Editing Symbolic Ai (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''Building a rule-based expert system in Python:'''

<syntaxhighlight lang="python">
class ExpertSystem:
    """Simple forward-chaining expert system for loan assessment."""

    def __init__(self):
        self.facts = {}
        self.conclusions = []

    def assert_fact(self, key, value):
        self.facts[key] = value

    def run(self):
        """Apply all rules and collect conclusions."""

        # Rule 1: Credit score classification
        if self.facts.get('credit_score', 0) >= 750:
            self.assert_fact('credit_rating', 'excellent')
        elif self.facts.get('credit_score', 0) >= 650:
            self.assert_fact('credit_rating', 'good')
        else:
            self.assert_fact('credit_rating', 'poor')

        # Rule 2: Debt-to-income ratio
        dti = self.facts.get('monthly_debt', 0) / max(self.facts.get('monthly_income', 1), 1)
        self.assert_fact('dti_ratio', dti)

        if dti <= 0.36:
            self.assert_fact('dti_status', 'acceptable')
        else:
            self.assert_fact('dti_status', 'high')

        # Rule 3: Employment stability
        if self.facts.get('employment_years', 0) >= 2:
            self.assert_fact('employment_status', 'stable')
        else:
            self.assert_fact('employment_status', 'unstable')

        # Rule 4: Final decision (requires ALL conditions)
        if (self.facts.get('credit_rating') in ['excellent', 'good'] and
                self.facts.get('dti_status') == 'acceptable' and
                self.facts.get('employment_status') == 'stable'):
            return 'APPROVED', self._explain()
        else:
            return 'DENIED', self._explain()

    def _explain(self):
        return {k: v for k, v in self.facts.items()}

# Usage
system = ExpertSystem()
system.assert_fact('credit_score', 720)
system.assert_fact('monthly_debt', 800)
system.assert_fact('monthly_income', 3500)
system.assert_fact('employment_years', 3)
decision, explanation = system.run()
print(f"Decision: {decision}")
print(f"Reasoning: {explanation}")
</syntaxhighlight>

; Symbolic AI tools and languages
: '''Prolog''' → Logic programming, backward chaining; used in NLP parsing, planning
: '''Clips''' → C Language Integrated Production System; forward-chaining rule engine
: '''Drools''' → Java-based business rule management system; widely used in enterprise
: '''OWL/RDF''' → Web Ontology Language; semantic web knowledge representation
: '''SPARQL''' → Query language for RDF knowledge graphs
: '''Planning Domain Definition Language (PDDL)''' → Standard for AI planning problems
</div>

<div style="background-color: #8B4500; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">