Propositional Logic Inference Engine

A Propositional Logic Inference Engine is a computational model designed to simulate rational reasoning by deriving logical conclusions from a set of given propositions. By manipulating formal logic, these engines evaluate the truth values of propositions based on a predefined knowledge base (KB). They are essential tools in fields such as artificial intelligence, automated reasoning, expert systems, and logical programming.

Inference Methods

This engine utilizes 4 core inference methods:

1. Truth Table: A method that exhaustively lists all possible truth values for a set of propositions and determines their validity.
2. Forward Chaining: A data-driven approach that begins with known facts and applies inference rules to deduce new facts until a conclusion is reached.
3. Backward Chaining: A goal-driven approach that starts with a desired conclusion and works backward to find the supporting facts in the knowledge base.
4. DPLL (Davis-Putnam-Logeman-Loveland) Algorithm: An efficient algorithm used for determining the satisfiability of propositional logic formulas, often used in solving SAT problems.

Minimum Requirements

• Python 3.7.16

Install the required dependencies from the requirements.txt file:

$ pip install -r requirements.txt

Program Instructions

Running the Program with a Single Test Case and Inference Method

To execute the program, use the following command format:

$ python main.py <filename> <method>

• <filename> is the path to the test case txt files (e.g., test-cases/test-1.txt).
• <method> is the inference method to execute (e.g., TT, FC, BC. DPLL).

Batch Testing

To execute a batch test of all test cases with all inference methods:

$ python batch_test.py