sampleof Canadian libraries that are likely to have the resources and expertise to test andimplement current language technologies.

The web site of each selected institution was examined for availability oflanguage-related services and information about them (accessible electronically viatheir respective web sites). Our primary interest was artificially intelligent NLPapplications such as text-based question-answering, and more specifically chatbotsand conversational agents on the library web sites. We also inventoried broaderinternet-enabled services where text or speech were involved (see the Appendix).

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In particular, we looked for text- or audio-based services (such as OPACs anddownloadable e-books), language-related resources (online language learningpackages) and web site accessibility tools (such as audio/video tutorials, and foreignlanguage translations), online socially interactive services (such as live or instantmessage reference services, and social software applications), as well as other NLP/AIapplications (such as retrievable FAQs). In addition, we examined web sitedocumentation (missions, visions, core values, or strategic plans, where publiclyavailable) for statements pertaining to new technologies to see how the librariesaddress and present issues of technological innovations.

Survey results: current status of broader innovative language-related technologiesAs expected, all 20 surveyed library web sites, provide access to OPACs anddownloadable e-books. Eight out of 23 public libraries (Toronto, Ottawa, Mississauga)and five academic libraries (UBC, Queen’s, University of Calgary, McGill, and Ottawa)– provide video tutorials to navigating their web sites. This is indicative of a new trendin help services: moving from searchable text manual style to more interactive andengaging brief presentations.

All 20 libraries have a-synchronous e-mail reference services. Six public librariesand all but one academic library (Montreal) have live or instant messaging reference(IM) services. Hours of operation of these services are typically restricted to officehours, for instance, Vancouver’s AskAway does not operate after 5 p.m., and isunavailable on Sundays. When unable to cope with the volume of simultaneousquestions, some IM reference services place patrons in live queues until a librarian isable to join the session: an estimated wait time for a question to the Virtual ReferenceDesk at the Toronto Public Library was 26 minutes (on 11 March 2010).

All but two (the Winnipeg and Fraser Valley Regional Public Libraries) have atleast one form of social media application: Facebook, Twitter, blogs, RSS, Tagging, orYouTube (Social Media, see the Appendix). The proliferation of these applications maybe a fad, but represents a consistent institutional response to user interests in newinteractive technologies.

More than a half of the library web sites provide alternative language support forweb site navigation through translations or alternative resources. English and Frenchbilingual navigation is common (in seven public libraries and in four academiclibraries – Alberta, McGill, Montreal, Ottawa). In addition to English and French website access, extensive multilingual access support is provided in five public librarycases: Vancouver offers library web site navigation in six alternative languages,Edmonton – in nine, Mississauga – in ten, Ottawa – in 11, and Toronto – in 16 (website Translation, see the Appendix). As far as online computer-assisted languagelearning applications, only four institutions, all public libraries, offer online access tosuch software (specifically, the Calgary and Edmonton Public Libraries provide accessto Tell Me More Online courses; Ottawa and Hamilton – to Mango Languages).

Four libraries show their dedication to technological innovation in their publiclyavailable online documentation. University of Saskatchewan Library acknowledgesthat “library users expect state-of-the-art computing equipment, robust networks, vastcollections of software, and other information resources as well as expert assistancewhen and where they need it” (University of Saskatchewan, 2010). Toronto PL states

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that “[n]ew technologies extend access to global information beyond library walls”(Toronto Public Library, 2010). Ottawa PL “continuously review[s] current practices,make[s] improvements, leverage[s] technology . . . ” (Ottawa Public Library, 2010). UBCLibrary announces “the digital agenda is a major plank of their library” includingon-line access to “thousands of full-text e-journals, e-books, indexes and databases”,and “providing new and more efficient types of digital services . . . ” (University ofBritish Columbia, 2010). The question is: to what extent are these libraries prepared toexperiment with groundbreaking state-of-the-art technologies, based on theirindividual priorities, criteria for adoption, and implementation capabilities?

Survey results: current status of conversational agentsOur survey confirms that NLI systems specifically are virtually non-existent in the top20 largest Canadian libraries as of early 2010. None of the top 20 surveyed librariesemployed embodied conversational agents for any of their online-accessible services.As for text-based NLI applications, there was only one rare approximation. Instead of astandard static FAQ list, the University of Western Ontario uses a text-basedartificially intelligent Ask Western Libraries service, an Instant Answer Agentdeveloped by the IntelliResponse aspiring to embrace “the new paradigm of the web,social media and mobile” (IntelliResponse, 2009). Ask Western Libraries retrievesbest-fit answers from the inventory of FAQs such as “What are western librarieshours?” (see Figure 1).

Figure 1.Ask Western Libraries:a searchable text-based

interactive FAQ list, theUniversity of Western

Ontario

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The service takes the form of familiar information retrieval format, but its domain islimited to the pre-existing information on library and archives operations. The system“has been asked 58,834 questions since it was implemented in 2007” as a form ofon-line support and self-help mechanism, and has been able to pair up “80 percent ofasked questions to one best-fit response” (From personal communications with JenniferRobinson, communications and outreach librarian, March 25, 2010). Although far froma full-fledged NLI system, this application is still a step up from unattractive static listsand endless scrolling.

In sum, the lack of conversational agents in Canada is not surprising consideringthat, to the best of our knowledge, there have been very few attempts of NLI systemapplications worldwide. Most notably, four systems were found in Germany and one atthe Mount Saint Vincent University Library in Halifax, Nova Scotia, which was notamong the surveyed Canadian libraries. These systems will be further illustrated anddiscussed in this paper. The current status of the availability of broaderlanguage-related technologies – and specifically NLI systems – motivates the needfor a broader discussion within the LIS community in order to heighten awareness ofthese systems’ applicability towards library purposes.

Evolution of capabilities and internal workings of select prototypesWe argue it is both timely and conceivable for Canadian libraries to considerincorporating conversational agents into their systems. To provide background andjustification for this argument, we first review several NLI systems, from classicimplementations to state-of-the-art prototypes: how they interact with users, producenatural language, and control conversational behaviors. Four systems are discussed asimportant landmarks in the development of NLI systems towards more human-likeintelligent interactions – a task far more difficult than it may seem at first glance. Formost people, using and manipulating language is second nature, but the processesinvolved in even the simplest acts of communication are decidedly non-trivial: wordsrarely have only one meaning, grammatical structures can often be interpreted in avariety of ways, and the pragmatic intent of a sentence can be completely at odds withits semantic meaning (e.g. sarcasm). A vast amount of human knowledge about thephysical and social world is required to achieve any practical level of languageunderstanding and manipulation.

The Classic ELIZAIn 1966, Joseph Weizenbaum designed one of the first chatbots, ELIZA, whichsimulates a dialogue with a Rogerian psychotherapist. ELIZA could convincinglycreate a discursive environment where almost no real-world knowledge was requiredon the part of the program. Since the role of a therapist was understood to benon-judgmental and passive, the chatbot was designed to allow the patient to direct theconversation. As a result, ELIZA often rephrases patient statements in the form of aquestion (Weizenbaum, 1966). By taking advantage of common conversationalpatterns (e.g. “I hate my father”/“Why do you say that you hate your father?”), ELIZAis capable of producing authentic-sounding responses. Depending on how the userchooses to interpret and respond to ELIZA, the illusion that he or she is actually

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interacting with a real person can be very strong. However, ELIZA’s responses areboth scripted and limited (see Figure 2, www.masswerk.at/elizabot/eliza.html).

In order to “understand” any kind of input, ELIZA parses it word by word andcompares each word to a database of pre-defined keywords until a match is found.These keywords are attached to a set of decomposition rules, for example: (0 YOU 0ME) in which ELIZA only recognizes “YOU” and “ME”, and the “0”s serve asplaceholders. A set of reassembly rules tell ELIZA how to construct an appropriateresponse. To generate a reply to “Do you really like me?”, ELIZA first breaks thesentence word by word, then converts the “YOU” to “I” and the “ME” to “YOU” inorder to redirect the question back to the user. Then, it applies a reassembly template,such as (WHAT MAKES YOU THINK I 3 YOU). The numeral “3” is a placeholderwhich tells ELIZA to insert the third block from the original input into that position.Therefore, ELIZA’s response to the example question would be: “What makes youthink I really like you”. If ELIZA is unable to discover a valid keyword in a user’ssentence, it deploys a canned, noncommittal response (e.g. “Can you elaborate on that?”or “I’m not sure I understand fully”). An extended conversation, unconventionalresponses or colloquial phrasing quickly reveals the limits of ELIZA’s conversationaltemplates. Despite its shortcomings, ELIZA was a revolutionary application for itstime, and today is one of the most well known and widely distributed programs in thehistory of AI (Wallace, 2009, p. 186).

The ALICE-bots: expanding databasesELIZA’s pattern-based approach has worked so well that, even today, ELIZA-likesremain among the most common chatbot implementations. One of the most successfulof these, ALICE (Artificial Linguistic Internet Computer Entity), has won numerousprizes in AI (Wallace, 2009). Essentially, ALICE is an evolutionary descendant of

Figure 2.Sample conversation with

ELIZA: the conversationappears on screen as lines

of text

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ELIZA, improving on all its ancestor’s capabilities, but without making any radicalchanges. Whereas ELIZA is usually equipped with only about 200 areas of knowledge(pattern/template combinations), ALICE has more than 40,000 (Wallace, 2009).ALICE’s greatly expanded rules database – along with better “targeting” algorithmsto identify conversation patterns, and the capacity to store and retrieve previousconversations – allows ALICE to converse far more freely and naturally than ELIZA.Incorporating common sense knowledge into NLI system, such as “facts, rules ofthumb, and heuristics for reasoning about the objects and events of everyday life”(Cycorp, 2009), can improve understanding of user input and generation of moreaccurate and appropriate responses.

Added sophistication of human-like behaviorsCoupling chatbots’ natural language capabilities with the animation of an avatar is thenext evolutionary step. We examine three prototypes that synchronize naturallanguage interaction with animation and human-like behavior. Facial expression,gestures, tone, body language, and even silence are important to human-computercommunication. If the language of an ECA is too formal, robotic, or has too manyerrors in speech, the experience may lose its human element and much of its value. Thecasual body language, pleasing voice, and engaging yet professional manner in whichmany contemporary ECAs converse strives to enhance the user’s experience.

SGT Star: an online virtual presence. SGT Star is an artificially intelligent onlinevirtual guide created for the US Army by the NextIT Corporation (NextIT, 2009, 12February; US Army, 2009). The user accesses SGT Star through a link on the US Armyweb site (www.goarmy.com). SGT Star appears in a window as a full-bodied animatedcharacter, and it opens the conversation by making a speech about what type ofinformation it is able to provide: “straight answers about your real questions” (seeFigure 3). Once the user types in a name, SGT Star’s animation changes to a head shot,more typical of chatbot format (see Figure 4). SGT Star answers in both text andspeech, although the user has the option to turn the sound off.

