If you have ever been in charge of managing complex business logic, you know how nested if-else statements can be a jungle: painful to navigate and easy to get lost. When it comes to mission-critical tasks, for example formal verification or satisfiability, many developers reach for sophisticated tools such as automated theorem provers or SMT solvers. Although powerful, these approaches can be overkill and a headache to implement. What if all you need is a simple, transparent rules engine?
The key idea for building such a lightweight engine relies on a concept that we were taught to be insightful but impractical: truth tables. Exponential growth, their fatal flaw, makes them unfit for real-world problems. So we were told.
A simple observation changes everything: In almost a…
If you have ever been in charge of managing complex business logic, you know how nested if-else statements can be a jungle: painful to navigate and easy to get lost. When it comes to mission-critical tasks, for example formal verification or satisfiability, many developers reach for sophisticated tools such as automated theorem provers or SMT solvers. Although powerful, these approaches can be overkill and a headache to implement. What if all you need is a simple, transparent rules engine?
The key idea for building such a lightweight engine relies on a concept that we were taught to be insightful but impractical: truth tables. Exponential growth, their fatal flaw, makes them unfit for real-world problems. So we were told.
A simple observation changes everything: In almost all practical cases, the “impossibly large” truth table is actually not dense with information; it is in fact a sparse matrix in disguise.
This reframing makes the truth tables both conceptually clear and computationally tractable.
This article shows you how to turn this insight into a lightweight and powerful rules engine. We’ll guide you through all the necessary steps to build the engine from scratch. Alternatively, you can use our open-source library vector-logic to start building applications on day one. This tutorial will give you all the necessary details to understand what’s under the hood.
While all the theoretical background and mathematical details can be found in our research paper on the State Algebra [1], here, we focus on the hands-on application. Let’s roll up our sleeves and start building!
A Quick Refresher on Logic 101
Truth Tables
We’ll start with a quick refresher: logical formulas are expressions that are built from Boolean variables and logical connectors like AND, OR, and NOT. In a real-world context, Boolean variables can be thought of as representing events (e.g. “the coffee cup is full”, which is true if the cup is actually full and false if it is empty). For example, the formula (f = (x_1 \vee x_2)) is true if (x_1) is true, (x_2) is true, or both are. We can use this framework to build a comprehensive brute-force map of every possible reality — the truth table.
Using 1 for “true” and 0 for “false”, the table for (x_1 \vee x_2) looks like this:
[ \begin{Bmatrix} x_1 & x_2 & x_1 \vee x_2 \ \hline 0 & 0 & 0 \ 0 & 1 & 1 \ 1 & 0 & 1 \ 1 & 1 & 1 \end{Bmatrix} ]
Everything we need to perform logical inference is encoded in the truth table. Let’s see it in action.
Logical Inference
Consider a classic example of the transitivity of implication. Suppose we know that… By the way, everything we say “we know” is called a premise. Suppose we have two premises:
- If (x_1) is true, then (x_2) must be true ((x_1 \to x_2))
- If (x_2) is true, then (x_3) must be true ((x_2 \to x_3))
It’s easy to guess the conclusion: “If (x_1) is true, then (x_3) must be true” ((x_1 \to x_3)). However, we can give a formal proof using truth tables. Let’s first label our formulas:
[\begin{align*} & f_1 = (x_1 \to x_2) && \text{premise 1}\ & f_2 = (x_2 \to x_3) && \text{premise 2}\ & f_3 = (x_1 \to x_3) && \text{conclusion} \end{align*}]
The first step is to build a truth table covering all combinations of the three base variables (x_1), (x_2), and (x_3):
[\begin{align*} \begin{Bmatrix} x_1 & x_2 & x_3 & f_1 & f_2 & f_3 \ \hline 0 & 0 & 0 & 1 & 1 & 1 \ 0 & 0 & 1 & 1 & 1 & 1 \ 0 & 1 & 0 & 1 & 0 & 1 \ 0 & 1 & 1 & 1 & 1 & 1 \ 1 & 0 & 0 & 0 & 1 & 0 \ 1 & 0 & 1 & 0 & 1 & 1 \ 1 & 1 & 0 & 1 & 0 & 0 \ 1 & 1 & 1 & 1 & 1 & 1 \end{Bmatrix} \end{align*}]
This table contains eight rows, one for each assignment of truth values to the base variables. The variables (f_1), (f_2) and (f_3) are derived, as we compute their values directly from the (x)-variables.
