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Learning regulation functions of metabolic systems by artificial neural networks., and . GECCO, page 193-200. ACM, (2009)Rule-based Shielding for Partially Observable Monte-Carlo Planning., , and . ICAPS, page 243-251. AAAI Press, (2021)From POMDP Executions to Probabilistic Axioms., , , and . OVERLAY@AI*IA, volume 3311 of CEUR Workshop Proceedings, page 93-98. CEUR-WS.org, (2022)Policy Interpretation for Partially Observable Monte-Carlo Planning: a Rule-based Approach., , and . AIRO@AI*IA, volume 2806 of CEUR Workshop Proceedings, page 44-48. CEUR-WS.org, (2020)HMMs for Anomaly Detection in Autonomous Robots., , , , and . AAMAS, page 105-113. International Foundation for Autonomous Agents and Multiagent Systems, (2020)Identification of Unexpected Decisions in Partially Observable Monte-Carlo Planning: A Rule-Based Approach., , and . AAMAS, page 889-897. ACM, (2021)I2EDL: Interactive Instruction Error Detection and Localization., , , , , , , and . CoRR, (2024)Bayesian Clustering of Multivariate Immunological Data., and . LOD, volume 11331 of Lecture Notes in Computer Science, page 506-519. Springer, (2018)Learning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approach., , and . J. Artif. Intell. Res., (2024)Influence of State-Variable Constraints on Partially Observable Monte Carlo Planning., , and . IJCAI, page 5540-5546. ijcai.org, (2019)