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x, 389 p. : 24 cm

x, 389 p. : 24 cm

This volume contains the papers presented at Arg-LPNMR 2016: First International Workshop on Argumentation in Logic Programming and Nonmonotonic Reasoning held on July 8-10, 2016 in New York City, NY.

x, 324 p. : 25 cm

x, 324 p. : 25 cm

x, 324 p. : 25 cm

x, 324 p. : 25 cm

x, 324 p. : 25 cm

logic with random max notes programming

logic with random max notes programming

logic with random max notes programming

logic with random max notes programming

logic with random max notes programming

logic with random max notes programming

logic with random max notes programming

The Journal of Logic Programming 1990: Volume 9 , Issue CONTENTS. Digitized from IA1652909-07 . Previous issue: sim_journal-of-logic-and-algebraic-programming_1990_9_index . Next issue: sim_journal-of-logic-and-algebraic-programming_1990-07_9_1 .

From the bitsavers.org collection, a scanned-in computer-related document. test equipment :: hp :: 165xx :: Preliminary Programming Information for HP 16500A Logic Analysis System Dec87

From the bitsavers.org collection, a scanned-in computer-related document. test equipment :: hp :: 165xx :: Preliminary Programming Information for HP 16500A Logic Analysis System Dec87

Disjunctive Logic Programming (\DLP) is an advanced formalism for Knowledge Representation and Reasoning (KRR). \DLP is very expressive in a precise mathematical sense: it allows to express every property of finite structures that is decidable in the complexity class $\SigmaP{2}$ ($\NP^{\NP}$). Importantly, the \DLP encodings are often simple and natural. In this paper, we single out some limitations of \DLP for KRR, which cannot naturally express problems where the size of the disjunction is not known ``a priori'' (like N-Coloring), but it is part of the input. To overcome these limitations, we further enhance the knowledge modelling abilities of \DLP, by extending this language by {\em Parametric Connectives (OR and AND)}. These connectives allow us to represent compactly the disjunction/conjunction of a set of atoms having a given property. We formally define the semantics of the new language, named $DLP^{\bigvee,\bigwedge}$ and we show the usefulness of the new constructs on relevant knowledge-based problems. We address implementation issues and discuss related works.

The Journal of Logic Programming 1994: Volume 19-20 , Issue CONTENTS. Digitized from IA1652909-07 . Previous issue: sim_journal-of-logic-and-algebraic-programming_1994_19-20_index . Next issue: sim_journal-of-logic-and-algebraic-programming_may-july-1994_19-20 .

The Journal of Logic Programming 1995: Volume 25 , Issue Index. Digitized from IA1652909-07 . Previous issue: sim_journal-of-logic-and-algebraic-programming_1995-09_24_3 . Next issue: sim_journal-of-logic-and-algebraic-programming_1995_25_contents .

The Journal of Logic and Algebraic Programming 1997: Volume 33 , Issue CONTENTS. Digitized from IA1652909-05 . Previous issue: sim_journal-of-logic-and-algebraic-programming_1997_33_index . Next issue: sim_journal-of-logic-and-algebraic-programming_1997-10_33_1 . Note: This issue has no cover

The Journal of Logic and Algebraic Programming 1998: Volume 34 , Issue CONTENTS. Digitized from IA1652909-05 . Previous issue: sim_journal-of-logic-and-algebraic-programming_1998_34_index . Next issue: sim_journal-of-logic-and-algebraic-programming_1998-01_34_1 . Note: This issue has no cover

The Journal of Logic and Algebraic Programming 1999: Volume 40 , Issue Index. Digitized from IA1652909-05 . Previous issue: sim_journal-of-logic-and-algebraic-programming_april-june-1999_39_1-3 . Next issue: sim_journal-of-logic-and-algebraic-programming_1999_40_contents . Note: This issue has no cover

The Journal of Logic and Algebraic Programming 1999: Volume 40 , Issue Index. Digitized from IA1652909-05 . Previous issue: sim_journal-of-logic-and-algebraic-programming_april-june-1999_39_1-3 . Next issue: sim_journal-of-logic-and-algebraic-programming_1999_40_contents . Note: This issue has no cover