SGT Star is task-oriented and is capable of conversing with the general public at anintroductory level, or with potential military recruits. It is more likely to provideaccurate responses when the user asks topic-specific questions about the US Army.SGT Star has answered an average of 145,000 questions a month in the three yearssince it went live, with a 95 percent accuracy rate (NextIT, 2009, 12 February). Inaddition to question answering, the system directs the user to appropriate web pageson the US Army web site. For example, if the user enters “What can I do in the army?”,SGT Star will produce the army’s “Careers and jobs” web page in a separate window.SGT Star can retain a record of the conversation for future reference if the user choosesto create a US Army account.

MAX: a proactive conversationalist with “world knowledge”. Max is a multimodaland multimedia virtual public museum guide at the Heinz Nixdorf Museum inPaderborn, Germany (Kopp et al., 2005). The MAX system is personified on a flatscreen as a man shown from the waist up. MAX receives natural language inputthrough a keyboard from one user at a time, and responds through a loudspeaker withcorresponding arm gestures and facial expressions. MAX incorporates severalmultimodal components. For example, camera-based visual perception impacts the

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system’s responses by adapting comments to roughly reflect the number of peoplestanding before the screen. MAX has 54 non-verbal behaviors fashioned assynchronized animations appropriate at a given point of the conversation and canincrease informativeness by nodding in agreement or pointing out directions. As bodylanguage forms an important part of human communication, MAX’s ability to produceanimated gestures is one of the benefits of using embodied conversational agents overstrictly text-based or audio-based agents.

MAX is a mixed-initiative dialogue system, and can influence the direction of theconversation. For example, it may take the initiative to suggest a topic from a museumexhibition for discussion. The user is able to regain the initiative at any time by posinga new question.

As a more sophisticated dialogue system than SGT Star, MAX’s conversationalbehavior and actions are determined through three steps: interpretation, dialoguemanagement, and behavior planning. During interpretation, MAX handles thetext-based input from the user, including ill-formed or ungrammatical language that is

Figure 3.Ask SGT Star: full-bodied

animation

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likely to occur in a public setting. The system searches for the best match to determineits next action. When no match is found among its 138 interpretation rules, Max revertsto small talk. During each conversation, the dialogue manager retains contextualinformation about each dialogue such as the topic and who has the initiative, andbuilds a model for the current user such as the name, interests, and the type of behaviordisplayed (e.g. level of cooperation in the dialogue) (Kopp et al., 2005). This “worldknowledge” allows MAX to participate in more human-like conversation. For example,if more than one user mentioned a particular movie, MAX might comment on themovie’s seeming popularity.

In summary, ELIZA and its evolutionary descendant, ALICE, offer examples ofchatbots that rely on text patterns but vary in number of pre-programmed“knowledge”. Two ECAs – SGT Star and MAX – have animation, higher levels oflinguistic sophistication, better dialogue strategies, and variety in interaction modes(online or in person; speech or text). As an online ECA, SGT Star is able to conversewith multiple users at the same time, unlike MAX, which requires one user at a time to

Figure 4.Ask SGT Star: Chatbotformat

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stand in front of an installation. SGT Star’s capabilities are perhaps most appropriateand practical for customization for libraries purposes.

Exemplified taxonomy of conversational agent purposesThe previous section introduced a progression of prototypes and discussed theirmechanisms, with variations in linguistic and human-like sophistication. In thissection, we propose a taxonomy of four pragmatic purposes for conversational agentsoutside of libraries, exemplified by implementations worldwide, and in the nextsection, we discuss how these purposes translate into the library setting: to enhance –not replace – face-to-face human interaction. Conversational agents can be customizedto suit a wide range of applications, but can be classified as serving four fundamentalpurposes: educational, informational, assistive, and socially interactive purposes, oroften some combination thereof (see Table I). Each of these purposes will now bediscussed individually with examples.

Educational purposesClinical instruction. Users engaging with embodied conversational agents for medicine,dentistry, or health instruction view a virtual patient in a simulated clinic. For instance,

Environment Purposes Example applications

Outsidelibraries

Educational (for instruction and training) Clinical instructionMilitary trainingTutoring systemsE-learningCorporate trainingCoursework supportComputer-assisted languagelearning

Informational (for information-seeking orpromotions)

Automated customer serviceInformation assistantsInterfaces for IR systemsMilitary recruitment agents

Assistive (for support of individuals withdisabilities)

Sign language avatar interpreters

Socially interactive (for entertainment or socialcompanionship)

Virtual world enhancementsGaming supportVirtual social companionsVirtual hosts

In libraries Combined: educational, informational,assistive, and socially interactive

Automated virtual referencelibrariansWeb site tour guidesVirtual readers’ advisory serviceprovidersSocial software hostsVirtual conversational club hostsVirtual book club hostsVirtual storytellers

Table I.Taxonomy of

conversational agents’purposes: illustrated with

existing and potentialapplications

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ADELE monitors student actions and questions, and delivers feedback to the student,pointing out the most important elements of the task at hand (CARTE, 2009). Thesystem may intervene to prevent a student from making irreversible mistakes in anattempt to simulate training in a real-life environment and provide the user with asense of practical learner support. The human-computer conversation is analyzed andprocessed by ADELE in order to create further instructional narrative, as well as adatabase of frequently asked questions, reference materials, and end-of-session reports(Elliott and Brzezinski, 1998, p. 26).