Notice how large the table is, even for this simple case!
The next step is to let our premises, represented by (f_1) and (f_2), act as a filter on reality. We are given that they are both true. Therefore, any row where either (f_1) or (f_2) is false represents an impossible scenario which should be discarded.
After applying this filter, we are left with a much smaller table:
[\begin{align*} \begin{Bmatrix} x_1 & x_2 & x_3 & f_1 & f_2 & f_3 \ \hline 0 & 0 & 0 & 1 & 1 & 1 \ 0 & 0 & 1 & 1 & 1 & 1 \ 0 & 1 & 1 & 1 & 1 & 1 \ 1 & 1 & 1 & 1 & 1 & 1 \end{Bmatrix} \end{align*}]
And here we are: In every remaining valid scenario, (f_3) is true. We have proven that (f_3) logically follows from (or is entailed by) (f_1) and (f_2).
An elegant and intuitive method indeed. So, why don’t we use it for complex systems? The answer lies in simple maths: With only three variables, we had (2^3=8) rows. With 20 variables, we would have over a million. Take 200, and the number of rows would exceed the number of atoms in the solar system. But wait, our article does not end here. We can fix that.
The Sparse Representation
The key idea for addressing exponentially growing truth tables lies in a compact representation enabling lossless compression.
Beyond just compressing the truth tables, we will need an efficient way to perform logical inference. We will achieve this by introducing “state vectors” — which represent sets of states (truth table rows) — and adopting set theory operations like union and intersection to manipulate them.
The Compressed Truth Table
First, we return to formula (f = (x_1 \to x_2)). Let’s identify the rows that make the formula true. We use the symbol (\sim) to represent the correspondence between the formula and a table of its “valid” truth assignments. In our example of (f) for implication, we write:
[\begin{align*} f\quad\sim\quad \begin{Bmatrix} x_1 & x_2 \ \hline 0 & 0 \ 0 & 1 \ 1 & 1 \end{Bmatrix} \end{align*}]
Note that we dropped the row ((1, 0)) since it invalidates (f). What would happen to this table, if we now extended it to involve a third variable (x_3), that (f) doesn’t depend on? The classic approach would double the size of the truth table to account for (x_3) being 0 or 1, although it does not add any new information about (f):
[\begin{align*} f\quad\sim\quad \begin{Bmatrix} x_1 & x_2 & x_3 \ \hline 0 & 0 & 0 \ 0 & 0 & 1 \ 0 & 1 & 0 \ 0 & 1 & 1 \ 1 & 1 & 0 \ 1 & 1 & 1 \end{Bmatrix} \end{align*}]
What a waste! Uninformative columns could be compressed, and, for this purpose, we introduce a dash (–) as a “wildcard” symbol. You can think of it as a logical Schrödinger’s cat: the variable exists in a superposition of both 0 and 1 until a constraint or a measurement (in the context of learning, we call it “evidence”) forces it into a definite state, removing one of the possibilities.
