Optimal Inference of Hidden Markov Models Through Expert-Acquired Data.

ABSTRACT: This paper focuses on inferring a general class of hidden Markov models (HMMs) using data acquired from experts. Expert-acquired data contain decisions/actions made by humans/users for various objectives, such as navigation data reflecting drivers' behavior, cybersecurity data carrying defender decisions, and biological data containing the biologist's actions (e.g., interventions and experiments). Conventional inference methods rely on temporal changes in data without accounting for expert knowledge. This paper incorporates expert knowledge into the inference of HMMs by modeling expert behavior as an imperfect reinforcement learning agent. The proposed method optimally quantifies experts' perceptions about the system model, which, alongside the temporal changes in data, contributes to the

SUBMITTER: Ravari A 

PROVIDER: S-EPMC11324238 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature