UNDERSTANDER Business Intelligence Seeker — User agent
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
UNDERSTANDER is an exploratory R&D project investigating Business Intelligence (BI) as a knowledge-gathering tool for supporting innovation processes in specific industrial domains. We develop a knowledge-based model that is grounded on the following three concepts: (i) Business Intelligence Model (BIM) developed by a Canadian project for BI technologies, (ii) taxonomy of Competitive Intelligence (CI) developed by CI practitioners, and (iii) Conceptual Dependency (CD) theory dealing with verb-oriented organization of knowledge. We also investigate whether knowledge-based models can be made adequate for the problem of gathering BI from web resources. Such a knowledge model can be used to “prime” a software agent and the agent can then search autonomously for relevant information on the Web. To express the interaction between agents, we consider “mechanism design”, which is a framework for designing interaction between self-interested agents to achieve specific outcomes. A BI Seeker is a multi-agent application developed in WADE. (Workflows and Agents Development Environment). WADE is an extension of JADE, a popular Open Source framework that brings workflow mechanisms to agents. We present the design of the BI Seeker as an application in the field of Industrial Internet.
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 | 0.001 |
| 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