Learning First-Order Symbolic Representations for Planning from the Structure of the State Space

12 September 2019

Papers citing "Learning First-Order Symbolic Representations for Planning from the Structure of the State Space"

8 / 8 papers shown

Title
Influence of the Geometry of the world model on Curiosity Based Exploration G. Sergeant-Perthuis Nils Ruet D. Rudrauf D. Ognibene Y. Tisserand 44 2 0 01 Apr 2023
Learning Efficient Abstract Planning Models that Choose What to Predict Nishanth Kumar Willie McClinton Rohan Chitnis Tom Silver Tomás Lozano-Pérez L. Kaelbling 32 18 0 16 Aug 2022
Learning First-Order Symbolic Planning Representations That Are Grounded Andrés Occhipinti Liberman Blai Bonet Hector Geffner NAI 24 7 0 25 Apr 2022
Online Learning of Reusable Abstract Models for Object Goal Navigation Tommaso Campari Leonardo Lamanna P. Traverso Luciano Serafini Lamberto Ballan EgoV 15 19 0 04 Mar 2022
Learning General Optimal Policies with Graph Neural Networks: Expressive Power, Transparency, and Limits Simon Ståhlberg Blai Bonet Hector Geffner 41 48 0 21 Sep 2021
Symbols as a Lingua Franca for Bridging Human-AI Chasm for Explainable and Advisable AI Systems Subbarao Kambhampati S. Sreedharan Mudit Verma Yantian Zha L. Guan 52 47 0 21 Sep 2021
Target Languages (vs. Inductive Biases) for Learning to Act and Plan Hector Geffner 42 6 0 15 Sep 2021
Learning First-Order Representations for Planning from Black-Box States: New Results I. D. Rodriguez Blai Bonet J. Romero Hector Geffner NAI 17 21 0 23 May 2021