Tensor Logic Thesis Ontology
type: Ontology
description: Lightweight ontology modeling Pedro Domingos's Tensor Logic framework as presented at ODSC AI East 2026 — a named set of ordered principles unifying symbolic logic and tensor-based deep learning.

Tensor Logic Principle
type: Class
comment: One ordered, named building block of the Tensor Logic framework, as introduced by Pedro Domingos, that together let logic programs and tensor computation be expressed as a single mathematical construct.

enables capability
type: Property
comment: The specific capability or advantage a Tensor Logic principle contributes toward unifying symbolic AI and deep learning.
domain: Tensor Logic Principle
range: string

Neural Architecture Expressed in Tensor Logic
type: Class
comment: A neural network architecture that Tensor Logic can express as one or two concise tensor equations.

expressed as
type: Property
comment: How a neural architecture is captured in Tensor Logic notation.
domain: Neural Architecture Expressed in Tensor Logic
range: string

Prior AI Language or Tool
type: Class
comment: A historical medium of expression for AI (a programming language, notation, or library) that Tensor Logic is positioned as synthesizing or superseding.

strength
type: Property
comment: What a prior AI language or tool did well.
domain: Prior AI Language or Tool
range: string

limitation
type: Property
comment: What a prior AI language or tool struggled with, motivating Tensor Logic.
domain: Prior AI Language or Tool
range: string

is official implementation
type: Property
comment: Whether a software implementation of Tensor Logic was released by Pedro Domingos himself (true) or is an independent third-party/community reimplementation based on his published paper (false). As of this writing, no official implementation from Domingos is known to be published.
domain: SoftwareSourceCode
range: boolean

ODSC AI East 2026 — Pedro Domingos on Tensor Logic: The Language of AI
type: AnalysisNewsArticle
type: ScholarlyArticle
headline: Tensor Logic: The Language of AI
description: Recap of Pedro Domingos's ODSC AI East 2026 keynote proposing Tensor Logic, a unifying language that merges tensor algebra and logic programming so that symbolic reasoning and gradient-based deep learning become mathematically identical rather than separate paradigms.
date published: 2026-05-27
author: ODSC - Open Data Science
publisher: ODSC - Open Data Science
about: Tensor Logic
mentions: Pedro Domingos
mentions: University of Washington
mentions: ODSC AI East 2026
mentions: Python
mentions: PyTorch
mentions: NumPy
citation: Tensor Logic: The Language of AI
has part: Frequently Asked Questions
has part: Glossary
has part: Tensor Logic Principles
has part: Architectures Expressed in Tensor Logic
has part: Prior AI Languages and Tools
has part: Quotations
has part: Known Implementations
was generated by: kg-generator

Frequently Asked Questions
is part of: ODSC AI East 2026 — Pedro Domingos on Tensor Logic: The Language of AI
type: FAQPage
main entity: What is Tensor Logic?
main entity: Why does AI need a 'definitive language' at all?
main entity: What problem in AI is Tensor Logic trying to solve?
main entity: How does Tensor Logic represent a logical fact like parent(Bob, Chris)?
main entity: What is Einstein Summation (EinSum) and why does it matter here?
main entity: What is tensor projection?
main entity: What is tensor join, and how does it relate to database joins?
main entity: What is 'backpropagation through structure'?
main entity: Why is the gradient of a Tensor Logic program 'just another Tensor Logic program'?
main entity: Can complex architectures like Transformers really be written in one or two equations?
main entity: Which existing tools already implement pieces of Tensor Logic's approach?
main entity: Why does interpretability matter enough to justify a new language?
main entity: Who is Pedro Domingos?
main entity: What historical AI languages does Tensor Logic build on or move past?
main entity: Are there any known systems using Tensor Logic?
has part: What is Tensor Logic?
has part: Why does AI need a 'definitive language' at all?
has part: What problem in AI is Tensor Logic trying to solve?
has part: How does Tensor Logic represent a logical fact like parent(Bob, Chris)?
has part: What is Einstein Summation (EinSum) and why does it matter here?
has part: What is tensor projection?
has part: What is tensor join, and how does it relate to database joins?
has part: What is 'backpropagation through structure'?
has part: Why is the gradient of a Tensor Logic program 'just another Tensor Logic program'?
has part: Can complex architectures like Transformers really be written in one or two equations?
has part: Which existing tools already implement pieces of Tensor Logic's approach?
has part: Why does interpretability matter enough to justify a new language?
has part: Who is Pedro Domingos?
has part: What historical AI languages does Tensor Logic build on or move past?
has part: Are there any known systems using Tensor Logic?

