TY - JOUR
T1 - Structurally Guided Task Decomposition in Spatial Navigation Tasks
AU - He, Ruiqi
AU - Correa, Carlos G.
AU - Griffiths, Thomas L.
AU - Ho, Mark K.
N1 - Publisher Copyright:
Copyright © 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2024/3/25
Y1 - 2024/3/25
N2 - How are people able to plan so efficiently despite limited cognitive resources? We aimed to answer this question by extending an existing model of human task decomposition that can explain simple planning problems by adding structure information to facilitate planning in more complex tasks. The extended model was applied to a more complex planning domain of spatial navigation. Our results suggest that our framework can correctly predict the navigation strategies of the majority of the participants in an online experiment.
AB - How are people able to plan so efficiently despite limited cognitive resources? We aimed to answer this question by extending an existing model of human task decomposition that can explain simple planning problems by adding structure information to facilitate planning in more complex tasks. The extended model was applied to a more complex planning domain of spatial navigation. Our results suggest that our framework can correctly predict the navigation strategies of the majority of the participants in an online experiment.
UR - http://www.scopus.com/inward/record.url?scp=85189629872&partnerID=8YFLogxK
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U2 - 10.1609/aaai.v38i21.30451
DO - 10.1609/aaai.v38i21.30451
M3 - Conference article
AN - SCOPUS:85189629872
SN - 2159-5399
VL - 38
SP - 23512
EP - 23513
JO - Proceedings of the AAAI Conference on Artificial Intelligence
JF - Proceedings of the AAAI Conference on Artificial Intelligence
IS - 21
T2 - 38th AAAI Conference on Artificial Intelligence, AAAI 2024
Y2 - 20 February 2024 through 27 February 2024
ER -