Military training. Military personnel are trained for duty in military gamesimulations where embodied conversational agents react to the speech or actions of theuser. To enhance the realism, the training is conducted in a theater with a screen thatwraps around half the room, and three projectors and a sound system make the theaterrealistic and directional (Hafner, 2001, June 21). The user’s natural language is receivedthrough speech recognition, and this in turn produces a behavioral response in theembodied agent. The ECAs (animated characters in the scene) generate their ownunscripted natural language as a response to the user, or to influence the user’s actions.The ECAs speak to the trainee or the other ECAs in an attempt to simulate a real-life,unpredictable environment.

Tutoring systems. Intelligent tutoring systems create a model of a student’sknowledge level and understanding of a subject in order to generate harder or easierproblems based on the student’s performance. Kerly et al. (2008) found that theincreased level of control and feedback afforded by the chatbot interactionssignificantly improved the accuracy of students’ self-assessment abilities as comparedto students using the same software minus the conversational agent. Incorporatingchatbots helps improve student engagement, increase interactivity, and keep studentson task.

E-learning and corporate training. SitePal, a producer of chatbots and otherspeaking characters, develops educational characters and corporate training agents(SitePal.com, 2009). For example, the Ontario Workplace Safety and Insurance Boardused SitePal’s avatars to provide e-learning about health and safety and were able totrain 1300 youths aged 14-24 within four months.

Coursework support. An instructor in the Kinesiology Department at the Universityof Houston used a SitePal avatar as an additional informational element forcoursework. The avatar was also found to engage students in a way that enabled themto learn organically instead of through memorization (SitePal.com, 2009).

The site ActiveHistory.co.uk portrays historical figures and answers historyquestions for students; students can interview historical characters such as Henry VIIIand Martin Luther King (Active History, 2009). The historical characters areresponsive and not proactive in conversation, but users who are uncertain where tostart can select topic areas or view a list of suggested questions. A record of theinterview can be viewed or printed for future reference.

Computer-assisted language learning. CSIEC, Computer Simulation in EducationalCommunication, is a system for English instruction focused on supplying a virtualchatting partner for English learners at any time. “It generates communicativeresponse according to the user input, the dialogue context, the user’s and its ownpersonality knowledge, common sense knowledge, and inference knowledge” ( Jia, 2009,

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p. 249). Animated prototypes based on ALICE-bot have been adapted from English toother languages such as French and Afrikaans (Shawar and Atwell, 2003).

Informational purposesAutomated customer service representatives are gaining popularity, although they areused mostly a novelty gimmick such as Eve, the eGain chatbot (Eve, 2009), or toanswer very simple questions and to direct users to the web site’s online FAQ filessuch as the Lloyds TSB Help Centre (Lloyds, 2010). Some systems, such as AINI (theArtificial Intelligent Solution Humanoid) are purported to reduce “customer reliance onhuman operators” and improving customer services (Goh and Fung, 2003).

Information assistants and interfaces for information retrieval. Microsoft’s EncartaEncyclopedia, before it was discontinued in October of 2009, featured a chatbot whichcould answer questions directly through Windows Live Messenger. REA is a virtualreal estate agent that answers user questions about properties in a database and guidesusers around a virtual house (Cassell, 2001; Foster, 2007). GETESS is a touristinformation agent that gathers information like housing, leisure activities, or sightsfrom the multitude of tourist sites on the web, and presents it to prospective tourists(Staab et al., 1999). SGT Blackwell (Artstein et al., 2009), just as previously discussedSGT Star, are examples of military recruitment agents. Scalable avatars arehuman-like assistants that can be incorporated on mobile devices like laptops, PDA’s,and cell phones (Berner and Rieger, 2003).

Assistive purposesAssistive technologies support the participation in activities that would otherwise bedifficult or impossible for individuals with disabilities, including those who cannot relyon speech or writing as the primary means of expression (Speech and LanguageProcessing for Assistive Technologies, 2010). One example is a speech to sign languagetranslation system that was developed in the Department of Electronics at theUniversity of Alcala in Spain (San-Segundo et al., 2008). The system translates spokenSpanish natural language into a Spanish sign language response through athree-dimensional animated agent.

Socially interactive purposesVirtual world enhancement and gaming support. The virtual world Second Life (2009)has introduced autonomous, animated conversational agents that act as informationguides and greeters. The player and the agent in Second Life interact throughtext-based conversation. The agent does not store information, but will extractinformation from secondary sources using NLP content-based information retrieval.For example, if you ask a Second Life information guide about a book, the guide willaccess Amazon.com and provide you with the answer in text. The Second Life guidesare equipped to move as directed by natural language, but without facial expressions.They may initiate discussion with human players around the virtual world – almost asroving reference librarians, although not necessarily within a Second Life library(Second Life, 2009).

Virtual social companions and hosts. The MIT FitTrack system is designed to buildand maintain long-term social emotional relationships with users; the agent plays therole of an exercise adviser who discusses physical activity with the user and

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encourages them to become more physically active (Bickmore and Picard, 2005).Chatbots have also been used as a virtual party host to help guests find conversationtopics (Andre and Rist, 2000) and in online dating services to fool people into thinkingthat they were conversing with a human being (Epstein, 2007, October 16-17).

