**HICSS symposium: \'Skilled Human-Intelligent Agent Performance:
Measurement, Application, and Symbiosis\', January 4, 2006**

**Human -Agent collaboration**

Luz M. Quiroga

Assistant Professor

Information & Computer Sciences - Library and Information Science

University of Hawai\'i at Manoa

lquiroga@hawaii.edu

The purpose of this paper is to illustrate how research, theories, and
practice in Library and Information Science (LIS) have a long tradition
in studying user -- librarian collaboration, where the librarian, acting
as an agent and intermediary between information and users, helps users
to find the information that is more relevant to their information
needs. Librarians learn about user information needs and, more
importantly, about the context and current user situation. Librarians
also have knowledge of the characteristics of the multiple types of
information and information systems available. Having information about
the user and the system allows them to match user needs to relevant
information.

For decades, user studies have been one of the most serious research
areas in LIS. This research is centered on the user who is portrayed as
the main player in the success of information systems. Experiments,
evaluations, and many assessment methods have resulted in models and
theories about cognitive and emotional characteristics of users.
Differences in information seeking behavior, based on experience with
the system, knowledge of the topic, age, gender, population, motivation,
personality, individual organization behavior, and individual ways to
cope with affective issues are only some of the variables analyzed that
influence user satisfaction while interacting with systems (Belkin,
2000; Kuhlthau et al., 1992; Julien, 2005).

Types of difficulties in finding relevant information are reviewed and
specific scenarios where collaboration between an automated agent and a
user can be helpful are presented. They include areas such as search
interfaces, specialized reference services, maintenance of personal
digital collections and libraries, and collaborative filtering
techniques. The experiences of information professionals could be used
as a model to design automated agents in order to provide a more
personalized service, which is much needed in this era of information
overload.

**I. Difficulties in Obtaining Relevant Information in a Timely
Manner.**

Many factors contribute to the difficulties in obtaining information in
a timely manner. Some of the factors are related to the information per
se while others, maybe more importantly, are related to the user who is
in need of relevant information.

***Information Aspects***

In addition to the wealth of information that is produced continuously,
other factors make obtaining relevant information a difficult task.
Information we produce or consume is scattered in sometimes ubiquitous
devices (e.g., servers, laptops, personal -- work computers, cell
phones, and i-pods), produced in many different media (e.g., music,
voice, graphics, and image), and belongs to diverse genres (e.g.,
informal or scholarly papers, documents, business cards, tips,
recommendations, recipes, schedules, agendas, plans, and medications).

***User Behavior Aspects***

Numerous studies have reported how obtaining information is influenced
by many personal characteristics of the users. Individual cognitive and
psychological differences, differences in our own organization behavior,
life style, emotions, preferences, knowledge, and experience \-- to
mention a few \--, may result in the same information not being equally
relevant for apparently the same informational need (Nahl, 2004; Julien,
2005; Fidel, 1997). Researchers agreed that the specific situation of
the user, the context of the need, plays a critical role (Quiroga, 2002;
Jameson, 2001).

These are only two of a wide range of aspects of the problems that
demerit the interaction of users with information systems, but that
suffice as background to discuss the need of "automated agent -- user
collaboration" to solve information overload and to facilitate access to
relevant and timely information.

**II. Research and Practices Aimed at Facilitating Timely Delivery of
Information: Some Scenarios**

The descriptions below attempted to identify situations where human and
automated agents could cooperate and are aimed at eliciting thoughts
about what part should be done by the human and what part could be left
to the automated agent. How can human and agent collaborate to
complement their strengths?

***From Integrated Systems to Interoperable Systems***

Integrated systems are defined as "suite of interrelated modules that
perform a variety of library functions (e.g., circulation, cataloging,
public access, acquisitions, and serials) and share a common database"
(Bilal, 2002). Applications run under one operation system with common
rules for each application. It works well in the printed and more
controlled environment of physical objects. Now the trend and need has
moved to interoperable systems where software and hardware on different
machines from different vendors have the ability to share data
(webopedia.com). The advantages of interoperable systems point to access
to more resources, accessed via common interfaces thanks to agreed on
standards and protocols. For library users, one of advantages is the
possibility to perform what it is called a federated search, which
retrieves items from a distributed collection of information sources and
systems, using a single search interface and receiving integrated
results. If, in addition, the results are going to be filtered and
ranked according to the user profile, the system has to manage not only
the models of the different systems, but the model of individual users
as well.\
Questions that arise relate to the human -- system interaction, the
modeling of the information need, and the modeling of each system. What
kind of interface is needed that will help the user to find relevant
documents existing in disparate systems, formats, and media? How can
this interface map the features of each interoperable system so as to
include the characteristic of each individual user and the particular
context of their specific needs? What kind of modeling can be left to
the automated agent and what part is better controlled by the user or
information professional?