Glossary
is part of: ODSC AI East 2026 — Pedro Domingos on Tensor Logic: The Language of AI
type: ConceptScheme
type: DefinedTermSet
has defined term: Tensor Logic
has defined term: Tensor
has defined term: Sparse Boolean Tensor
has defined term: Einstein Summation (EinSum)
has defined term: Tensor Projection
has defined term: Tensor Join
has defined term: Backpropagation Through Structure
has defined term: Neuro-Symbolic AI
has defined term: Symbolic AI
has defined term: Deep Learning
has defined term: Database Join
has defined term: Step Function
has part: Tensor Logic
has part: Tensor
has part: Sparse Boolean Tensor
has part: Einstein Summation (EinSum)
has part: Tensor Projection
has part: Tensor Join
has part: Backpropagation Through Structure
has part: Neuro-Symbolic AI
has part: Symbolic AI
has part: Deep Learning
has part: Database Join
has part: Step Function

Tensor Logic Principles
is part of: ODSC AI East 2026 — Pedro Domingos on Tensor Logic: The Language of AI
type: ItemList
description: The ordered sequence of ideas Tensor Logic builds on, from unifying data structures through to expressing complete neural architectures.
item list element: Relations as Sparse Boolean Tensors
item list element: Einstein Summation (EinSum)
item list element: Tensor Projection
item list element: Tensor Join
item list element: Backpropagation Through Structure
item list element: Architecture Compression
has part: Relations as Sparse Boolean Tensors
has part: Einstein Summation (EinSum)
has part: Tensor Projection
has part: Tensor Join
has part: Backpropagation Through Structure
has part: Architecture Compression

Architectures Expressed in Tensor Logic
is part of: ODSC AI East 2026 — Pedro Domingos on Tensor Logic: The Language of AI
type: ItemList
item list element: Perceptron
item list element: Recurrent Neural Network (RNN)
item list element: Convolutional Neural Network (CNN)
item list element: Transformer
has part: Perceptron
has part: Recurrent Neural Network (RNN)
has part: Convolutional Neural Network (CNN)
has part: Transformer

Prior AI Languages and Tools
is part of: ODSC AI East 2026 — Pedro Domingos on Tensor Logic: The Language of AI
type: ItemList
item list element: Lisp
item list element: Prolog
item list element: 1990s Graphical Models
item list element: Python / PyTorch Stack
has part: Lisp
has part: Prolog
has part: 1990s Graphical Models
has part: Python / PyTorch Stack

Quotations
is part of: ODSC AI East 2026 — Pedro Domingos on Tensor Logic: The Language of AI
type: ItemList
item list element: q definitiveLanguage
has part: q definitiveLanguage

Known Implementations
is part of: ODSC AI East 2026 — Pedro Domingos on Tensor Logic: The Language of AI
type: ItemList
description: As of this writing, no reference implementation from Pedro Domingos himself is known to be published. The implementations below are independent, community-built reimplementations based on his arXiv paper — not official releases.
item list element: tensor-logic (Julia)
item list element: tensor-logic-cpp
item list element: tensorlogic (Python)
has part: tensor-logic (Julia)
has part: tensor-logic-cpp
has part: tensorlogic (Python)

kg-generator
type: SoftwareApplication
type: SoftwareAgent
description: OpenLink Software agent skill that generates RDF-Turtle knowledge graphs from source articles using curated prompt templates.

Pedro Domingos
type: Person
job title: Professor Emeritus
affiliation: University of Washington
description: Professor Emeritus at the University of Washington, author of 'The Master Algorithm', and originator of Tensor Logic, presented as a keynote at ODSC AI East 2026.
knows about: Tensor Logic

University of Washington
type: CollegeOrUniversity

ODSC - Open Data Science
type: Organization
description: Open Data Science Conference organization; publisher of the Medium recap article and organizer of the ODSC AI East 2026 conference where Pedro Domingos delivered the Tensor Logic keynote.