Library applications: existing and potentialEducational, informational, assistive, and socially interactive purposes are oftencombined in libraries, which land themselves as ideal candidates for adoptingconversational agents. The potential for conversational agents to augment and bridgegaps in current library service warrants further investigation. Several concrete ideasfor library implementations and rare existing examples in libraries worldwide arepresented in the following.

Informational and educational purposesAutomated virtual reference librarians and web site tour guides. With existing virtualreference services using instant messaging, librarian hours are limited, and thevirtual reference station cannot be staffed overnight. Given the highly scripted natureof many reference interview interactions, rule-based chatbots can provide online help.In a similar fashion to SGT Star, using content-based information retrieval, an ECAcould direct the patron around a library web site, pulling up relevant library webpages, external web links, documents, or OPAC records. The application could help thepatron navigate the library content more efficiently: acting as a virtual tutor, walkingpatrons through basic tasks such as renewing materials online, ordering interlibraryloans, or searching for materials in the databases or OPAC. The anonymity of chatbotconversations can also appeal to those who have “no or little knowledge about libraries. . . [or] that suffer from library anxiety” (Christensen, 2007). Patrons can also accept orreject the returned information on their own terms, without worrying about offendinganyone.

With ECAs, information can be delivered to the patron in speech or text, and thepatron should be able to control when and how they receive information. Using aseminar-style environment of intelligent tutors, patrons can scaffold their ownacquisition of knowledge and preempt frustration by controlling the pace ofinformation dispersal from the system.

A library system could adopt the end-of-session reporting as noted in the ADELEmedical training system. Patron-computer conversations could be automaticallysummarized, e-mailed or printed. With SGT Star’s dialogue management capabilities,the conversation may also be stored on the patron’s account and retrieved for futureinteractions (by patron’s choice to ensure privacy). In this way, the application mayprovide more relevance, service continuity, and customization for an individual patron.

Informational chatbots have been incorporated into very few library web sitesworldwide. For example, a chatbot prototype, Lillian (see Figure 5), informs patronsabout library holdings and answers questions on library materials using Amazon.comand the OCLC [Online Computer Library Center] (Chatbots.org, 2006). From these websites, Lillian aims to advise users about a book’s content, titles by the same author,related book reviews, and what other books people are reading.

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In Nova Scotia, the Mount Saint Vincent University employed two chatbots, Sarah andSuzy Sitepal, to answer frequently asked questions about writing and research, tohighlight library resources and services, and to relay information about library events(Skov-Nielson, 2008). The chatbots generated traffic to the library web site andincreased the library’s visibility in the campus community. The latest incarnation ofanimated character, Darcy, was re-launched in mid-March 2010 and a non-interactiveavatar, a Sitepal basic service which does not require building an intelligent databasethat captures all FAQs. Darcy delivers short audio-messages from the main libraryweb site such as an informal public awareness announcement about the library openhouse event (see Figure 6). “Audio grabs attention but definitely goes stale far morequickly than text”, so occasional “guest appearances” are preferred to “feature sectionsof the web site in a fresh way” and “appeal to the our youngest students, generallydescribed as Millennials” (from personal communications with Donna Bourne-Tyson,the University Librarian on March 19, 2010).

In a number of institutions in Germany, text-based chatbots have been given theopportunity to prove themselves as virtual librarians, exemplified here by Stella fromthe Hamburg State and University Library (see Figures 7 and 8). Unlike othercountries, “live chat services in particular lack acceptance in German libraries, eventhough they can undeniably offer much more individual and comprehensive answersthan chatbots” (Christensen, 2007). In a survey of four library chatbots (both public andacademic), Christensen (2007) found that “the average number of interactions per day[for chatbots] exceeds those of other forms of virtual reference by far”, with some ofthem engaging in hundreds of dialogues per day. Furthermore, the chatbots did notend up “stealing” reference questions from the human librarians. Instead, the chatbotsmostly took care of simple reference queries and frequently asked questions, leavingthe human staff free to work with more detailed requests.

Virtual readers’ advisory service providers. Using a format similar to Lillian’s use ofAmazon.com, a system may also access databases such as NoveList in order to provide

Figure 5.OCLC virtual librarian,

Lillian: designed to answeruser queries about books

and particularly libraryholdings, or refer users toanother library or Amazon

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patrons with a virtual reader’s advisory and referral service delivery streams. Similarlyto the reference interview, reader’s advisory services are another fairly scripted type ofinteraction. Many online reader’s advisory resources that appear on library web sitesacross North America are currently e-mail-based, where responses are offered bylibrary staff (e.g. the Peoria Public Library in Illinois (2009)). Others use automatedform-based applications such as the Reader’s Robot at the Thompson-Nicola RegionalDistrict Library System, British Columbia (2010) that asks users to define theirinterests through lists of appeal factors such as genre, length, and the overall emotionaleffect of the book (agitation, sadness, contentment, or excitement). Much like with

Figure 6.Mount Saint VincentUniversity Library website greeter, Darcy: shortmessages are deliveredwith facial animation; nointeraction is currentlyoffered

Figure 7.A. Stella, Hamburg Stateand University Library:aims to teach informationliteracy by increasing thestudents’ awareness forinformation resources

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virtual reference, chatbots could potentially supplement live reader’s advisory serviceswith a real-time conversation in which the system can fine-tune its retrieved resultsbased on the user’s responses.