Now, we can represent (f) across a universe of (n) variables without any bloat:
[\begin{align*} f\quad\sim\quad \begin{Bmatrix} x_1 & x_2 & x_3 & \ldots & x_n \ \hline 0 & 0 & – & \ldots & -\ 0 & 1 & – &\ldots & – \ 1 & 1 & – &\ldots & – \end{Bmatrix} \end{align*}]
We can generalise this by postulating that any row containing dashes is equivalent to the set of multiple rows obtained by all possible substitutions of 0s and 1s in the places of dashes. For example (try it with pencil and paper!):
[\begin{align*} \begin{Bmatrix} x_1 & x_2 & x_3 \ \hline – & 0 & – \ – & 1 & 1 \end{Bmatrix} = \begin{Bmatrix} x_1 & x_2 & x_3 \ \hline 0 & 0 & 0 \ 0 & 0 & 1 \ 1 & 0 & 0 \ 1 & 0 & 1 \ 0 & 1 & 1 \ 1 & 1 & 1 \end{Bmatrix} \end{align*}]
This is the essence of sparse representation. Let’s introduce a few definitions for basic operations: We call replacing dashes with 0s and 1s expansion. The reverse process, in which we spot patterns to introduce dashes, is called reduction. The simplest form of reduction, replacing two rows with one, is called atomic reduction.
An Algebra of States
Now, let’s give these ideas some structure.
- A state is a single, complete assignment of truth values to all variables — one row in a fully expanded truth table (e.g. ((0, 1, 1))).
- A state vector is a set of states (think of it as a subset of the truth table). A logical formula can now be considered as a state vector containing all the states that make it true. Special cases are an empty state vector (0) and a vector containing all (2^n) possible states, which we call a trivial vector and denote as (\mathbf{t}). (As we’ll see, this corresponds to a t-object with all wildcards.)
- A row in a state vector’s compact representation (e.g. ((0, -, 1) )) is called a t-object. It’s our fundamental building block — a pattern that can represent one or many states.
Conceptually, shifting the focus from tables to sets is a crucial step. Remember how we carried out inference using the truth table method: we used premises (f_1) and (f_2) as a filter, keeping only the rows where both premises were true. This operation, in terms of the language of set theory, is an intersection.
Each premise corresponds to a state vector (the set of states that satisfy the premise). The state vector for our combined knowledge is the intersection of these premise vectors. This operation is at the core of the new model.
For friendlier notation, we introduce some “syntax sugar” by mapping set operations to simple arithmetic operations:
- Set Union ((\cup)) (\rightarrow) Addition ((+))
- Set Intersection ((\cap)) (\rightarrow) Multiplication ((*))
The properties of these operations (associativity, commutativity, and distributivity) allow us to use high-school algebra notation for complex expressions with set operations:
[ \begin{align*} & (A\cup B) \cap (C\cup D) = (A\cap C) \cup (A\cap D) \cup (B\cap C) \cup (B\cap D) \ & \rightarrow \ & (A+B)\cdot(C+D) = A,C + A,D + B,C + B,D \end{align*} ]
Let’s take a break and see where we are. We’ve laid a strong foundation for the new framework. Truth tables are now represented sparsely, and we reinterpret them as sets (state vectors). We also established that logical inference can be accomplished by multiplying the state vectors.
We are nearly there. But before we can apply this theory to develop an efficient inference algorithm, we need one more ingredient. Let’s take a closer look at operations on t-objects.
The Engine Room: Operations on T-Objects
We are now ready to go to the next phase — creating an algebraic engine to manipulate state vectors efficiently. The fundamental building block of our construction is the t-object — our compact, wildcard-powered representation of a single row in a state vector.
Note that to describe a row, we only need to know the positions of 0s and 1s. We denote a t-object as (\mathbf{t}^\alpha_\beta), where (\alpha) is the set of indices where the variable is 1, and (\beta) is the set of indices where it is 0. For instance:
[ \begin{Bmatrix} x_1 & x_2 & x_3 & x_4 \ \hline 1 & 0 & – & 1 \end{Bmatrix} = \mathbf{t}_2^{14} ]
A t-object consisting of all the dashes (\mathbf{t} = { -;; – \ldots \ -}) represents the previously mentioned trivial state vector that contains all possible states.