***Specialized Reference Service***

A long tradition in special libraries is to have the information
professional as a agent who alerts users of incoming relevant
information. One way to automate the task is by placing the information
professional in control of building and managing the profiles of the
library users. They know the topics of interest of each user, but more
importantly, they know the context in which the information is needed
\-- the projects, the mission of the institution, competitors, the
strengths and weaknesses of users/organizations, and so on. Therefore,
the profile, maintained by the information professional, moves from a
list of keywords to more sophisticated profiles that are continuously
tuned by the information professional as the context changes (e.g., the
priority or stage of a project). Many current SDI (Selective
Dissemination of Information) systems still work in this way, and there
is a lot to learn from information professionals acting as
intermediaries in the provision of relevant information to users
(Schlembach, 2001). For example, research on information professionals
as intermediaries frequently points to how important trust in the
communication specialist - user is. This points to needed research in
human- agent collaboration regarding what elements promote or impede
trusting agents.

***Personal Information Management: Personal Digital Libraries***

Years ago, handling personal records, although a difficult task, was not
the top concern. Nowadays, the term "Generation **C**"
(Trendwatching.com) is used to refer to the digital **C**ontent that
individuals are creating in different media and different formats
ranging from informal mail to scholarly papers, passing through blogs,
genealogical records, personal webpages, photo albums, family videos,
music collections, power point presentations, bookmarks, and so on
(Beagrie 2005). In addition to the information we produce, there is the
information that we consume: books, articles, music, videos, and so on.
A daunting task is organizing this information so we can find and reuse
it.

There are many issues related to organization and access of information.
Personal digital libraries, with organized, classified information are
needed so each person can track what they produce or consume (Renda &
Straccia, 2004). As the amount of information grows, agents to help with
this task are in demand. Also, given that humans have different
organization behavior (Kwasnik, 1989; Quiroga & Crosby 2004), human --
agent collaboration is a good approach. It will allow humans to have
control of their data while relying on the agent to maintain the
personal digital library and maintain order in the information space.
This collaboration is something that requires investigation: what
features are desired, what tasks can or cannot be delegated in order to
protect privacy and conform to our values and our own organization
behavior.

***Community of Practices and Collaborative Filtering***

Social networks and communities of practice (CoPs) are formed by groups
of people with similar interests that could be a field of study, hobby,
job, problem, and so on. In online forums, community members meet and
interact through virtual spaces such as bulletin boards or newsgroups.
Archives produced by members are rich sources for knowledge sharing and
creation (Quiroga, 2005). Community members face some problems of
interest to the discussion of agent -- human collaboration to find
relevant information.

The first issue is related to information overload. As postings and
interaction increase, it becomes more difficult to keep up with relevant
threads. To alleviate the problem, collaborative and social filtering
techniques could be employed. An agent can maintain profiles, find
peers, i.e. persons with similar profiles, and can provide
recommendations, and guide community members to resources and
discussions relevant to them, based on tracking the behavior and
preferences of the peers.

The second issue is related to improving access to community archives.
One possible solution is to maintain an ontology, i.e. "the basic terms
and relations comprising the vocabulary of a topic area as well as the
rules for combining terms and relations to define extensions to the
vocabulary" (Fensel, 2004). Maintaining an ontology could be a task
partially achieved by an agent. ONTOShare is a system used to share
ontologies where an agent extracts keywords and summaries from a virtual
environment, maps results to ontologies, and provides an alert when
something of interest occurs (Davies, et al.., 2002). Another
interesting example is the modeling of profiles as personal ontologies,
a personal conceptual map. These personal ontologies can be mapped to
search results, to general ontology, or to other personal ontologies
(Chaffee, 2000). Barriers to accepting collaborative filtering
techniques relate to trust and privacy, which might be better dealt with
by a human-agent collaboration approach instead of a completely
automated system.

**III. Conclusions**

Previous discussion makes evident the need for modeling users.
Maintaining and adjusting profiles of users is the base upon which the
enhancement and utility of information and systems is constructed. User
profiles for information retrieval and filtering commonly emphasize
modeling the topic of the information need, leaving the context in the
mind of the user or the intermediary. A promising research area is that
of addressing user profiling as a task where topic, cognitive, and
emotional factors are considered, as well as context and culture implied
in habits, hobbies, situations, environment, responsibilities, values,
and so on. (Quiroga and Mostafa, 2002). Agents and human experts could
collaborate in the creation and maintenance of such profiles to provide
a trusted service in supplying information. The question is how much to
leave to the user, how much to delegate to the information professional
serving as intermediary, and how much assign to the automated agent
(Bates, 19900. The complexity of obtaining information is increasing at
a rapid rate, so we are in need of human-agent collaboration in order to
provide create an environment that will emulate the services provided by
what Vannevar Bush described as, "\[the\] profession of trail blazers,
those who find delight in the task of establishing useful trails through
the enormous mass of the common record" (Bush 1945).

