State Machines on steroids

If you define a rule group as a state, you lift classical static logic to a dynamic level:

Decision tables as a complete description of state machines – with guaranteed completeness, consistency, and freedom from redundancy.

LF‑ET offers something that can be described as

state-based decision tables

A synthesis of:

  • decision tables

  • state machines

  • rules engines

  • modular functional decomposition

This combines the formal strength of state machine theory with the practical strength of decision tables.

Rule group → State

In LF‑ET each rule group can be viewed functionally as a state of a state machine:

  • A rule group contains all rules that are valid in this state

  • Each rule describes a state transition

  • The conditions of the rule define when the state transition takes place

  • The actions of the rule define what happens

  • At the end of the rule, the next state is defined.

Thus, a rule group is exactly what a state is in the state machine theory.

Rule → State transition

A rule is a complete and deterministic transition:

If all conditions are fulfilled → execute actions → go to state X

This is formally equivalent to:

δ(state, entry) → new state

with the exception that LF‑ET additionally permits actions (outputs) → Mealy automat.

The combination forms are consequently "embedded"

This model enables the representation of classic combination forms without the need for explicit constructs:

Sequence

State A → State B → State C
By simply selecting "Next rule group = …"

Branch

State A → depending on the rule → State B or C
This is a classic deterministic transition.

Loop

State A → State A
By simply selecting "Next rule group = current rule group"

Why is it so powerful?

This yields state machines whose transition logic is guaranteed to be complete, consistent, and free of redundancy:

  • Each rule group is complete (all cases are covered)

  • Each rule is deterministic

  • No rule overlaps with another

  • Each transition is explicit

  • Professional users have access to view it

  • Developers can generate code

  • Testers receive test cases automatically

Why is this ideal for large rule systems?

Large rule systems are essentially state machines:

  • Insurance claims

  • Loan approval decisions

  • Medical coding systems

  • Automotive functions

  • Workflow management

  • Dialog systems

  • Control logic

  • Configuration logic

They consist of:

  • States

  • Transitions

  • Conditions

  • Actions

LF-ET naturally captures this — without having to resort to UML state charts, BPMN, or pseudocode, for example.

And with the mathematical rigor of a decision table.