Socially interactive purposesSocial software hosts. Conversational agents have the potential to add to library culturethrough their inherently interactive and social nature. If what distinguishes ananimated conversational agent from strictly text-based applications is its ability toheighten personal engagement and enjoyment for the patron, then library prototypesshould seek to maximize their social impact. For example, animated conversationalagents could be employed in interactive Web 2.0 applications. The agent couldautomatically compile frequently asked questions or subjects, and generate a blog thatproduces lists, summaries or abstracts of library materials and services, includinglibrary news, programming, and outreach. A virtual agent may host such blogs byspecific subjects: an automated video gaming blog could showcase connections to otherlibrary materials, such as gaming magazines, graphic novels, digital arts, graphicdesign, films, or lesser-known services or materials. The animated agent may prove tobe a useful marketing tool to sustain traffic on the library web site, engage new users,and target teen patrons. If “technology has the most potential in an often overlookedbut utterly essential element of teen services – simply keeping up a dialogue with yourteen” (Uhler and O’Neil, 2010, p. 472), then a 24/7 animated librarian offers greatpotential for an ongoing and ever-changing conversation.

Virtual book club hosts, conversational clubs hosts, and storytellers. In an onlinelibrary book club or an ESL conversation club, an artificially intelligent animated hostinitiates and sustains conversation between patrons. An embodied virtual host mayalso exist in a virtual environment where patrons are also avatars, or in a chat roomformat that goes beyond the prevalent text-based format. The host could be assignedan appropriate physical likeness, vocal tone, language level, and a casual or formalspeaking style. A virtual environment creates other challenges, as the library would

Figure 8.Stella’s limited animationand multilingual abilities

in response to “Do youspeak English?”

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need to determine to what degree it would monitor user behavior and content. Yetanimated host applications may facilitate and inspire meaningful new online librarycommunities.

There is at least one example of an online virtual storytelling prototype (Figa et al.,2004) that uses voice-enabled conversational agents. The agents extract informationfrom story-specific metadata files and search the web for topics matching the story’scontent. Such applications have the potential to engage a group of children and youthin the library by using “the common ground of technology [to] create the meetingpoint” in the library community (Uhler and O’Neil, 2010, p. 472). Through greater use ofelectronic settings, libraries may offer more for unique audiences, such as reluctantreaders.

Benefits of adopting conversational agents in librariesIncreasing diversityThe goal in using conversational agents in libraries is to enhance – not replace –face-to-face human contact, interactivity, and service delivery streams. These systemsare most valuable to libraries in affordable online formats where the application canserve large numbers of people at any time of the day. There will likely be manypatrons, who continue to choose genuine human interaction over AI-based interaction,but automated applications may appeal particularly to teens or tech-savvy patrons.Conversational agents may service a wider range of library patrons. Applications, suchas virtual storytelling, would enable library patrons and users to engage in libraryservices even if they are at a physical distance from a library, have limited mobility, orhave other special needs. Spoken dialogue systems may facilitate hands-freeapplications to better service patrons that struggle with literacy, the blind, or peoplewith other physical impairments. Animated agents may use sign language tocommunicate information to the deaf. Multilingual systems would broaden servicedelivery streams for ESL patrons.

Enhancing patron-information interaction experiencesConversational agents also offer some valuable and unique benefits to individual users.They add expressive power to a computer (Andre and Rist, 2000), and can makehuman-computer interaction social, enjoyable, and entertaining; this in turncontributes to a positive user experience. Conversational agents can reducerepetitive, predetermined answers because the answers can be highly contextualized(Heudin, 2008). In training situations, animated systems can provide one-on-oneinstruction that proceeds at the pace of the individual. As with any web-basedapplication conversational agents may solve distance-related or physical barriers, andconversations are conducted in real time. Another practical benefit is that a guidereleases “the user from orientation and navigation problems known frommulti-window/multi-screen settings” (Andre and Rist, 2000, p. 1), because theconversation is contained within one window or page. For the user, conversationalagents reduce the need to sift through overwhelming amounts of onscreen text becausethe agent can point or link to the proper section on the web site (e.g. SGT Star), or theycan provide a summarized response.

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Even with the proliferation of technological innovations in libraries previouslydiscussed in our survey (see the Appendix), users often complain that electronicservices are “DUMB, BORING and DUSTY” (Goh and Fung, 2003). Due to theinteractive nature of a conversation, the level of user engagement and user controlmade possible with a NLI application can be far greater than with other interfacestyles. These systems can help libraries break out of the boring-and-dusty paradigmand satisfy the need for innovation by offering new ways to educate, inform, assist, andamuse.

Freeing librarians from tedious and repetitive tasksIn 2000, Laura Zick thought that a real artificial intelligence was “not yet within sight”(p. 9), and suggested that collaborative future between information professional andsoftware agents made the most sense. She cited Nardi and O’Day (1996), whodiplomatically stated: “Rather than seeing human agents and software agents as incompetition, as vying for the same place in our world, the wiser course is to leveragethe strengths of each, deliberately designing work practices and institutionalarrangements that reflect and exploit the possibility of collaboration between humanand software agents” (p. 83). We most emphatically agree that in 2010 libraries shouldcertainly “automate some of the more tedious and repetitive library tasks andlibrarians can be freed to do what they do best: guide clients through the maze to thebest information for that client’s particular need” (Zick, 2000, p. 9).

