scp_final_lpstructure.md

Expert System with SCP Algorithm This document provides an overview of the expert system implemented with backward inference and the SCP algorithm for rule prioritization.

Overview The expert system is designed to perform backward inference to determine if a given goal can be proven based on a set of facts and rules. The SCP algorithm is used to prioritize rules during the inference process.

Classes Rule Purpose: Represents a logical rule with premises and a conclusion. Attributes: premises: A list of conditions that must be met for the rule to be applied. conclusion: The outcome if the premises are satisfied. ExpertSystem Purpose: Manages the knowledge base of rules and facts, and performs backward inference. Methods: add_rule(premises, conclusion): Adds a rule to the knowledge base. add_fact(fact): Adds a fact to the set of known facts. backward_inference(goal, scp_algorithm, used_rules=None): Determines if a goal can be proven using the knowledge base and SCP algorithm for rule prioritization. Tracks the rules used in the process. LPStructure Purpose: Provides logical operations for manipulating binary representations. Methods: EQ(ls, rs): Checks if two lists are equal. EZ(ls): Checks if a list is zero. LE(ls, rs): Checks if one list is less than or equal to another. LT(ls, rs): Checks if one list is less than another. lJoin(ls, rs): Performs a logical OR operation between two lists. lMeet(ls, rs): Performs a logical AND operation between two lists. lDiff(ls, rs): Computes the difference between two lists. isMeet(ls, rs): Checks if there is any overlap between two lists. isON(eTest, nAtom): Checks if a specific bit is set in a list. to_binary_string(individual): Converts a list to a binary string representation. SCPAlgorithm Purpose: Implements a simple genetic algorithm for optimizing rule prioritization. Methods: generate_individual(): Generates a random individual. generate_population(): Generates a population of individuals. fitness(individual): Computes the fitness of an individual. select(population): Selects the top individuals based on fitness. crossover(parent1, parent2): Performs crossover between two parents to produce offspring. mutate(individual): Mutates an individual with a small probability. evolve(): Evolves the population over a number of generations to find the best individual. prioritize_rules(rules, goal): Prioritizes rules based on a fitness function. Example Usage

Initialize LPStructure and SCPAlgorithm

lp_structure = LPStructure(10, 8) scp_algorithm = SCPAlgorithm(lp_structure)

Initialize Expert System

expert_system = ExpertSystem() expert_system.add_fact("A") expert_system.add_fact("D")

Add rules to the expert system

expert_system.add_rule(["A"], "B") expert_system.add_rule(["B"], "C") expert_system.add_rule(["D"], "E") expert_system.add_rule(["E", "B"], "F")

Define a goal and check if it can be proven

goal = "F" used_rules = [] result = expert_system.backward_inference(goal, scp_algorithm, used_rules) print(f"Goal '{goal}' is {'proven' if result else 'not proven'}")

Print the rules used to achieve the goal

if result: print("Rules used to achieve the goal:") for rule in used_rules: print(f"Premises: {rule.premises} -> Conclusion: {rule.conclusion}")

Check another goal

goal = "C" used_rules = [] result = expert_system.backward_inference(goal, scp_algorithm, used_rules) print(f"Goal '{goal}' is {'proven' if result else 'not proven'}")

Print the rules used to achieve the goal

if result: print("Rules used to achieve the goal:") for rule in used_rules: print(f"Premises: {rule.premises} -> Conclusion: {rule.conclusion}")

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Conclusion This expert system demonstrates how backward inference can be combined with a genetic algorithm to optimize rule prioritization. The system is flexible and can be extended with additional rules and facts to solve more complex problems.

This documentation provides a comprehensive overview of the code, explaining the purpose and functionality of each component, and includes example usage to demonstrate how the system can be used.