Understanding Forward Chaining in Propositional Logic
Forward chaining in propositional logic is a recursive, stack-based version of back-chaining that can be modified to handle variables using unification and negation context. By applying a set of rules and facts, the process aims to prove a given query by iteratively inferring new information. Illustrated steps demonstrate the application of forward chaining with a knowledge base and a specific query, showcasing how the algorithm deduces the possibility of reaching a desired goal through logical reasoning.
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Presentation Transcript
Recursive stack-based version of Back-chaining using Propositional Logic could be modified to handle... variables (using unification) negation context (fail if sub-goal is repeated) Backchain(KB,query) stack {query} // initialize return BC(KB,stack) BC(KB,stack) if stack empty, return True goal stack.pop() if goal KB, return BC(KB,stack) // a known fact for each rule a1..an goal in KB: stack.push(a1..an) result BC(KB,stack) if result=True, return True remove a1..an from stack return False
KB = {CanBikeToWork CanDriveToWork CanWalkToWork HaveBike Sunny OwnCar HaveMoney RentCar HaveMoney TaxiAvailable Sunny CanWalkToWork HaveUmbrella Rainy, // facts HaveBike, HaveMoney, RentCar } CanGetToWork CanGetToWork CanGetToWork CanBikeToWork CanDriveToWork CanDriveToWork CanDriveToWork CanWalkToWork query = CanGetToWork ?
1. {CanGetToWork} // initialize goal stack with query 2. {CanBikeToWork} // replace subgoal with rule 1 3. {HaveBike,Sunny} // push antecedents for rule 4 4. {Sunny} // HaveBike is fact, so pop it 5. backtracksince Sunny in not a fact and can t be proved 6. {CanDriveToWork} // rule 2, another way CanGetToWork 7. {OwnCar} // try rule 5 8. backtrack, not provable 9. {HaveMoney,RentCar} // another way to prove CanDriveToWork 10.{RentCar} // pop HaveMoney since known fact 11.{} // success! empty goal stack, return True
