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Reductions among KR Formalisms

We now define the forms of reduction between PKR formalisms that we analyze in the following sections. A formula can always be represented as a string over an alphabet $\Sigma$, hence from now on we consider translations as functions transforming strings.

Let $F_1$ and $F_2$ be two PKR formalisms. There exists a poly-size reduction from $F_1$ to $F_2$, denoted as $f:F_1 \mapsto F_2$, if $f$ is a poly-size function such that for any given knowledge base $KB$ in $F_1$, $f(KB)$ is a knowledge base in $F_2$. Clearly, reductions should be restricted to produce a meaningful output. In particular, we now discuss reductions that preserve the models of the original theory.

The semantic approach by Gogic and collegues [21] is that the models of the two knowledge bases must be exactly the same. In other words, if a knowledge base $KB$ of the formalism $F_1$ is translated into a knowledge base $KB'$ of the formalism $F_2$, then $M
\models_{F_1} KB$ if and only if $M \models_{F_2} KB'$. This approach can be summarized by: a reduction between formalisms $F_{1}$ and $F_{2}$ is a way to translate knowledge bases of $F_{1}$ into knowledge bases of $F_{2}$, preserving their sets of models. While this semantics is intuitively grounded, it is very easy to show examples in which two formalisms that we consider equally space-efficient cannot be translated to each other. Let us consider for instance a variant of the propositional calculus in which the syntax is that formulas must be of the form $x_1 \wedge F$, where $F$ is a regular formula over the variables $x_2, \ldots$. Clearly, this formalism is able to represent knowledge in the same space than the propositional calculus (apart a polynomial factor). However, according to the definition, this formalism cannot be translated to propositional calculus: there is no knowledge base that is equivalent to $KB=\neg x_1$. Indeed, the only model of $KB$ is $\emptyset$, while any model of any consistent knowledge base of the modified propositional calculus contains $x_1$.

We propose a more general approach that can deal also with functions $f$ that change the language of the $KB$. To this end, we allow for a translation $g_{KB}$ from models of $KB$ to models of $f(KB)$. We stress that, to be as general as possible, the translation may depend on $KB$ -- i.e., different knowledge bases may have different translations of their models. We want this translation easy to compute, since otherwise the computation of $g_{KB}$ could hide the complexity of reasoning in the formalism. However, observe that to this end, it is not sufficient to impose that $g_{KB}$ is computable in polynomial time. In fact, once $KB$ is fixed, its models could be trivially translated to models of $f(KB)$ in constant time, using a lookup table. This table would be exponentially large, though; and this is what we want to forbid. Hence, we impose that $g_{KB}$ is a circuit of polynomial-size wrt $KB$. We still use a functional notation $g_{KB}(M)$ to denote the result of applying a model $M$ to the circuit $g_{KB}$. A formal definition follows.

Definition 8 (Model Preservation)   A poly-size reduction $f:F_1 \mapsto F_2$ satisfies model-preservation if there exists a polynomial $p$ such that, for each knowledge base $KB$ in $F_1$ there exists a circuit $g_{KB}$ whose size is bounded by $p(\vert KB\vert)$, and such that for every interpretation $M$ of the variables of $KB$ it holds that $M
\models_{F_1} KB$ iff $g_{KB}(M) \models_{F_2} f(KB)$.

The rationale of model-preserving reduction is that the knowledge base $KB$ of the first formalism $F_1$ can be converted into a knowledge base $f(KB)$ in the second one $F_2$, and this reduction should be such that each model $M$ in $F_1$ can be easily translated into a model $g_{KB}(M)$ in $F_2$.

We require $g$ to depend on $KB$, because the transformation $f$, in general, could take the actual form of $KB$ into account. This happens in the following example of a model-preserving translation.

Example 3  

We reduce a fragment of skeptical default logic [29] to circumscription with varying letters, using the transformation introduced by Etherington [15]. Let $\langle D, W \rangle $ be a prerequisite-free normal (PFN) default theory, i.e., all defaults are of the form $\frac{~:\gamma}{\gamma}$, where $\gamma$ is a generic formula. Let $Z$ be the set of letters occurring in $\langle D, W \rangle $. Define $P_D$ as the set of letters $\{a_\gamma \vert \frac{~:\gamma}{\gamma} \in D\}$. The function $f$ can be defined in the following way: $f(\langle D, W \rangle ) = CIRC(T;P_D;Z)$, where $T = W \cup \{ a_\gamma \equiv \neg \gamma \vert a_\gamma \in
P_D\}$, $P_D$ are the letters to be minimized, and $Z$ (the set of letters occurring in $\langle D, W \rangle $) are varying letters. We show that $f$ is a model-preserving poly-size reduction. In fact, given a set of PFN defaults $D$ let $g_{D}$ be a function such that for each interpretation $M$ for $Z$, $g_{D}(M) = M \cup \{a_\gamma \in P_D \vert
M\models \neg\gamma\}$. Clearly, $f$ is poly-size, $g_{D}$ can be realized by a circuit whose size is polynomial in $\vert D\vert$, and $M$ is a model of at least one extension of $\langle D, W \rangle $ iff $g_{D}(M)
\models CIRC(T;P_D;Z)$. The dependence of $g$ only on $D$ stresses the fact that, in this case, the circuit $g$ does not depend on the whole knowledge base $\langle D, W \rangle $, but just on $D$.

Clearly, when models are preserved, theorems are preserved as well. A weaker form of reduction is the following one, where only theorems are preserved. Also in this case we allow theorems of $KB$ to be translated by a ``simple" circuit $g_{KB}$ to theorems of $KB$.

Definition 9 (Theorem Preservation)  

A poly-size reduction $f:F_1 \mapsto F_2$ satisfies theorem-preservation if there exists a polynomial $p$ such that, for each knowledge base $KB$ in $F_1$, there exists a circuit $g_{KB}$ whose size is bounded by $p(\vert KB\vert)$, and such that for every formula $\varphi$ on the variables of $KB$, it holds that $KB \vdash_{F_1}
\varphi$ iff $f(KB) \vdash_{F_2} g_{KB}(\varphi)$.

The theorem-preserving reduction has a property similar to that of the model-preserving reduction, when the knowledge bases are used to represent theorems rather than models. Namely, a knowledge base $KB$ is translated into another knowledge base $f(KB)$ which can be used to represent the same set of theorems. More precisely, we have that each theorem $\varphi$ of $KB$ is represented by a theorem $g_{KB}(\varphi)$ of $f(KB)$.

Winslett [50] has shown an example of a reduction from updated knowledge bases to PL that is theorem-preserving but not model-preserving. Using Winslett's reduction, one could use the same machinery for propositional reasoning in the $KB$, both before and after the update (plus the reduction). Also the reduction shown in the previous Example 3 is theorem-preserving, this time $g$ being the identity circuit.

We remark that our definitions of reduction are more general than those proposed by Gogic and collegues [21]. In fact, these authors consider only a notion analogous to Definition 8. and only for the case when $g$ is the identity -- i.e., models in the two formalisms should be identical. By allowing a simple translation $g$ between models Definition 8 covers more general forms of reductions preserving models, like the one of Example 3.

next up previous
Next: Comparing the Space Efficiency Up: Space Efficiency of Propositional Previous: Compilability Classes
Paolo Liberatore