Changeset 12728
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- 26.10.2009 16:37:55 (4 weeks ago)
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trunk/GMP/papers/conditional/example.tex (modified) (6 diffs)
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trunk/GMP/papers/conditional/example.tex
r12685 r12728 263 263 conditional $\Rightarrow$, but only replacement of equivalents in the 264 264 left-hand arguments. Absorption of cut and contraction requires a 265 unified rule set consisting of the rules 266 \begin{equation*} 267 \infrule{A_0=A_1;\;\dots;\; A_n=A_0\quad \neg B_1,\dots,\neg B_1,B_0} 268 {\neg(A_1\Rightarrow B_1),\dots,\neg(A_n\Rightarrow B_n),(A_0\Rightarrow B_0)} 269 \end{equation*} 265 unified rule set consisting of the rules depicted in Fig.~\ref{fig:modalCK} 270 266 with $A=C$ abbreviating the pair of sequents $\neg A,C$ and $\neg 271 C,A$. This illustrates two important points that play a role in the 267 C,A$. 268 \begin{figure}[!h] 269 \begin{center} 270 \begin{tabular}{| c |} 271 \hline 272 \\[-5pt] 273 (\textsc {\textbf{CK}})\inferrule{A_0=A_1;\dots;A_n=A_0\\ \neg B_1,\dots,\neg B_1,B_0} 274 {\Gamma, \neg(A_1\Rightarrow B_1),\dots,\neg(A_n\Rightarrow B_n),(A_0\Rightarrow B_0)}\\[-5pt] 275 \\ 276 \hline 277 \end{tabular} 278 \end{center} 279 \caption{The modal rule of \textbf{CK}} 280 \label{fig:modalCK} 281 \end{figure} 282 %\begin{equation*} 283 % \infrule{A_0=A_1;\;\dots;\; A_n=A_0\quad \neg B_1,\dots,\neg B_1,B_0} 284 % {\neg(A_1\Rightarrow B_1),\dots,\neg(A_n\Rightarrow B_n),(A_0\Rightarrow B_0)} 285 %\end{equation*} 286 This illustrates two important points that play a role in the 272 287 optimisation strategies described below. First, the rule has a large 273 288 branching degree both existentially and universally -- we have to … … 290 305 According to the framework introduced above, we now devise a generic 291 306 algorithm to decide the provability of formulas, which is easily 292 instantiated a tospecific logic by just implementing the relevant modal307 instantiated to a specific logic by just implementing the relevant modal 293 308 rules. 294 309 … … 305 320 starting point of the proof search algorithms employed in CoLoSS, 306 321 being essentially a sequent reformulation of the algorithm described 307 in~\cite{CalinEA09}; optimisations of this algorithm are the subject 308 of the present work. 322 in~\cite{CalinEA09}, where it is easily verified that correctness and termination 323 of the algorithm are preserved by this reformulation; optimisations of this 324 algorithm are the subject of the present work. 309 325 310 326 %\begin{algorithm}[h] … … 344 360 The genericity of the introduced sequent calculus allows us to easiliy 345 361 create instantiations of Algorithm~\ref{alg:seq} for a large variety 346 of modal logics: By setting $\mathcal R^m_{sc}$ for instance to the 347 rule shown in Figure~\ref{fig:modalCKCEM}, 348 % (where $A_a = A_b$ is 349 %shorthand for $A_a\rightarrow A_b\wedge A_b\rightarrow A_a$), 350 we obtain an algorithm for deciding provability (and satisfiability) 351 of conditional logic. We restrict ourselves to the examplary 362 of modal logics: 363 364 \noindent For instance it has been shown in~\cite{PattinsonSchroder09a} that the 365 complexity of \textbf{CKCEM} is $\mathit{coNP}$, using a dynamic 366 programming approach in the spirit of~\cite{Vardi89}; in fact, this 367 was the original motivation for exploring the optimisation strategies 368 pursued here. 369 Due to this reason, we restrict ourselves to the examplary 352 370 conditional logic \textbf{CKCEM} for the remainder of this section; 353 371 slightly adapted versions of the optimisation will work for other … … 355 373 axioms of \emph{conditional excluded middle} $(A\Rightarrow 356 374 B)\lor(A\Rightarrow\neg B)$, which to absorb cut and contraction is 357 integrated in the rule for \textbf{CK} as follows. 375 integrated in the rule for \textbf{CK} as shown in Figure~\ref{fig:modalCKCEM}. 376 377 %By setting $\mathcal R^m_{sc}$ for to the 378 %rule shown in Figure~\ref{fig:modalCKCEM}, 379 % (where $A_a = A_b$ is 380 %shorthand for $A_a\rightarrow A_b\wedge A_b\rightarrow A_a$), 381 %we obtain an algorithm for deciding provability (and satisfiability) 382 %of conditional logic. 358 383 359 384 \begin{figure}[h!] … … 372 397 \label{fig:modalCKCEM} 373 398 \end{figure} 374 \noindent It has been shown in~\cite{PattinsonSchroder09a} that the375 complexity of \textbf{CKCEM} is $\mathit{coNP}$, using a dynamic376 programming approach in the spirit of~\cite{Vardi89}; in fact, this377 was the original motivation for exploring the optimisation strategies378 pursued here.379 399 380 400 In the following, we use the notions of \emph{conditional antecedent} and