  Evaluating the surroundings to gain understanding, frame perspectives, and
anticipate behavioral reactions is an inherent human trait. However, these
continuous encounters are diverse and complex, posing challenges to their study
and experimentation. Researchers have been able to isolate environmental
features and study their effect on human perception and behavior. However, the
research attempts to replicate and study human behaviors with proxies, such as
by integrating virtual mediums and interviews, have been inconsistent. Large
language models (LLMs) have recently been unveiled as capable of contextual
understanding and semantic reasoning. These models have been trained on large
amounts of text and have evolved to mimic believable human behavior. This study
explores the current advancements in Generative agents powered by LLMs with the
help of perceptual experiments. The experiment employs Generative agents to
interact with the urban environments using street view images to plan their
journey toward specific goals. The agents are given virtual personalities,
which make them distinguishable. They are also provided a memory database to
store their thoughts and essential visual information and retrieve it when
needed to plan their movement. Since LLMs do not possess embodiment, nor have
access to the visual realm, and lack a sense of motion or direction, we
designed movement and visual modules that help agents gain an overall
understanding of surroundings. The agents are further employed to rate the
surroundings they encounter based on their perceived sense of safety and
liveliness. As these agents store details in their memory, we query the
findings to get details regarding their thought processes. Overall, this study
experiments with current AI developments and their potential in simulated human
behavior in urban environments.