To take this position one step further, we argue that in 2010, especially at timeswhen librarians are not available or are out of patrons’ reach, NLI systems could stepinto specific roles, such as automated virtual reference librarians, web site tour guides,reader’s advisory service providers, and conversational or book club hosts. Just likenurses that can perform basic diagnostics prior to the physician’s examination,conversational agents could take care entirely of “the tedious and repetitive” tasks, andpotentially triage patrons to a superior authority, a live human expert: the librarian.

Factoring-in human psychologyAn interesting issue in conjunction with this discussion is the human tendency toanthropomorphize computers. Despite the relative lack of sophistication of earlychatbots, many users often became emotionally involved in their conversations,sometimes even while fully aware that the programs were not intended to simulateempathy or emotion. This type of cognitive dissonance has been termed “the ELIZAeffect”, and specifically refers to “the susceptibility of people to read far moreunderstanding than is warranted into strings of symbols – especially words – strungtogether by computers” (Hofstadter, 1995, p. 157). This principle has been exploited inthe way computer interfaces are programmed in services. For instance, ATMs say“hello” and “thank you” to show little touches of human-ness that can also help supportthe credibility and plausibility of conversational agents (Weizenbaum, 1966).

There is evidence that interactions with computers are “fundamentally social” –users “do in fact apply social rules to their interactions with computers, even thoughthey report such attributions as inappropriate” (Nass et al., 1994, p. 77). Theimplications for the AI systems in libraries are that, given a choice between them andmindless traditional search tools, users may want to experiment with and even preferartificially intelligent human-like interactions, despite current system limitations.

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A mere suggestion of human-like agency elicits social responses (e.g. offenses orthanks). Since humans are predisposed to seek out intelligence, it makes sense torepresent a user interface as a human being – in cases where social collaborativebehavior is the key – to facilitate human-machine interaction (Cassell, 2001), orpatron-information interaction in libraries.

Responding to perceptions of intelligenceAnother interesting question that has often occupied AI and NLP researchers is relatedto how much human behaviors are pre-programmed. Are interactions inherentlyscripted, and if so, could they be imitated? As we have seen with a step from ELIZA toALICE-bot, increased conversational acuity can be achieved simply through theaddition of more pattern recognition rules. If, for example, a programmer were able toanticipate every conceivable dialogue interaction and create a corresponding templaterule, how could we differentiate this mindless automaton from a thinking person? Doesthis suggest, then, that humans are themselves just robots following a set of highlysophisticated algorithms? Richard Wallace muses: “my own answer is that I’m99.999999 percent robot. But the 0.000001 percent non-robot is the source ofself-actualization, the inner-soul-gyroscope of self-control and responsibility” (Wallace,2009, p. 208). When we greet each other in the morning or answer a phone, how much ofhuman behavior is essentially following a script? In an early paper discussing thepossibility and implications of artificial intelligence, the father of computer scienceposed the question “can machines think” (Turing, 1950)? Of course, just whatconstitutes “thinking” and how we would be able to recognize it – proved to be aquestion with no unambiguous answer. When we reflect on these ideas in relation tolibraries, but is a compilation of FAQs, but a predetermined set of conversations?Given these considerations, the potential for an artificial intelligence to respondeffectively and accurately to library users may not be far-fetched.

Keeping up with the timesThe argument for consideration of NLI systems in libraries is not only important, butalso timely. Human-like interfaces have been experiencing a renaissance and aretransitioning from laboratories to applications in the workplace, home, and classroom(Lester, 2001), but yet rarely to libraries. The inventory of language technologies hasbeen increasing in popular mainstream applications: from familiar spell-checkers inMicrosoft Word to the addition of the Auto Summarize function in 2007. Theproliferation of AI and NLP technologies is also becoming more apparent: from thepopular Google Translate and Transliterate to the experimental Google Trends andFast Flip. The general awareness about language technologies is increasing with everynew application release: from stand-alone applications (like IBM’s Via-Voice speechrecognition) to built-in operating system capabilities (like Microsoft’s Windows VistaEmbedded speech recognition and text-to-speech). The time is right to consideroffering natural language interaction systems to library patrons.

Challenges in adopting conversational agents in librariesPractical concernsDespite their potential advantages, neither text-based nor animated conversationalagents have been widely adopted in libraries. Hsieh and Hall (1989) surveyed 185

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articles of AI applications to libraries published from 1976-1987 in LIS literature, andconcluded that AI systems have made “only a minor impact” in libraries since, it takes“a great deal precious commodities – money, time and knowledge” – to incorporate AIin the library. Availability of high speed internet is a pre-requisite to effectivelyprovide any virtual services, and may be the first obstacle to overcome in librariesoutside of major population centers. The cost involved in creating a program fromscratch or licensing the technology from a third-party developer. For rules-basedsystems (like ELIZA and ALICE), it may take a significant amount of time and effort topopulate the database with appropriate templates and responses, time that librariansmay not be able to commit to. Combined with maintenance and support costs, suchsystems may not be economically viable for most individual libraries, and may requirelibrary partnerships and cost sharing solutions. The cost-benefit for each librarysystem would need to be weighed – online conversational agents would be able toservice large urban populations, as well as smaller rural populations without physicalaccess to a library.

