epistemic.py

# Epistemic Logic as an extension to BDI (Belief-Desire-Intention) model

# Basic Assumptions: Autoepistemic logic assumes that an agent has some initial beliefs or assumptions about the world.

# Default Rules: Nonmonotonic reasoning in autoepistemic logic involves the use of default rules. 
# These rules capture common-sense reasoning and are assumed to hold in the absence of contradictory evidence.

# Adding Information: When new information is added to the agent's knowledge base, it can lead to the reevaluation of previously drawn conclusions.

# Revised Beliefs: Autoepistemic logic allows the agent to revise its beliefs based on the new information while maintaining consistency with its original beliefs.

# Withdrawal of Conclusions: Unlike classical logic, where the addition of new information only expands the set of valid conclusions, 
# autoepistemic logic permits the withdrawal of previously drawn conclusions when they conflict with the newly acquired information.

# Reasoning Process: The agent's reasoning process involves iteratively considering default rules and their implications in light of the new information. 
# If a contradiction arises, the agent may retract conclusions that were previously drawn based on default rules.

# Python Code Implementation

class AutoepistemicAgent:
    def __init__(self, initial_beliefs):
        self.beliefs = initial_beliefs

    def add_information(self, new_information):
        # Update beliefs with new information
        self.beliefs.update(new_information)

    def revise_beliefs(self):
        # Check if any conclusions based on default rules need to be retracted
        for belief in list(self.beliefs):
            if self.contradicts_new_information(belief):
                self.beliefs.remove(belief)

    def contradicts_new_information(self, belief):
        # Placeholder function to be implemented with actual logic
        return False