Hierarchical Model of Human Guidance Performance Based on Interaction Patterns in Behavior
This work addresses the challenge of understanding and simulating human navigation for applications in robotics or human-computer interaction, but it appears incremental as it builds on existing concepts like hidden Markov models.
The paper tackles the problem of modeling human spatial guidance behavior in complex environments by introducing a framework based on interaction patterns, resulting in a hierarchical hidden Markov model that formalizes these behaviors.
This paper describes a framework for the investigation and modeling of human spatial guidance behavior in complex environments. The model is derived from the concept of interaction patterns, which represent the invariances or symmetries inherent in the interactions between an agent and its environment. These patterns provide the basic elements needed for the formalization of spatial behavior and determine a natural hierarchy that can be unified under a hierarchical hidden Markov model.