  Many decision-making problems feature multiple objectives. In such problems,
it is not always possible to know the preferences of a decision-maker for
different objectives. However, it is often possible to observe the behavior of
decision-makers. In multi-objective decision-making, preference inference is
the process of inferring the preferences of a decision-maker for different
objectives. This research proposes a Dynamic Weight-based Preference Inference
(DWPI) algorithm that can infer the preferences of agents acting in
multi-objective decision-making problems, based on observed behavior
trajectories in the environment. The proposed method is evaluated on three
multi-objective Markov decision processes: Deep Sea Treasure, Traffic, and Item
Gathering. The performance of the proposed DWPI approach is compared to two
existing preference inference methods from the literature, and empirical
results demonstrate significant improvements compared to the baseline
algorithms, in terms of both time requirements and accuracy of the inferred
preferences. The Dynamic Weight-based Preference Inference algorithm also
maintains its performance when inferring preferences for sub-optimal behavior
demonstrations. In addition to its impressive performance, the Dynamic
Weight-based Preference Inference algorithm does not require any interactions
during training with the agent whose preferences are inferred, all that is
required is a trajectory of observed behavior.