ODSC AI East 2026
type: Event
organizer: ODSC - Open Data Science
description: Open Data Science conference at which Pedro Domingos delivered a keynote introducing Tensor Logic as a unifying language for AI.
sub event: Tensor Logic: The Language of AI (Keynote)

Tensor Logic: The Language of AI (Keynote)
type: Event
performer: Pedro Domingos
super event: ODSC AI East 2026
about: Tensor Logic

Tensor Logic
type: DefinedTerm
description: A framework proposed by Pedro Domingos that merges tensor algebra and logic programming by treating relations as sparse Boolean tensors, so that logic rules and tensor equations become mathematically identical, unifying symbolic reasoning with gradient-based deep learning.
is part of: Glossary

q definitiveLanguage
type: Quotation
position: 1
text: Fields only truly take off when they find their definitive language.
citation: Tensor Logic: The Language of AI (Keynote)
creator: Pedro Domingos
is part of: Quotations

Relations as Sparse Boolean Tensors
type: Tensor Logic Principle
position: 1
description: A logical relation, such as parent(Bob, Chris), is a compact representation of a sparse Boolean tensor: a matrix or higher-dimensional array where a 1 marks a true fact (e.g. a connection in a social network) and a 0 marks its absence. Representing only the non-zero entries lets a database of logical facts be treated directly as tensor data, closing the gap between symbolic facts and numerical arrays.
enables capability: Lets logic-program facts be stored and manipulated using the same sparse-tensor data structures deep learning already relies on.
is part of: Tensor Logic Principles

Einstein Summation (EinSum)
type: Tensor Logic Principle
position: 2
description: A notation that simplifies tensor algebra by implicitly summing over any index repeated within a term, removing the need for explicit summation signs. EinSum is already implemented in NumPy and PyTorch but is rarely used to its full potential. In Tensor Logic, a logic rule (e.g. defining an 'aunt' relation) is rewritten as a single EinSum-based tensor equation over Boolean tensors, using a step function as the nonlinearity.
enables capability: Turns a symbolic logic rule into a single tensor equation, mathematically identical to the corresponding EinSum computation rather than a separate process run alongside it.
is part of: Tensor Logic Principles

Tensor Projection
type: Tensor Logic Principle
position: 3
description: A generalization of the database 'projection' operation: summing a tensor's elements across specific indices to collapse one or more dimensions, such as projecting a matrix onto a vector.
enables capability: Generalizes a familiar relational-database operation into the tensor domain so database-style queries can run over the same structures used for learning.
is part of: Tensor Logic Principles

Tensor Join
type: Tensor Logic Principle
position: 4
description: A generalization of the database 'join' operation: when two tensors share common indices, their corresponding values are multiplied together. When the tensors are Boolean, this operation reduces exactly to a standard database join.
enables capability: Gives Tensor Logic a join operator that is provably identical to relational-database join in the Boolean case, while extending smoothly to weighted, continuous-valued tensors.
is part of: Tensor Logic Principles

Backpropagation Through Structure
type: Tensor Logic Principle
position: 5
description: Because a Tensor Logic program is built from a single construct — the tensor equation — its gradient is simply another Tensor Logic program, making differentiation straightforward. 'Backpropagation through structure' generalizes backpropagation through time (used in Recurrent Neural Networks) so gradients can be computed even when the network of rules and facts differs for every example, letting symbolic structures themselves be learned via gradient descent on standard deep-learning hardware.
enables capability: Enables gradient-based learning of symbolic structures whose shape varies per example, reusing the same hardware acceleration that made deep learning scalable.
is part of: Tensor Logic Principles

Architecture Compression
type: Tensor Logic Principle
position: 6
description: Because Tensor Logic is so concise, complex modern neural network architectures can be reduced to just one or two equations each — turning them into logic-based tensor equations that are easier to debug, experiment with, and generate automatically.
enables capability: Makes an architecture's full computation legible as a small number of equations instead of opaque layer-by-layer code, improving debuggability and interpretability.
is part of: Tensor Logic Principles

Perceptron
type: Neural Architecture Expressed in Tensor Logic
position: 1
expressed as: A one-layer perceptron is defined in a single line combining a step function and a dot product.
is part of: Architectures Expressed in Tensor Logic

Recurrent Neural Network (RNN)
type: Neural Architecture Expressed in Tensor Logic
position: 2
expressed as: Expressed by accounting for time indices and previous hidden states within the tensor join.
is part of: Architectures Expressed in Tensor Logic

