Identification of Unexpected Decisions in Partially Observable Monte-Carlo Planning: a Rule-Based Approach

23 December 2020

Papers citing "Identification of Unexpected Decisions in Partially Observable Monte-Carlo Planning: a Rule-Based Approach"

8 / 8 papers shown

Title
Towards Explainable AI Planning as a Service Michael Cashmore Anna Collins Benjamin Krarup Senka Krivic Daniele Magazzeni David Smith 33 50 0 14 Aug 2019
An Inductive Synthesis Framework for Verifiable Reinforcement Learning He Zhu Zikang Xiong Stephen Magill Suresh Jagannathan 35 95 0 16 Jul 2019
Verifiable Reinforcement Learning via Policy Extraction Osbert Bastani Yewen Pu Armando Solar-Lezama OffRL 104 331 0 22 May 2018
Bounded Policy Synthesis for POMDPs with Safe-Reachability Objectives Yue Wang Swarat Chaudhuri Lydia E. Kavraki 53 30 0 29 Jan 2018
Explainable Planning M. Fox D. Long Daniele Magazzeni 32 287 0 29 Sep 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 278 1,849 0 03 Feb 2017
Safety Verification of Deep Neural Networks Xiaowei Huang Marta Kwiatkowska Sen Wang Min Wu AAML 196 935 0 21 Oct 2016
Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes A. Cassandra Michael L. Littman N. Zhang 57 508 0 06 Feb 2013