Abstract
Stimulus equivalence is the ability to act as if two objects are identical, despite no shared properties. This ability is hypothesized to be the foundation for symbolic reasoning and the development of language. It is believed to be unique to humans and not present in other animals. Stimulus equivalence can be studied in the context of a matching-to-sample experimental task, by demonstrating a combination of symmetrical and transitive performances. This study aimed to explore stimulus equivalence with the Non-Axiomatic Reasoning System (NARS). More specifically, we propose two new capabilities for OpenNARS for Applications (ONA) - contingency entailment and acquired relations. We provide an explanation how this would lead to ONA being able to learn symmetrical and transitive performances leading to full stimulus equivalence.
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References
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Acknowledgements
We want to acknowledge Patrick Hammer, Robert Wünsche and Pei Wang for valuable discussions regarding this work.
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Johansson, R., Lofthouse, T. (2023). Stimulus Equivalence in NARS. In: Hammer, P., Alirezaie, M., Strannegård, C. (eds) Artificial General Intelligence. AGI 2023. Lecture Notes in Computer Science(), vol 13921. Springer, Cham. https://doi.org/10.1007/978-3-031-33469-6_16
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DOI: https://doi.org/10.1007/978-3-031-33469-6_16
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