Quantitative Assessment Model for Technology Transfer Risks in University‐Enterprise Collaborative Innovation: Based on Multi‐Objective Optimization Strategy of Deep Adversarial Reinforcement Learning.
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| Title: | Quantitative Assessment Model for Technology Transfer Risks in University‐Enterprise Collaborative Innovation: Based on Multi‐Objective Optimization Strategy of Deep Adversarial Reinforcement Learning. |
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| Authors: | Wang, Guojun |
| Source: | Engineering Reports; Jan2026, Vol. 8 Issue 1, p1-20, 20p |
| Subject Terms: | TECHNOLOGY transfer, RISK assessment, INNOVATION management, DECISION making, DEEP learning, MULTI-objective optimization, REINFORCEMENT learning, SEMICONDUCTOR manufacturing |
| Abstract: | As a core model for promoting technological industrialization, school‐enterprise collaborative innovation faces multi‐dimensional risks from technology maturity fluctuations, market policy changes, and multi‐party interest games. Traditional risk assessment methods like AHP and SVM rely on static indicators and single‐objective optimization, struggling with dynamic constraints. While multi‐objective evolutionary algorithms handle objective conflicts, they suffer from low convergence efficiency in high‐dimensional spaces and poor real‐time performance. Data sparsity and limited emergency scenario generation further restrict industrial applicability. This study proposes a technology transfer risk assessment framework integrating deep adversarial reinforcement learning with multi‐objective optimization. We develop a physically‐constrained adversarial generation network to simulate technology failure distributions and market fluctuation patterns, generating high‐fidelity risk scenarios. Combined with the Proximal Policy Optimization algorithm, we design a dynamic decision‐making mechanism that simultaneously optimizes risk control costs, technology transfer efficiency, and patent revenue. Experiments in semiconductor manufacturing and new energy batteries demonstrate significantly improved assessment accuracy and decision‐making speed. The framework achieves a 92% decision correction rate with 1.2‐h response delay in emergencies, overcoming traditional methods' limitations in dynamic multi‐objective collaboration. Adaptive reference point strategy and adversarial training effectively address data distribution bias and noise interference, providing practical intelligent decision support for school‐enterprise innovation. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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