A multi-attribute utility theoretic negotiation architecture for electronic commerce
Bibliographic record
Abstract
We present a generic negotiation architecture that uses MultiAttribute Utility Theory (MAUT) principles to reach agreements that satisfy multiple interdependent objectives. The architecture is built by giving a constraint optimization formulation to the MAUT principles and by using a constraint optimization solver to find the best 'deals' from an agent's local perspective. These are then proposed to other agents via a second component that supports conversational interactions among agents. When received proposals are disjoint from what an agent can currently accept, we provide a systematic constraint relaxation protocol that allows agents to generate the next acceptable 'deal'. This protocol ensures that in the end the Pareto optimal deal will be found, if one exists. The approach is built on top of our Negotiation Engine, a generic architecture for coordination and negotiation that integrates local reasoning, in the form of propositional constraint optimization, with interaction, in the form of conversational exchanges. The system is fully operational, being currently used to automate negotiations in the electronic components domain.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".