REFERENCES

Bates, M. (1990). Where should the person stop and the information
search interface start? Information Processing and Management, 26(5),
575-591.

Beagrie, N. (2005) \"Plenty of Room at the Bottom? Personal Digital
Libraries and Collections\". D-Lib Magazine 11(6)(June 2005)
<http://www.dlib.org/dlib/june05/beagrie/06beagrie.html>

Belkin, N.J. (2000). [Helping people find what they don\'t
know](http://portal.acm.org/citation.cfm?id=345143&coll=portal&dl=ACM&CFID=9373732&CFTOKEN=8495281).
Communications of the ACM, 43 (8)

Biggs, Mary (1989). Information overload and information seekers: What
we know about them, what to do about them. Reference Librarian, 25/26,
411-429.

Bilal, D. (2002). Automating media centers and small libraries. A
microcomputer- based approach. 2^nd^. ed. Greenwood Village, Colorado,
Libraries Unlimited.

Bush, V. (1945). As we may think. The Atlantic Monthly (July).

Chaffee, J. and Gauch, S.(2000). \"Personal Ontologies for Web
Navigation.\" Proceedings of the Conference on Information and Knowledge
Management: 227-234.

Davies, J., Duke A. and Stonnkus, A. (2002). OntoShare: Using ontologies
for knowledge sharing. Proceedings of the Semantic Web Workshop of the
Eleventh International World Wide Web Conference. Retrieved 13 December
2004 from
<http://www.semanticweb2002.aifb.uni-karsruhe.de/proceedings/research/davies.pdf>

Davies, J., Duke A. and Stonnkus, A. (2002). OntoShare: Using ontologies
for knowledge sharing. Proceedings of the Semantic Web Workshop of the
Eleventh International World Wide Web Conference. Retrieved 13 December
2004 from
http://www.semanticweb2002.aifb.uni-karsruhe.de/proceedings/research/davies.pdf.

Fensel, F. (2004). Ontologie: A silver bullet for knowledge management
and electronic commerce. 2^nd^. ed. New York: Springer.

Fidel, R. and Crandall, M. (1997). Users\' perception of the performance
of a filtering system. In SIGIR 97, 198-205. Philadelphia, PA.

Generation C . In online newsletter of Trendwatching.com at:
\<<http://www.trendwatching.com/trends/GENERATION_C.htm>\>

Jameson, A. (2001). Modelling both the context and the user. Personal
and ubiquitous computing., 5, 29-33.

Julien, H. (2005). What we've learned about the role of affect in
information behaviour/information retrieval. ISKO, 7o. Congreso del
Capitulo Espanol , Barcelona, 6-8 Julio. La dimension humana de la
organizacion del conocimiento, 342-356.

Kuhlthau, C., Spink, A. and Cool, C. (1992). Exploration into stages in
the information search process in online
information-retrieval-communication between users and intermediaries.
Proceedings of the 55^th^ ASIS Annual Meeting on Celebrating Change:
Information Management on the Move, Pittsburgh, Pa., 29, 67-71.

Kwasnik, B. (1989). The influence of context on classificatory behavior.
PhD dissertation. Rutgers, the State University of New Jersey - New
Brunswick.

Nalh, D. (2004). Measuring the affective information environment of web
searchers. Proceedings of the 67^th^ ASIST Annual Meeting. 41, 191-197.

Prell, C. (2003). "Community networking and social capital: Early
investigations". Journal of Computer-Mediated Communication 8(3):
http://jcmc.indiana.edu/vol8/issue3/

Quiroga, L. & Mostafa J. (2002). An experiment in building profiles in
information filtering: the role of context of user relevance feedback.
Information Processing and Management, 38, 671-694.

Quiroga, L. and Crosby M. E. (2004). "Information filtering". In:
Berkshire Encyclopedia of Human Computer Interaction: 351-355.

Quiroga, L. & Campbell J. (2005). Communities of Practice: Increasing
Social Capital through the Integration of Digital Libraries, Filtering,
Ontologies and Social Networking. Accepted to be presented at the
International Conference on Knowledge Mangament (ICKM), October 27, 2005

Quiroga, L. (2005). Sistemas de filtrado: un puente tecnológico entre la
oferta y demanda

de información en línea al servicio de la toma de decisiones. In: Yáñez
M.R, Villatoro, P. (comp). Las nuevas tecnologías de la información y de
la comunicación (TIC) y la institucionalidad social. Hacia una gestión
basada en el conocimiento. CEPAL, 49-57.

Renda, M. E & Straccia U. (2004). "A personalized collaborative Digital
Library environment: a model and an application", Information processing
and Management, 41, 5-21.

Schlembach, M.C. Trends in Current Awareness Services. Science &
Technology

Libraries, 2001. 20(2/3), 121-32.