An interactive system for negotiation in e-commerce with incremental user knowledge
Bibliographic record
Abstract
In retail electronic commerce, incomplete user knowledge is a reality that must be addressed by electronic negotiation models and systems. This is particularly true in the case of multi-attribute products where valid product-configurations may require several constraints on attribute-values to be satisfied. Often, in such cases, the individual buyer refines the preferences for individual attributes as more and more information is exchanged during the negotiation process in an incremental fashion. In this paper, we consider how the negotiating parties can benefit from the incremental knowledge as the negotiation progresses. We assume the trust between the customer and merchant is such that the negotiation is for the purpose of seeking a mutually acceptable configuration of the product and its price. We have implemented a prototype system in which negotiation takes place between a human customer and multiple autonomous software agents, each carrying out sales operations on behalf of different merchants. We also describe the architecture of such an interactive multi-issue negotiation system on a distributed platform. The paper describes the various models we have used, and the multi-agent based software architecture that facilitates the interaction and the user interface. Our initial experiences with the prototype gives hope that e-commerce negotiation systems, in future, can benefit by making use of the incremental knowledge during the negotiation process.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".