Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hippocampal formation-inspired probabilistic generative model   In building artificial intelligence (AI) agents, referring to how brains
function in real environments can accelerate development by reducing the design
space. In this study, we propose a probabilistic generative model (PGM) for
navigation in uncertain environments by integrating the neuroscientific
knowledge of hippocampal formation (HF) and the engineering knowledge in
robotics and AI, namely, simultaneous localization and mapping (SLAM). We
follow the approach of brain reference architecture (BRA) (Yamakawa, 2021) to
compose the PGM and outline how to verify the model. To this end, we survey and
discuss the relationship between the HF findings and SLAM models. The proposed
hippocampal formation-inspired probabilistic generative model (HF-PGM) is
designed to be highly consistent with the anatomical structure and functions of
the HF. By referencing the brain, we elaborate on the importance of integration
of egocentric/allocentric information from the entorhinal cortex to the
hippocampus and the use of discrete-event queues.

23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg