The application of intelligent agents in libraries: a survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this article is to provide a comprehensive literature review on the utilisation of intelligent agent technology in the library environment. Design/methodology/approach Research papers since 1990 on the use of various intelligent agent technologies in libraries are divided into two main application areas: digital library (DL), including agent‐based DL projects, multi‐agent architecture for DLs, intelligent agents for distributed heterogeneous information retrieval and agent support to information search process in DLs; and services in traditional libraries, including user interface for library information systems, automatic reference services and multi‐agent architecture for library services. For each paper on the topic, its new ideas or models, referred work, analyses, experiments, findings and conclusions are addressed. Findings The majority of the literature covers DLs and there have been fewer studies about services in traditional libraries. A variety of architecture, framework and models integrating agent technology in library systems or services are proposed, but only a few have been implemented in the practical environment. The application of agent technology is still at the research and experimentation stage. Agent technology has great potential in many areas in the library context; however it presents challenges to libraries that want to be involved in its adoption. Practical implications The survey has practical implications for libraries, librarians and computer professionals in developing projects that employ intelligent agent technology to meet end‐users' expectations as well as to improve information services within limited resources in library settings. Originality/value The paper provides a comprehensive survey on the development and research of intelligent agents in libraries in literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.010 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it