MULTI-AGENT SYSTEM ARCHITECTURE TO TRADING SYSTEMS
Why this work is in the frame
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Bibliographic record
Abstract
Agents for online trading purpose can be seen as a tool that helps computer users to purchase products from distributed resources based on their interests and preferences. One of the major features that determine the success of trading agent is the ability to negotiate with other agents, because most trading tasks involve interaction among agents. This paper presents a peer-to-peer multi-agent system architecture for online trading. The main objective of this system is to address some of the shortcomings that are present in contemporary online trading systems that focused on providing solutions for specific trading issues, such as single attribute-based negotiation, the requirement of an electronic marketplace and variations and status changes within the network. The proposed system architecture is a multi-tier, multi-agent architecture. The system architecture consists of three types of agents that are classified based on their functionality: interface, resource and retrieval agents. The interface agents are the front-end of the system and able to interact with different users to fulfill their needs. At the middle-tier, the resource agents access and capture the contents and the changes of the local information database. The retrieval agents are the back-end of the system and able to travel and interact with other agents at remote host machines. A prototype of this system is implemented using the IBM Aglet SDK.
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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 | 0.000 |
| 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 it