From Formulas to T-Objects
A state vector is the union of its rows or, in our new notation, the sum of its t-objects. We call this row decomposition. For example, the formula (f=(x_1\to x_2)) can be represented as:
[\begin{align*} f\quad\sim\quad \begin{Bmatrix} x_1 & x_2 & \ldots & x_n \ \hline 0 & 0 & \ldots & -\ 0 & 1 & \ldots & – \ 1 & 1 & \ldots & – \end{Bmatrix} = \mathbf{t}_{12} + \mathbf{t}_1^2 + \mathbf{t}^{12} \end{align*}]
Notice that this decomposition doesn’t change if we add more variables ((x_3, x_4, \dots)) to the system, which shows that our approach is inherently scalable.
The Rule of Contradiction
The same index cannot appear in both the upper and lower positions of a t-object. If this occurs, the t-object is null (an empty set). For instance (we highlighted the conflicting index):
[ \mathbf{t}^{1{\color{red}3}}_{2{\color{red}3}} = 0 ]
This is the algebraic equivalent of a logical contradiction. A variable ((x_3) in this case) cannot be both true (superscript) and false (subscript) at the same time. Any such t-object represents an impossible state and vanishes.
Simplifying Expressions: Atomic Reduction
Atomic reduction can be expressed cleanly using the newly introduced t-object notation. Two rows can be reduced if they are identical, except for one variable, which is 0 in one and 1 in the other. For instance:
[ \begin{align*} \begin{Bmatrix} x_1 & x_2 & x_3 & x_4 & x_5 \ \hline 1 & – & 0 & 0 & – \ 1 & – & 0 & 1 & – \end{Bmatrix} = \begin{Bmatrix} x_1 & x_2 & x_3 & x_4 & x_5 \ \hline 1 & – & 0 & – & – \end{Bmatrix} \end{align*} ]
In algebraic terms, this is:
[ \mathbf{t}1_{34} + \mathbf{t}{14}_3 = \mathbf{t}^1_3 ]
The rule for this operation follows directly from the definition of the t-objects: If two t-objects have index sets that are identical, except for one index that is a superscript in one and a subscript in the other, they combine. The clashing index (4 in this example) is annihilated, and the two t-objects merge.
By applying atomic reduction, we can simplify the decomposition of the formula (f = (x_1 \to x_2)). Noticing that (\mathbf{t}_{12} + \mathbf{t}_1^2 = \mathbf{t}_1), we get:
[ f \quad \sim\quad \mathbf{t}_{12} + \mathbf{t}_1^2 + \mathbf{t}{12} = \mathbf{t}_1 + \mathbf{t}{12} ]
The Core Operation: Multiplication
Finally, let us discuss the most important operation for our rules engine: intersection (in terms of set theory), represented as multiplication (in terms of algebra). How do we find the states common to the two t-objects?
The rule governing this operation is straightforward: to multiply two t-objects, one forms the union of their superscripts, as well as the union of their subscripts (we leave the proof as a simple exercise for a curious reader):
[ \mathbf{t}{\alpha_1}_{\beta_1},\mathbf{t}{\alpha_2}_{\beta_2} = \mathbf{t}^{\alpha_1 \cup \alpha_2}_{\beta_1\cup\beta_2} ]
The rule of contradiction still applies. If the resulting superscript and subscript sets overlap, the product vanishes:
[ \mathbf{t}^{\alpha_1 \cup \alpha_2}_{\beta_1\cup\beta_2} = 0 \quad \iff \quad (\alpha_1 \cup \alpha_2) \cap (\beta_1\cup\beta_2) \not = \emptyset ]
For example:
[ \begin{align*} & \mathbf{t}{12}_{34},\mathbf{t}5_6 = \mathbf{t}{125}_{346} && \text{Simple combination} \ & \mathbf{t}{12}_{34} ,\mathbf{t}{4} = \mathbf{t}{12{\color{red}4}}_{3{\color{red}4}} = 0 && \text{Vanishes, because 4 is in both sets} \end{align*} ]
A vanishing product means that the two t-objects have no states in common; therefore, their intersection is empty.
