Business-to-Consumer Mobile Agent-Based Internet Commerce System (MAGICS)
Bibliographic record
Abstract
We present MAGICS, a mobile agent-based system for supporting business-to-consumer electronic commerce (e-commerce) or mobile commerce (m-commerce) applications. To use the system, consumers first provide their buying requirements to a proxy/agent server through a Web browser or a wireless application protocol (WAP) terminal. Having obtained the requirements, mobile agents are generated to carry out tasks for the consumers including getting offers from merchants, evaluating offers, and even completing purchases. In the case of mobile commerce, consumers can generate a mobile agent to conduct a search and evaluation in the digital marketspace before making a purchase in the physical marketplace. To make it possible to choose an offer that best satisfies the consumer's requirement(s), we present a mathematical model for evaluating multiple decision factors. To test the basic functions of the mobile agent-based Internet commerce system (MAGICS), we have built a prototype system. To minimize the average cost of a product (including the cost of sending agents), we have also developed an analytical model that can determine how many agents should be sent to compare prices. Four different price distributions and some real price information are analyzed based on the model. The analysis provides valuable insights into the design of mobile agent-based shopping applications for m-commerce, in particular, and for e-commerce, in general.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".