Basic Reasoning with Tensor Product Representations
Not on the map. Why?
Abstract (arXiv)
In this paper we present the initial development of a general theory for mapping inference in predicate logic to computation over Tensor Product Representations (TPRs; Smolensky (1990), Smolensky & Legendre (2006)). After an initial brief synopsis of TPRs (Section 0), we begin with particular examples of inference with TPRs in the 'bAbI' question-answering task of Weston et al. (2015) (Section 1). We then present a simplification of the general analysis that suffices for the bAbI task (Section 2). Finally, we lay out the general treatment of inference over TPRs (Section 3). We also show the simplification in Section 2 derives the inference methods described in Lee et al. (2016); this shows how the simple methods of Lee et al. (2016) can be formally extended to more general reasoning tasks.
Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). First posted on arXiv 12 Jan 2016.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 1; 2024: 2; 2025: 1.
4 papers.
- 2024A Survey on Verification and Validation, Testing and Evaluations of Neurosymbolic Artificial Intelligencecited by 10
- 2025Neuro-Symbolic Frameworks: Conceptual Characterization and Empirical Comparative Analysiscited by 2
- 2023What can knowledge graph alignment gain with Neuro-Symbolic learning approaches?cited by 0
- 2024A Review of Mechanistic Models of Event Comprehensioncited by 0
- 2025Neuro-Symbolic Frameworks: Conceptual Characterization and Empirical Comparative Analysis8 Sep 2025
- 2024A Review of Mechanistic Models of Event Comprehension17 Sep 2024
- 2024A Survey on Verification and Validation, Testing and Evaluations of Neurosymbolic Artificial Intelligence6 Jan 2024
- 2023What can knowledge graph alignment gain with Neuro-Symbolic learning approaches?11 Oct 2023