These rules complete our construction. We defined a sparse representation of logic and algebra for manipulating the objects. These are all the theoretical tools that we need. We are ready to assemble them into a practical algorithm.
Putting It All Together: Inference With State Algebra
The engine is ready, it’s time to turn it on! In its core, the idea is simple: to find the set of valid states, we need to multiply all state vectors corresponding to premises and evidences.
If we have two premises, represented by the state vectors ((\mathbf{t}_{(1)} + \mathbf{t}_{(2)})) and ((\mathbf{t}_{(3)} + \mathbf{t}_{(4)})), the set of states in which both are true is their product:
[ \left(\mathbf{t}_{(1)} + \mathbf{t}_{(2)}\right),\left(\mathbf{t}_{(3)} + \mathbf{t}_{(4)}\right) = \mathbf{t}_{(1)},\mathbf{t}_{(3)} + \mathbf{t}_{(1)},\mathbf{t}_{(4)} + \mathbf{t}_{(2)},\mathbf{t}_{(3)} + \mathbf{t}_{(2)},\mathbf{t}_{(4)} ]
This example can be easily generalised to any arbitrary number of premises and t-objects.
The inference algorithm is straightforward:
- Decompose: Convert each premise into its state vector representation (a sum of t-objects).
- Simplify: Use atomic reduction on each state vector to make it as compact as possible.
- Multiply: Multiply the state vectors of all premises together. The result is a single state vector representing all states consistent with your premises.
- Reduce Again: The final product may have reducible terms, so simplify it one last time.
Example: Proving Transitivity, The Algebraic Way
Let’s revisit our classic example of implication transitivity: if (f_1 = (x_1\to x_2)) and (f_2 = (x_2\to x_3)) are true, prove that (f_3=(x_1\to x_3)) must also be true. First, we write the simplified state vectors for our premises as follows:
[ \begin{align*} & f_1 \quad \sim \quad \mathbf{t}_1 + \mathbf{t}{12} \ & f_2 \quad \sim \quad \mathbf{t}_2 + \mathbf{t}{23} \end{align*} ]
To prove the conclusion, we can use a proof by contradiction. Let’s ask: does a scenario exist where our premises are true, but our conclusion (f_3) is false?
The states that invalidate (f_3 = (x_1 \to x_3)) are those in which (x_1) is true (1) and (x_3) is false (0). This corresponds to a single t-object: (\mathbf{t}^1_3).
Now, let’s see if this “invalidating” state vector can coexist with our premises by multiplying everything together:
[ \begin{gather*} (\text{Premise 1}) \times (\text{Premise 2}) \times (\text{Invalidating State Vector})\ (\mathbf{t}_1 + \mathbf{t}{12}),(\mathbf{t}_2 + \mathbf{t}{23}), \mathbf{t}^1_3 \end{gather*} ]
First, we multiply by the invalidating t-object, as it’s the most restrictive term:
[ (\mathbf{t}_1 \mathbf{t}1_3 + \mathbf{t}{12} \mathbf{t}1_3),(\mathbf{t}_2 + \mathbf{t}{23}) = (\mathbf{t}{{\color{red}1}}_{{\color{red}1}3} + \mathbf{t}{12}_3),(\mathbf{t}_2 + \mathbf{t}^{23}) ]
The first term, (\mathbf{t}^{{\color{red}1}}_{{\color{red}1}3}), vanishes due to contradiction. So we are left with:
[ \mathbf{t}{12}_3,(\mathbf{t}_2 + \mathbf{t}{23}) = \mathbf{t}{12}_3 \mathbf{t}_2 + \mathbf{t}{12}_3 \mathbf{t}{23} = \mathbf{t}{1{\color{red}2}}_{{\color{red}2}3} + \mathbf{t}^{12{\color{red}3}}_{{\color{red}3}} = 0 + 0 = 0 ]
The intersection is empty. This proves that there is no possible state where (f_1) and (f_2) are true, but (f_3) is false. Therefore, (f_3) must follow from the premises.