Convolutional Neural Network (CNN)
type: Neural Architecture Expressed in Tensor Logic
position: 3
expressed as: Convolution and pooling layers are represented using offset indices and summation.
is part of: Architectures Expressed in Tensor Logic

Transformer
type: Neural Architecture Expressed in Tensor Logic
position: 4
expressed as: Multi-head attention and residual streams are captured in a handful of tensor equations.
is part of: Architectures Expressed in Tensor Logic

Lisp
type: Prior AI Language or Tool
position: 1
strength: Rigorous symbolic reasoning.
limitation: Limited ability to learn from raw data or scale with modern gradient-based methods.
is part of: Prior AI Languages and Tools

Prolog
type: Prior AI Language or Tool
position: 2
strength: Rigorous logic-programming reasoning over facts and rules.
limitation: Same fragmentation from tensor-based scalability as other 1950s-70s symbolic languages.
is part of: Prior AI Languages and Tools

1990s Graphical Models
type: Prior AI Language or Tool
position: 3
strength: Structured probabilistic reasoning over relationships between variables.
limitation: Still struggled to balance rigorous reasoning with scalability.
is part of: Prior AI Languages and Tools

Python / PyTorch Stack
type: Prior AI Language or Tool
position: 4
strength: Excels at gradient descent and scales effortlessly with modern hardware.
limitation: Lacks native automated logical reasoning; operates as a 'black box' without built-in logical structure.
is part of: Prior AI Languages and Tools
has part: Python
has part: PyTorch
has part: NumPy

Python
type: SoftwareApplication

PyTorch
type: SoftwareApplication
description: Deep learning library that already implements Einstein Summation (EinSum), underused by most practitioners.

NumPy
type: SoftwareApplication
description: Numerical computing library that already implements Einstein Summation (EinSum), underused by most practitioners.

Tensor Logic: The Language of AI
type: ScholarlyArticle
author: Pedro Domingos
date published: 2025-10-14
description: The formal arXiv paper underlying the ODSC AI East 2026 keynote. Proposes tensor logic as a unified programming language for AI whose sole construct, the tensor equation, lets neural networks (transformers, kernel machines), symbolic reasoning, and graphical models all be expressed in the same formalism.

tensor-logic (Julia)
type: SoftwareSourceCode
position: 1
programming language: Julia
code repository: https://github.com/zekebass/tensor-logic
license: https://opensource.org/licenses/MIT
author: Zeke Bass
is based on: Tensor Logic: The Language of AI
description: Community Julia implementation unifying neural and symbolic AI by treating relations as sparse Boolean tensors and Datalog rules as Einstein summations. README states it is built by Zeke Bass and Claude Opus 4.5 (Anthropic) in a pair-programming session, December 2025, inspired by a Machine Learning Street Talk podcast episode with Pedro Domingos.
is official implementation: false
is part of: Known Implementations

Zeke Bass
type: Person

tensor-logic-cpp
type: SoftwareSourceCode
position: 2
programming language: C++
code repository: https://github.com/harshaljanjani/tensor-logic-cpp
license: https://www.apache.org/licenses/LICENSE-2.0
author: harshaljanjani
is based on: Tensor Logic: The Language of AI
description: Community C++ implementation described as production-ready, providing a unified framework for neural and symbolic AI by representing logical relations as sparse Boolean tensors, explicitly based on Domingos's arXiv:2510.12269v3.
is official implementation: false
is part of: Known Implementations

harshaljanjani
type: Person

tensorlogic (Python)
type: SoftwareSourceCode
position: 3
programming language: Python
code repository: https://github.com/Kocoro-lab/tensorlogic
license: https://opensource.org/licenses/MIT
author: Kocoro-lab
is based on: Tensor Logic: The Language of AI
description: Community Python implementation of Tensor Logic combining neural and symbolic reasoning through tensor equations; README requests citation of Domingos's paper and is discussed in a companion blog post, 'Tensor Logic: A Brain-Like Architecture' (waylandz.com, October 21, 2025).
is official implementation: false
is part of: Known Implementations

Kocoro-lab
type: Organization

Logic Tensor Networks (LTN)
type: SoftwareApplication
code repository: https://github.com/logictensornetworks/LTNtorch
description: A PyTorch neuro-symbolic framework with a similarly-worded name. Pre-dates and is UNRELATED to Pedro Domingos's Tensor Logic — a common point of confusion the reader should not conflate with the framework described in this document.
comment: Disambiguation entity only: included to prevent readers from mistaking this pre-existing, differently-authored framework for Domingos's Tensor Logic.

