The current schema-based approach has several limitations, particularly in its ability to generalize to complex environments with dynamic elements, such as moving obstacles or varying sensory inputs. One significant challenge is the reliance on a fixed metric representation, which may not adequately capture the nuances of more intricate spatial layouts or the interactions between multiple agents. Additionally, the schemas may struggle with tasks that require multi-modal integration, where information from different sensory modalities must be combined to inform decision-making.
To improve the schema-based approach, several strategies can be employed. First, incorporating more sophisticated representations, such as hierarchical or graph-based structures, could enhance the agent's ability to model complex relationships within the environment. Second, integrating reinforcement learning techniques with the schema framework could allow for adaptive learning policies that evolve based on environmental feedback, enabling the agent to navigate and learn in real-time. Finally, enhancing the working memory mechanisms to better filter out distractions and prioritize relevant cues could significantly improve performance in cluttered or dynamic settings.