RedAgent-2003: An Autonomous Market-Based Supply-Chain Management Agent
Bibliographic record
Abstract
The Supply Chain Management track of the international Trading Agents Competition (TAC SCM) was introduced in 2003 as a test-bed for researchers interested in building autonomous agents that act in dynamic supply chains. TAC SCM provides a challenging scenario for existing AI decision-making algorithms, due to the high dimensionality and the non-determinism of the environment, as well as the combinatorial nature of the problem. In this paper we present RedAgent, the winner of the first TAC SCM competition. RedAgent is based on a multi-agent design, in which many simple, heuristic agents manage tasks such as fulfilling customer orders or procuring particular resources. The key idea is to use internal markets as the main decision mechanism, in order to determine what products to focus on and how to allocate the existing resources. The internal markets ensure the coordination of the individual agents, but at the same time provide price estimates for the goods that RedAgent has to sell and purchase, a key feature in this domain. We describe RedAgentýs architecture and analyze its behavior based on data from the competition.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.063 | 0.008 |
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; both teacher heads agree on what is shown here.
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".