Application of intelligent agent technology for knowledge management integration
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
Organizations invest in various knowledge management (KM) systems and tools to enable seamless integration of the constantly increasing volume and sources of information. This research first, presents a case study on the existing categories of KM systems and tools, their potential contribution to the KM process, and their pitfalls; second, it proposes a comprehensive methodology for building KM through the organization using software agent technology. This approach aims to address the research issue of how KM can be optimized using intelligent agents and how to enhance decision-making process. The proposed system is applied to a real-world project lifecycle case that is EPC (Engineering Procurement and Construction) project. A prototype of the system is presented where intelligent agents are the building blocks of a peer-to-peer organization wide system. The application was implemented using Eclipse technology, and the agents were deployed on the FIPA-OS (Foundation for Intelligent Physical Agents-Open-Source) environment, we used JESS (Java expert system shell) to develop the knowledge based of the agents' reasoning.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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