Paper Title
PERSONALIZED TASK ASSIGNMENT IN MULTI-AGENT SYSTEMS BASED ON A DEADLINE MODEL

Abstract
The paper presents a new approach to task allocation in multi-agent systems that considers customer profiles, task parameters, and the state of executors and uses ideas from scheduling theory for online models with deadlines. The proposed method is adapted for work in real-time systems, where tasks arrive dynamically, and assignment decisions must be made promptly, considering constraints on execution time and executor availability. A method aimed at minimizing delays and increasing customer satisfaction is proposed. Experiments on synthetic data have demonstrated that the proposed method is effective for tasks with tight deadlines. It is also shown that the inclusion of task personalization based on customer profiles and their willingness to wait improves system performance. In conclusion, the possibilities of applying the proposed approaches to tasks without human participation, such as resource allocation in automated systems, are discussed. Keywords - Personalized Assignment, Multi-Agent Systems, Scheduling Theory, Real-Time System.