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Applying Neural-Symbolic Cognitive Agents in Intelligent Transport Systems to reduce CO2 emissions.

, , , , and . IJCNN, page 55-62. IEEE, (2014)

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Learning to adapt requirements specifications of evolving systems., , , and . ICSE, page 856-859. ACM, (2011)Continual Reasoning: Non-monotonic Reasoning in Neurosymbolic AI using Continual Learning., and . NeSy, volume 3432 of CEUR Workshop Proceedings, page 210-222. CEUR-WS.org, (2023)Inductive Learning in Shared Neural Multi-Spaces., , and . NeSy, volume 2003 of CEUR Workshop Proceedings, CEUR-WS.org, (2017)Measurable Counterfactual Local Explanations for Any Classifier., and . ECAI, volume 325 of Frontiers in Artificial Intelligence and Applications, page 2529-2535. IOS Press, (2020)Reasoning about Time and Knowledge in Neural Symbolic Learning Systems., and . NIPS, page 921-928. MIT Press, (2003)Neural-symbolic cognitive agents: architecture, theory and application., , , and . AAMAS, page 1621-1622. IFAAMAS/ACM, (2014)Applying the connectionist inductive learning and logic programming system to power system diagnosis., , and . ICNN, page 121-126. IEEE, (1997)Neuro-symbolic Representation of Logic Programs Defining Infinite Sets., , and . ICANN (1), volume 6352 of Lecture Notes in Computer Science, page 301-304. Springer, (2010)Multi-instance learning using recurrent neural networks., and . IJCNN, page 1-6. IEEE, (2012)Towards Reasoning about the Past in Neural-symbolic Systems., , and . NeSy, volume 230 of CEUR Workshop Proceedings, CEUR-WS.org, (2007)