\documentclass{article} \input{common} \title{Summary of Important Definitions} \date{May 11th} \begin{document} \maketitle \section{Lattice Theory} \begin{definition} A {\em partial order} $\preceq$ is a relation over a set $S$ such that for every triple of elements $x, y, z \in S$ the following hold \begin{itemize} \item (reflexivity) $x \preceq x$ \item (antisymmetry) $(x \preceq y \land y \preceq x) \implies x = y$ \item (transitivity) $(x \preceq y \land y \preceq z) \implies (x \preceq z)$ \end{itemize} \end{definition} \begin{definition} Given a partial order $\preceq$ over a set $S$ and a subset $X \subseteq S$, a {\em lower bound} of $X$ (resp.\ an {\em upper bound} of $X$) is an element $x \in S$ (Note that it may be the case that $x \not\in X$) such that \begin{itemize} \item $\forall y \in X, \boxed{x} \preceq y$ \item (resp. $\forall y \in X, \boxed{y} \preceq x$) \end{itemize} The {\em greatest lower bound} of a set $X \subseteq S$ (denoted as $\textbf{glb}(X)$) is a unique upperbound of the set of all lowerbounds of $X$. The {\em least upper bound} of a set $X \subseteq S$ (denoted as $\textbf{lub}(X)$) is a unique lowerbound of the set of all upperbounds of $X$. In general, $\textbf{lub}(X)$ and $\textbf{glb}(X)$ may not exist. \end{definition} \begin{definition} A (complete) lattice $\langle \mathcal{L}, \preceq \rangle$ is a set of elements $\mathcal{L}$ and a partial order $\preceq$ over $\mathcal{L}$ such that for any set $S \subseteq \mathcal{L}$ \begin{itemize} \item $\textbf{lub}(X)$ and $\textbf{glb}(X)$ exist and are unique. \end{itemize} \end{definition} \subsection{Fixpoint Operators} \begin{definition} Given a complete lattice $\langle \mathcal{L}, \preceq \rangle$, a fixpoint operator over the lattice is a function $\Phi: \mathcal{L} \rightarrow \mathcal{L}$ that is {\em $\preceq$-monotone} \begin{itemize} \item $\Phi$ is $\preceq$-monotone if for all $x, y \in \mathcal{L}$ \begin{align*} x \preceq y \implies \Phi(x) \preceq \Phi(y) \end{align*} (Note: this does not mean the function is inflationary, i.e., $x \preceq \Phi(x)$ may not hold) \end{itemize} A {\em fixpoint} is an element $x \in \mathcal{L}$ s.t. $\Phi(x) = x$. \end{definition} \begin{theorem}[Knaster-Tarski] The set of all fixpoints of a fixpoint operator $\Phi$ on a complete lattice is itself a complete lattice. The least element of this new lattice exists and is called the least fixpoint (denoted as $\lfp~{\Phi}$) \end{theorem} Some intuitions about lattices \begin{itemize} \item The entire lattice has a biggest element $\lub{}(\mathcal{L}) = \top$ and a smallest element $\glb{}(\mathcal{L}) = \bot$ \item When a lattice has a finite height (or finite domain). The least fixed point of a fixpoint operator can be computed by iteratively applying the fixpoint operator to $\bot$ \item An operator may return an element that is not comparable to the input, however, after a comparable element is returned (either greater or less than) that comparability and direction are maintained for all subsequent iterations. \item Further, because $\bot$ is less than all elements in the lattice, it is always the case that $\bot \preceq \Phi(\bot)$ \end{itemize} \section{Partial Stable Model Semantics} \begin{definition} A (ground and normal) {\em answer set program} $\P$ is a set of rules where each rule $r$ is of the form \begin{align*} h \leftarrow a_0,~ a_1,~ \dots,~ a_n,~ \Not b_0,~\Not b_1,~\dots,~\Not b_k \end{align*} where we define the following shorthand for a rule $r \in \P$ \begin{align*} \head(r) &= h\\ \bodyp(r) &= \{ a_0,~ a_1,~ \dots,~ a_n \}\\ \bodyn(r) &= \{ b_0,~ b_1,~ \dots,~ b_k \} \end{align*} \end{definition} \begin{definition} A two-valued interpretation $I$ of a program $\P$ is a set of atoms that appear in $\P$. \end{definition} \begin{definition} An interpretation $I$ is a \boxedt{model} of a program $\P$ if for each rule $r \in \P$ \begin{itemize} \item If $\bodyp(r) \subseteq I$ and $\bodyn(r) \intersect I = \emptyset$ then $\head(r) \in I$. \end{itemize} \end{definition} \begin{definition} An interpretation $I$ is a \boxedt{stable model} of a program $\P$ if $I$ is a model of $\P$ \textbf{and} for every interpretation $I' \subseteq I$ there exists a rule $r \in \P$ such that \begin{itemize} \item $\bodyp(r) \subseteq I'$, $\bodyn(r) \intersect I \not= \emptyset$ (Note that this is $I$ and not $I'$) and $\head(r) \not\in