Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence A Neural-Symbolic Cognitive Agent for Online Learning and Reasoning

Citations in the corpus

• Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning (2019, methodology): “Extraction of temporal rules have been studied in [34] and generated semantic relations of domain variables over time.”
• Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge (2020, background): “Various neural-symbolic methods have attempted to unify these two extremes (Pinkas, 1991; de Penning et al., 2011; Besold et al., 2017) including many cognitive architectures which used hybrid symbolic and subsymbolic systems (Laird et al., 2017), and more recently, compositional query languages…”
• Deep Logic Networks: Inserting and Extracting Knowledge From Deep Belief Networks (2018, unknown): “A more recent development in neural–symbolic integration is the neural–symbolic cognitive agent (NSCA) introduced in [17] in which a model based on RTRBMs is proposed to represent temporal symbolic knowledge and applied to online learning and reasoning.”
• Neural, symbolic and neural-symbolic reasoning on knowledge graphs (2021, background): “…problem of combining symbolism and connectionism has been researched since the 1980s (Gallant, 1988, 1993; Shavlik et al., 1991; Towell and Shavlik, 1994a, 1994b), and has drawn much attention recent years (Besold et al., 2017; de Penning et al., 2011; Garcez et al., 2012, 2019; Lamb et al., 2020).”
• Neural-Symbolic Learning and Reasoning: A Survey and Interpretation (2017, methodology): “NSCA was able to learn from these observations and infer assessment scores that are similar to those of the driving instructors (de Penning et al., 2010, 2011).”