What is Tensor Logic?
type: Question
position: 1
is part of: Frequently Asked Questions
accepted answer: a1

a1
type: Answer
text: Tensor Logic is a framework proposed by Pedro Domingos that unifies symbolic AI and deep learning by treating logical relations as sparse Boolean tensors, so that logic programs and tensor algebra become mathematically identical rather than two separate systems bolted together.

Why does AI need a 'definitive language' at all?
type: Question
position: 2
is part of: Frequently Asked Questions
accepted answer: a2

a2
type: Answer
text: Pedro Domingos argues that fields only truly take off once they find their definitive language — a shared notation precise enough for rigorous reasoning yet flexible enough to scale. AI has lacked that until now, oscillating between symbolic languages (rigorous but not scalable) and deep-learning frameworks (scalable but not reasoning-native).

What problem in AI is Tensor Logic trying to solve?
type: Question
position: 3
is part of: Frequently Asked Questions
accepted answer: a3

a3
type: Answer
text: The historical fragmentation between symbolic AI, which reasons rigorously but struggles to learn from raw data, and deep learning, which scales effortlessly but remains an opaque, logic-free 'black box'.

How does Tensor Logic represent a logical fact like parent(Bob, Chris)?
type: Question
position: 4
is part of: Frequently Asked Questions
accepted answer: a4

a4
type: Answer
text: As a non-zero entry in a sparse Boolean tensor. A relation is treated as a compact representation of that tensor: a '1' marks a true fact (e.g. a connection between two people) and a '0' marks its absence, so a whole database of facts becomes tensor data.

What is Einstein Summation (EinSum) and why does it matter here?
type: Question
position: 5
is part of: Frequently Asked Questions
accepted answer: a5

a5
type: Answer
text: EinSum is a notation that implicitly sums over any index repeated within a term, removing explicit summation signs. It is already built into NumPy and PyTorch but underused. Tensor Logic uses EinSum to rewrite logic rules as tensor equations, making logic programming and tensor manipulation mathematically the same operation.

What is tensor projection?
type: Question
position: 6
is part of: Frequently Asked Questions
accepted answer: a6

a6
type: Answer
text: A generalization of the database projection operation: summing a tensor's elements across specific indices to collapse one or more dimensions, such as reducing a matrix down to a vector.

What is tensor join, and how does it relate to database joins?
type: Question
position: 7
is part of: Frequently Asked Questions
accepted answer: a7

a7
type: Answer
text: Tensor join generalizes the database join: when two tensors share common indices, their corresponding values are multiplied. If both tensors are Boolean, this operation reduces exactly to a standard database join — the numerical operation and the relational-database operation are the same thing in that special case.

What is 'backpropagation through structure'?
type: Question
position: 8
is part of: Frequently Asked Questions
accepted answer: a8

a8
type: Answer
text: A generalization of backpropagation through time (used to train RNNs) that lets gradients be computed even when the network of rules and facts differs for every training example. It allows symbolic structures themselves — not just fixed-shape neural weights — to be learned via gradient descent.

Why is the gradient of a Tensor Logic program 'just another Tensor Logic program'?
type: Question
position: 9
is part of: Frequently Asked Questions
accepted answer: a9

a9
type: Answer
text: Because every Tensor Logic program is built from a single recurring construct — the tensor equation — differentiating it mechanically produces another set of tensor equations, keeping the entire system inside the same formalism rather than requiring a separate symbolic differentiation layer.

Can complex architectures like Transformers really be written in one or two equations?
type: Question
position: 10
is part of: Frequently Asked Questions
accepted answer: a10

a10
type: Answer
text: According to the keynote, yes: because Tensor Logic is so concise, even a Transformer's multi-head attention and residual streams can be captured in a handful of tensor equations, in contrast to the many lines of framework-specific code typically needed.

Which existing tools already implement pieces of Tensor Logic's approach?
type: Question
position: 11
is part of: Frequently Asked Questions
accepted answer: a11

a11
type: Answer
text: NumPy and PyTorch already implement Einstein Summation (EinSum), one of Tensor Logic's core mechanisms — but the keynote argues its full potential is rarely realized by practitioners today.

