Preference Inference from Demonstration in Multi-objective Multi-agent Decision Making

Autor:	Lu, Junlin
Rok vydání:	2023
Předmět:	Computer Science - Artificial Intelligence Computer Science - Multiagent Systems
Druh dokumentu:	Working Paper
Popis:	It is challenging to quantify numerical preferences for different objectives in a multi-objective decision-making problem. However, the demonstrations of a user are often accessible. We propose an algorithm to infer linear preference weights from either optimal or near-optimal demonstrations. The algorithm is evaluated in three environments with two baseline methods. Empirical results demonstrate significant improvements compared to the baseline algorithms, in terms of both time requirements and accuracy of the inferred preferences. In future work, we plan to evaluate the algorithm's effectiveness in a multi-agent system, where one of the agents is enabled to infer the preferences of an opponent using our preference inference algorithm. Comment: This work is accepted by the Doctoral Consortium of AAMAS 2023, London
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2304.14126 Zobrazit plný text záznamu View this record from Arxiv