I'$ \end{itemize} \end{definition} \begin{definition} A three-valued interpretation $(T, P)$ of a program $\P$ is a pair of sets of atoms such that $T \subseteq P$. The \boxedt{truth-ordering} respects $\ff < \uu < \tt$ and is defined for two three-valued interpretations $(T, P)$ and $(X, Y)$ as follows. \begin{align*} (T, P) \preceq_t (X, Y) \textrm{ iff } T \subseteq X \land P \subseteq Y \end{align*} The \boxedt{precision-ordering} respects the partial order $\uu < \tt$, $\uu < \ff$ and is defined for two three-valued interpretations $(T, P)$ and $(X, Y)$ as follows. \begin{align*} (T, P) \preceq_p (X, Y) \textrm{ iff } T \subseteq X \land \boxed{Y \subseteq P} \end{align*} \end{definition} \begin{definition} A three-valued interpretation $(T, P)$ is a {\em model} of a program $\P$ if for each rule $r \in \P$ \begin{itemize} \item $\body(r) \subseteq P \land \bodyn(r) \intersect T = \emptyset$ implies $\head(r) \in P$, and \item $\body(r) \subseteq T \land \bodyn(r) \intersect P = \emptyset$ implies $\head(r) \in T$. \end{itemize} \end{definition} \begin{definition} A three-valued interpretation $(T, P)$ is a {\em stable model} of a program $\P$ if it is a model of $\P$ and if for every three-valued interpretation $(X, Y)$ such that $(X, Y) \preceq_t (T, P)$ there exists a rule $r \in \P$ such that either \begin{itemize} \item $\bodyp(r) \subseteq Y \land \bodyn(r) \intersect T = \emptyset$ and $\head(r) \not\in Y$ \textbf{OR} \item $\bodyp(r) \subseteq X \land \bodyn(r) \intersect P = \emptyset$ and $\head(r) \not\in X$ \end{itemize} \end{definition} \section{Approximation Fixpoint Theory} We can think of a three-valued interpretation $(T, P)$ as an approximation on the set of true atoms. $T$ is a lower bound and $P$ is the upper bound. \begin{definition} An {\em approximator} is a fixpoint operator on the complete lattice $\langle \wp(\mathcal{L})^2, \preceq_p \rangle$ (called a bilattice) \end{definition} Given a function $f(T, P): S^2 \rightarrow S^2$, we define two separate functions \begin{align*} f(\cdot, P)_1:~ &S \rightarrow S\\ f(T, \cdot)_2:~ &S \rightarrow S \end{align*} such that \begin{align*} f(T, P) = \Big(&(f(\cdot, P)_1)(T), \\&(f(T, \cdot)_2)(P)\Big) \end{align*} \begin{definition} Given an approximator $\Phi(T, P)$ the stable revision operator is defined as follows \begin{align*} S(T, P) = (\lfp{(\Phi(\cdot, P)_1)},~ \lfp{(\Phi(T, \cdot)_2)}) \end{align*} Note: the $\lfp$ is applied to a unary operator, thus it's the least fixpoint of the lattice $\langle \wp(\mathcal{L}), \subseteq \rangle$ whose least element is $\emptyset$. \end{definition} \subsection{An Approximator for Partial Stable Semantics} \begin{align*} \Gamma(T, P) &\define \{ \head(r) ~|~ r \in \P, T \subseteq \bodyp(r), \bodyn(r) \intersect P = \emptyset \}\\ \Gamma(P, T) &\define \{ \head(r) ~|~ r \in \P, P \subseteq \bodyp(r), \bodyn(r) \intersect T = \emptyset \}\\ \Phi(T, P) &\define \Bigl( \Gamma(T, P), \Gamma(P, T) \Bigr) \end{align*} Without stable revision where $(T, P) = (\{ a, b \}, \{ a, b \})$ \begin{align*} \Phi(T, P) = (\{ a, b \}, \{ a, b \}) \textrm{ (fixpoint reached)} \end{align*} With stable revision \begin{align*} S(T, P) = (\emptyset, \emptyset) \textrm{ (fixpoint reached)} \end{align*} \section{The Polynomial Heirarchy} Intuitive definitions of ${\sf NP}$ \begin{itemize} \item the class of problems which have algorithms that can verify solutions in polynomial time. \item A problem that can be solved by reducing it to a SAT expression of the form \begin{align*} \exists (a \lor b \lor c) \land (\neg a \lor \neg d \lor c)~\land \cdots \end{align*} \item (Alternating turing machine) A problem that is solved by some path of an algorithm that is allowed to branch is parallel \end{itemize} Intuitive definitions of ${\sf NP^{NP}}$ a.k.a.\ $\Sigma_2^P$ \begin{itemize} \item the class of problems which have algorithms that can verify solutions in ${\sf NP}$ time. \item A problem that can be solved by reducing it to a SAT expression of the form \begin{align*} \exists c,~ \forall a,~ b,~ (a \lor b \lor c) \land (\neg a \lor \neg d \lor c)~\land \cdots \end{align*} \item (Alternating Turing machine) A problem that is solved by some path of an algorithm that is allowed to branch in parallel. A branch is allowed to switch to ``ALL'' mode only once and require that all subsequent forks return success \end{itemize} \end{document}