Why FOPC If your thesis is utter vacuous Use first-order predicate calculus. With sufficient formality The sheerest banality Will be hailed by the critics: "Miraculous!"

11/4 Qns/comments/Concerns on how the semester is going? Project 2 returned; Avg 52/65 Project 3 due; Project 4 assigned.

Apt-pet An apartment pet is a pet that is small Dog is a pet Cat is a pet Elephant is a pet Dogs and cats are small. Some dogs are cute Each dog hates some cat Fido is a dog

Notes on encoding English statements to FOPC You get to decide what your predicates, functions, constants etc. are. All you are required to do it be consistent in their usage. When you write an English sentence into FOPC sentence, you can “double check” by asking yourself if there are worlds where FOPC sentence doesn’t hold and the English one holds and vice versa Since you are allowed to make your own predicate and function names, it is quite possible that two people FOPCizing the same KB may wind up writing two syntactically different KBs If each of the DBs is used in isolation, there is no problem. However, if the knowledge written in one DB is supposed to be used in conjunction with that in another DB, you will need “Mapping axioms” which relate the “vocabulary” in one DB to the vocabulary in the other DB. This problem is PRETTY important in the context of Semantic Web The “Semantic Web” Connection

Caveat: Decide whether a symbol is predicate, constant or function… Make sure you decide what are your constants, what are your predicates and what are your functions Once you decide something is a predicate, you cannot use it in a place where a predicate is not expected! In the previous example, you cannot say

Caveat: Order of quantifiers matters “either Fido loves both Fido and Tweety; or Tweety loves both Fido and Tweety” “ Fido or Tweety loves Fido; and Fido or Tweety loves Tweety” Loves(x,y) means x loves y

More on writing sentences Forall usually goes with implications (rarely with conjunctive sentences) There-exists usually goes with conjunctions—rarely with implications Everyone at ASU is smart Someone at UA is smart

Two different Tarskian Interpretations This is the same as the one on The left except we have green guy for Richard Problem: There are too darned many Tarskian interpretations. Given one, you can change it by just substituting new real-world objects  Substitution-equivalent Tarskian interpretations give same valuations to the FOPC statements (and thus do not change entailment)  Think in terms of equivalent classes of Tarskian Interpretations (Herbrand Interpretations)

Herbrand Interpretations Herbrand Universe –All constants Rao,Pat –All “ground” functional terms Son-of(Rao);Son-of(Pat); Son-of(Son-of(…(Rao)))…. Herbrand Base –All ground atomic sentences made with terms in Herbrand universe Friend(Rao,Pat);Friend(Pat,Rao);Friend(P at,Pat);Friend(Rao,Rao) Friend(Rao,Son-of(Rao)); Friend(son-of(son-of(Rao),son-of(son- of(son-of(Pat)) –We can think of elements of HB as propositions; interpretations give T/F values to these. Given the interpretation, we can compute the value of the FOPC database sentences If there are n constants; and p k-ary predicates, then --Size of HU = n --Size of HB = p*n k But if there is even one function, then |HU| is infinity and so is |HB|. --So, when there are no function symbols, FOPC is really just syntactic sugaring for a (possibly much larger) propositional database Let us think of interpretations for FOPC that are more like interpretations for prop logic

Proof-theoretic Inference in first order logic For “ground” sentences (i.e., sentences without any quantification), all the old rules work directly (think of ground atomic sentences as propositions) –P(a,b)=> Q(a); P(a,b) |= Q(a) –~P(a,b) V Q(a) resolved with P(a,b) gives Q(a) What about quantified sentences? –May be infer ground sentences from them…. –Universal Instantiation (a universally quantified statement entails every instantiation of it) –Existential instantiation (an existentially quantified statement holds for some term (not currently appearing in the KB). Can we combine these (so we can avoid unnecessary instantiations?) Yes. Generalized modus ponens Needs UNIFICATION

11/6 90 AU from earth.. Going a million miles a day.. Should reach the neighboring star anyday now (okay, in about ~40,000 years) 11/6

UI can be applied several times to add new sentences --The resulting KB is equivalent to the old one EI can only applied once --The resulting DB is not equivalent to the old one BUT will be satisfiable only when the old one is

How about knows(x,f(x)) knows(u,u)? x/u; u/f(u)  leads to infinite regress (“occurs check”)

GMP can be used in the “forward” (aka “bottom-up”) fashion where we start from antecedents, and assert the consequent or in the “backward” (aka “top-down”) fashion where we start from consequent, and subgoal on proving the antecedents.

Forward (bottom-up) vs. Backward (top-down) chaining Forward chaining fires rules starting from facts –Using P, derive Q –Using Q & R, derive S – Using S, derive Z – Using Z, Q, derive W –Using Q, derive J –No more inferences. Check if J holds. It does. So proved Backward chaining starts from the theorem to be proved –We want to prove J. –Using Q=>J, we can subgoal on Q –Using P=>Q, we can subgoal on P –P holds. We are done. Suppose we have P => Q Q & R =>S S => Z Z & Q => W Q => J P R We want to prove J Forward chaining allows parallel derivation of many facts together; but it may derive facts that are not relevant for the theorem. Backward chaining concentrates on proving subgoals that are relevant to the theorem. However, it proves theorems one at a time. Some similarity with progression vs. regression…

Datalog and Deductive Databases A deductive database is a generalization of relational database, where in addition to the relational store, we also have a set of “rules”. –The rules are in definite clause form (universally quantified implications, with one non-negated head, and a conjunction of non-negated tails) When a query is asked, the answers are retrieved both from the relational store, and by deriving new facts using the rules. The inference in deductive databases thus involves using GMP rule. Since deductive databases have to derived all answers for a query, top-down evaluation winds up being too inefficient. So, bottom-up (forward chaining) evaluation is used (which tends to derive non-relevant facts  A neat idea called magic-sets allows us to temporarily change the rules (given a specific query), such that forward chaining on the modified rules will avoid deriving some of the irrelevant facts. Base facts P(a,b),Q(b) R(c).. Rules P(x,y),Q(y)=>R(y) ?R(z) RDBMS R(c); R(b).. This stuff was discussed in the class at a high-level, and is a One slide summary of CSE 513

Apt-pet An apartment pet is a pet that is small Dog is a pet Cat is a pet Elephant is a pet Dogs, cats and skunks are small. Fido is a dog Louie is a skunk Garfield is a cat Clyde is an elephant Is there an apartment pet?

Generate compilable matchers for each pattern, and use them