Proof by contradiction is not the only way to solve this problem. You will find a more elaborate analysis in the “State Algebra” paper [1].
From Logic Puzzles to Fraud Detection
This isn’t just about logic puzzles. So much of our world is governed by rules and logic! For example, consider a rule-based fraud-detection system.
Your knowledge base is a set of rules like
IF card_location is overseas AND transaction_amount > $1000, THEN risk is high
The entire knowledge base can be compiled into a single large state vector.
Now, a transaction occurs. This is your evidence:
card_location = overseas, transaction_amount > $1000, user_logged_in = false
This evidence is a single t-object, assigning 1s to observed facts that are true and 0s to facts that are false, leaving all unobserved facts as wildcards.
To make a decision, you simply multiply:
[ \text{Knowledge Base Vector}\times \text{Evidence T-object} ]
The resulting state vector instantly tells you the value of the target variable (such as risk) given the evidence. No messy chain of “if-then-else” statements was needed.
Scaling Up: Optimisation Strategies
As with mechanical engines, there are many ways to make our engine more efficient.
Let’s face the reality: logical inference problems are computationally hard, meaning that the worst-case runtime is non-polynomial. Put simply, no matter how compact the representation is, or how smart the algorithm is, in the worst-case scenario, the runtime will be extremely long. So long that most likely, you will have to stop the computation before the result is calculated.
The reason SAT solvers are doing a great job is not because they change reality. It is because the majority of real-life problems are not worst-case scenarios. The runtime on an “average” problem will be extremely sensitive to the heuristic optimisations that your algorithm uses for computation.
Thus, optimisation heuristics could be one of the most important components of the engine to achieve meaningful scalability. Here, we just hint at possible places where optimisation can be considered.
Note that when multiplying many state vectors, the number of intermediate t-objects can grow significantly before eventually shrinking, but we can do the following to keep the engine running smoothly:
- Constant Reduction: After each multiplication, run the reduction algorithm on the resulting state vector. This keeps intermediate results compact.
- Heuristic Ordering: The order of multiplication matters. It’s often better to multiply smaller or more restrictive state vectors first, as this can cause more t-objects to vanish early, pruning the calculation.
Conclusion
We have taken you on a journey to discover how propositional logic can be cast into the formalism of state vectors, such that we can use basic algebra to perform logical inference. The elegance of this approach lies in its simplicity and efficiency.
At no point does inference require the calculation of giant truth tables. The knowledge base is represented as a set of sparse matrices (state vector), and the logical inference is reduced to a set of algebraic manipulations that can be implemented in a few straightforward steps.
While this algorithm doesn’t aim to compete with cutting-edge SAT solvers and formal verification algorithms, it offers a beautiful, intuitive way of representing logic in a highly compact form. It’s a powerful tool for building lightweight rules engines, and a great mental model for thinking about logical inference.
Try It Yourself
The best way to understand this system is to use it. We’ve packaged the entire algorithm into an open-source Python library called vector-logic. It can be installed directly from PyPI:
pip install vector-logic
The full source code, along with more examples and documentation, is available on
We encourage you to explore the repository, try it on your own logic problems, and contribute.
If you’re interested in delving deeper into mathematical theory, check out the original paper [1]. The paper covers some topics which we could not include in this practical guide, such as canonical reduction, orthogonalisation and many others. It also establishes an abstract algebraic representation of propositional logic based on t-objects formalism.
We welcome any comments or questions.
Who We Are
References
[1] Dmitry Lesnik and Tobias Schäfer, “State Algebra for Propositional Logic,” arXiv preprint arXiv:2509.10326, 2025. Available at: https://arxiv.org/abs/2509.10326