Preference Inference from Demonstration in Multi-objective Multi-agent Decision Making
This addresses the problem of preference inference for users in multi-objective decision-making, though it appears incremental as it builds on existing methods with specific enhancements.
The paper tackles the challenge of quantifying numerical preferences for different objectives in multi-objective decision-making by proposing an algorithm to infer linear preference weights from optimal or near-optimal demonstrations. Empirical results show significant improvements in time requirements and accuracy compared to baseline methods.
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.