Why does interpretability matter enough to justify a new language?
type: Question
position: 12
is part of: Frequently Asked Questions
accepted answer: a12

a12
type: Answer
text: As AI increasingly runs critical infrastructure, models must be both high-performing and interpretable. Tensor Logic aims to let neural networks gain the reliability of symbolic AI while symbolic systems gain the learnability of deep learning — enabling reasoning in embedding spaces that is both sound and transparent.

Who is Pedro Domingos?
type: Question
position: 13
is part of: Frequently Asked Questions
accepted answer: a13

a13
type: Answer
text: Pedro Domingos is Professor Emeritus at the University of Washington, author of 'The Master Algorithm', and the originator of Tensor Logic, which he introduced in his ODSC AI East 2026 keynote.

What historical AI languages does Tensor Logic build on or move past?
type: Question
position: 14
is part of: Frequently Asked Questions
accepted answer: a14

a14
type: Answer
text: Lisp and Prolog (1950s-70s symbolic reasoning languages), 1990s graphical models, and the modern Python/PyTorch stack — each offered either rigorous reasoning or scalability, but historically not both at once.

Are there any known systems using Tensor Logic?
type: Question
position: 15
is part of: Frequently Asked Questions
accepted answer: a15

a15
type: Answer
text: No official implementation from Pedro Domingos himself is known to be published, and no widely-adopted production system is known to run on Tensor Logic — the formal arXiv paper (2510.12269) is barely a year old. Three independent community reimplementations exist based on that paper: a Julia implementation (github.com/zekebass/tensor-logic), a C++ implementation (github.com/harshaljanjani/tensor-logic-cpp), and a Python implementation (github.com/Kocoro-lab/tensorlogic). None are official releases. Note also that 'Logic Tensor Networks' (LTNtorch) is a separate, pre-existing, unrelated PyTorch framework despite the similar name.

Tensor
type: DefinedTerm
description: A generalization of vectors and matrices to arbitrary dimensions, defined by its type and shape; the fundamental data structure of deep learning.
is part of: Glossary

Sparse Boolean Tensor
type: DefinedTerm
description: A tensor whose entries are mostly zero, storing only the non-zero (true) entries — the representation Tensor Logic uses to encode logical relations and facts efficiently.
is part of: Glossary

Einstein Summation (EinSum)
type: DefinedTerm
description: A tensor-algebra notation in which any index repeated within a term is implicitly summed over, eliminating explicit summation signs; implemented in NumPy and PyTorch.
is part of: Glossary

Tensor Projection
type: DefinedTerm
description: Summing a tensor's elements across specific indices to collapse dimensions — the tensor-domain generalization of the database projection operation.
is part of: Glossary

Tensor Join
type: DefinedTerm
description: Multiplying corresponding values where two tensors share indices — the tensor-domain generalization of the database join operation; reduces to a standard join when both tensors are Boolean.
is part of: Glossary

Backpropagation Through Structure
type: DefinedTerm
description: A generalization of backpropagation through time that computes gradients even when the network of rules and facts differs per example, enabling gradient-based learning of symbolic structure itself.
is part of: Glossary

Neuro-Symbolic AI
type: DefinedTerm
description: The broader research field combining neural networks with symbolic reasoning; Tensor Logic is presented as fulfilling the long-elusive goal of merging logical transparency with tensor scalability within this field.
is part of: Glossary

Symbolic AI
type: DefinedTerm
description: AI built on explicit logical rules and facts, capable of rigorous reasoning but historically limited in learning from raw data.
is part of: Glossary

Deep Learning
type: DefinedTerm
description: Gradient-based neural network learning that scales effortlessly with data and compute, but is often criticized as an opaque 'black box' lacking logical structure.
is part of: Glossary

Database Join
type: DefinedTerm
description: The relational-database operation of combining rows from two tables based on shared keys; tensor join reduces exactly to this operation when applied to Boolean tensors.
is part of: Glossary

Step Function
type: DefinedTerm
description: A nonlinearity that outputs a discrete value change at a threshold; used in Tensor Logic as the nonlinearity applied to EinSum-based tensor equations when encoding logic rules.
is part of: Glossary