Intelligent Information Retrieval Using Mobile Agents: A Proximal Policy Optimization Approach in Dynamic Networks
Bibliographic record
Abstract
Efficient information retrieval in distributed and dynamic networks remains challenging due to evolving network topologies, variable node availability, and resource constraints. In this paper, we introduce the Intelligent Reinforcement-based Mobile Agent (IRMA) framework, which utilizes Proximal Policy Optimization (PPO) to enable adaptive routing of mobile intelligent agents (MIAs). The IRMA framework was implemented and rigorously evaluated within a simulated network of 200 nodes. Comparative analyses against traditional methods—such as shortest-path, heuristic, and Deep Q-Network (DQN)-based routing—demonstrate substantial improvements. Empirical results indicate that IRMA significantly reduces latency by approximately 35%, lowers energy consumption by 25%, and enhances successful data retrieval rates by 20–30%. Statistical validation employing 95% confidence intervals and p-values below 0.05 confirms these performance enhancements. This research substantiates IRMA as a robust, scalable, and practical solution for intelligent information retrieval in dynamic network environments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".