System limitations and language complexitiesEarly chatbots like ELIZA suffered from a number of limitations, some technical, sometheoretical. One obvious technical problem was the lack of memory and processingpower available on the hardware that these applications were built for, which made itdifficult for developers to give their programs an extensive vocabulary or the ability todeal with significant variation in conversational style. However, a greater limitationwas the top-down strategy employed in the creation of ELIZA, which has been adoptedinto ALICE. The basic premise is that developers attempt to predict the types ofinteractions that their programs will likely encounter and then try to encode as manyresponses as possible. Giving the chatbots a specific personality and conversationalrole can help cut down the number of possible conversations immensely by narrowingthe scope and subject discussed, but the sheer complexity of natural language and thelimitless turns and twists possible in even the most prescribed conversations can stilllead to awkward interactions.

User preparedness and acceptanceDialogue systems have been shown to be more successful when the user has cleargrammar, which may be an unrealistic expectation. Text-based user input often relieson user literacy; the SGT Star introduction specifically states that “spelling counts” (USArmy, 2009). Users who do not use correct grammar may not receive relevant answersto their questions. One solution to this problem is to incorporate speech recognition asan option for user input. However, speech recognition has its own difficulties, such asrecognizing dialects, accents, colloquialisms, or slang.

Some information professionals may feel that increased automation may changethe librarian’s role or even threaten the future of face-to-face services. Rao and Babu(2001) argue that intelligent agents “need not be construed as a threat”, for they are“also an opportunity to relieve the librarian of the drudgework of searching forinformation, which more often than not is a boring task” (p. 29). Others simplyconsider the technology to be too unrefined to be useful as more than a marketinggimmick.

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If conversational agents are to be effective in providing information or engaging inconversation, they do need to overcome their current pigeonhole as a novelty, whichrequires greater user awareness and familiarity with such systems. For example,MAX’s logfiles reveal that 1.6 percent of user comments are insulting, and 1.4 percentare obscene or politically inappropriate (Kopp et al., 2005). It is perhaps naıve toassume that all participants will be cooperative in all communications with artificialintelligence.

Overall, preventive costs, lack of time or expertise, system limitations, and userunpreparedness are all real concerns, and perhaps libraries are still not quite ready forconversational agents. However, given time and as this discussion continues in LIS,such NLI systems may prove to be indispensable.

ConclusionsConversational agents have the potential to enhance and emphasize social interactionas an essential part of human-computer communication, information retrieval, andinformation sharing. As shown in our survey of 20 Canadian library web sites – andwith the few active examples available worldwide – this potential is underutilized. Inpart, this is due to the scant attention that conversational agents have received inprofessional and scholarly librarianship circles. This discussion is probably mostbeneficial to practicing librarians who may not necessarily follow the latest trends atthe intersection of AI, NLP, and human-computer interaction.

Conversational agents, both text-based and embodied, have undergone exponentialtechnological development with the advancement of computer-generated imagery andthe proliferation of virtual worlds. While they may currently be too costly and complexto be integrated into some single libraries, they could be made more affordable by cost-and resource-sharing library initiatives. Librarians themselves must approachconversational agents as a useful tool to reducing a number of trivial and repetitivetasks, and shift in perception of the technology to enhance rather than threatentraditional library services. Library patrons may also need to become acclimatized toconversational agents in order for the agents to be viewed and trusted as importantinformational and educational resources. The value of conversational agents in libraryservice will be largely based on their ability to be accessed by a wide range of users,and their practical applications in everyday online environments.

A summary of studies by Foster (2007) indicates that the development ofanimated conversational agents with appropriate non-verbal behaviors has meritbecause the applications “can improve user satisfaction and engagement with acomputer system, and in some cases . . . can even improve users’ opinion of theobjects being described by the system” (p. 835). Although NLI systems areincreasingly sophisticated and better able to handle the complexities of humanlanguage than early prototypes, the use of such agents has limitations. It isarguable whether or not they will ever entirely capture the subtleties that areinherent to human-to-human conversation. However, scripted and predictableinteractions for educational, informational, assistive and socially interactivepurposes are being more widely used commercially. Conversational agents clearlyhave a long way to go before they can offer a truly authentic conversationalexperience, but the possibilities for libraries are certainly exciting.

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Table AI.

Artificiallyintelligent agents

in libraries

521

About the authorsVictoria L. Rubin is an Assistant Professor in the Faculty of Information and Media Studies andthe Principal Investigator of the Language and Information Technologies Research Lab at theUniversity of Western Ontario, London, Canada. She received her PhD in Information Scienceand Technology in 2006, her MA in Linguistics in 1997 from Syracuse University, NY, USA, anda BA in English, French, and Interpretation from Kharkov State University, Ukraine in 1993. Herresearch interests are in information organization and information technology. She specializes ininformation retrieval and natural language-processing techniques that enable analyses of textsto identify, extract, and organize structured knowledge. Victoria L. Rubin is the correspondingauthor and can be contacted at: vrubin@uwo.ca

Yimin Chen received his Master’s degree in Library and Information Science from theUniversity of Western Ontario in 2010 and a Bachelor of Science degree from the University ofBritish Columbia in 2006, majoring in Biology and English Literature. His research interestsinclude the interconnecting relationships between technology, culture, and literacy, with anemphasis on internet subcultures.

Lynne Marie Thorimbert received her Master’s degree in Library and Information Sciencefrom the University of Western Ontario in 2010 and a BA in Art History from the University ofCalgary in 2008. She is currently a Consultant Librarian with the Marigold Library System inAlberta, Canada. Her areas of interest include myth and folklore, young adult services, andgraphic novels, picture book and video game illustration. Her work has been presented at theOntario Library Association Super Conference and published in Access.

LHT28,4

522

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