UNDERSTANDER Business Intelligence Seeker — User agent
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.055 | 0.013 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".