The Journal of Logic and Algebraic Programming 1999: Volume 40 , Issue Index. Digitized from IA1652909-05 . Previous issue: sim_journal-of-logic-and-algebraic-programming_april-june-1999_39_1-3 . Next issue: sim_journal-of-logic-and-algebraic-programming_1999_40_contents . Note: This issue has no cover

We introduce a lazy approach to the explanation-based approximation of probabilistic logic programs. It uses only the most significant part of the program when searching for explanations. The result is a fast and anytime approximate inference algorithm which returns hard lower and upper bounds on the exact probability. We experimentally show that this method outperforms state-of-the-art approximate inference.

With sound unification, Definite Clause Grammars and compact expression of combinatorial generation algorithms, logic programming is shown to conveniently host a declarative playground where interesting properties and behaviors emerge from the interaction of heterogenous but deeply connected computational objects. Compact combinatorial generation algorithms are given for several families of lambda terms, including open, closed, simply typed and linear terms as well as type inference and normal order reduction algorithms. We describe a Prolog-based combined lambda term generator and type-inferrer for closed well-typed terms of a given size, in de Bruijn notation. We introduce a compressed de Bruijn representation of lambda terms and define its bijections to standard representations. Our compressed terms facilitate derivation of size-proportionate ranking and unranking algorithms of lambda terms and their inferred simple types. The S and K combinator expressions form a well-known Turing-complete subset of the lambda calculus. We specify evaluation, type inference and combinatorial generation algorithms for SK-combinator trees. In the process, we unravel properties shedding new light on interesting aspects of their structure and distribution. A uniform representation, as binary trees with empty leaves, is given to expressions built with Rosser's X-combinator, natural numbers, lambda terms and simple types. Using this shared representation, ranking/unranking algorithm of lambda terms to tree-based natural numbers are described. Our algorithms, expressed as an incrementally developed literate Prolog program, implement a declarative playground for exploration of representations, encodings and computations with uniformly represented lambda terms, types, combinators and tree-based arithmetic.

The emphasis is made on the juxtaposition of (quantum~theorem) proving versus quantum (theorem~proving). The logical contents of verification of the statements concerning quantum systems is outlined. The Zittereingang (trembling input) principle is introduced to enhance the resolution of predicate satisfiability problem provided the processor is in a position to perform operations with continuous input. A realization of Zittereingang machine by a quantum system is suggested.

The emphasis is made on the juxtaposition of (quantum~theorem) proving versus quantum (theorem~proving). The logical contents of verification of the statements concerning quantum systems is outlined. The Zittereingang (trembling input) principle is introduced to enhance the resolution of predicate satisfiability problem provided the processor is in a position to perform operations with continuous input. A realization of Zittereingang machine by a quantum system is suggested.

The emphasis is made on the juxtaposition of (quantum~theorem) proving versus quantum (theorem~proving). The logical contents of verification of the statements concerning quantum systems is outlined. The Zittereingang (trembling input) principle is introduced to enhance the resolution of predicate satisfiability problem provided the processor is in a position to perform operations with continuous input. A realization of Zittereingang machine by a quantum system is suggested.

The emphasis is made on the juxtaposition of (quantum~theorem) proving versus quantum (theorem~proving). The logical contents of verification of the statements concerning quantum systems is outlined. The Zittereingang (trembling input) principle is introduced to enhance the resolution of predicate satisfiability problem provided the processor is in a position to perform operations with continuous input. A realization of Zittereingang machine by a quantum system is suggested.

In order to give appropriate semantics to qualitative conditionals of the form "if A then normally B", ordinal conditional functions (OCFs) ranking the possible worlds according to their degree of plausibility can be used. An OCF accepting all conditionals of a knowledge base R can be characterized as the solution of a constraint satisfaction problem. We present a high-level, declarative approach using constraint logic programming techniques for solving this constraint satisfaction problem. In particular, the approach developed here supports the generation of all minimal solutions; these minimal solutions are of special interest as they provide a basis for model-based inference from R.

Paisley is an extensible lightweight embedded domain-specific language for nondeterministic pattern matching in Java. Using simple APIs and programming idioms, it brings the power of functional-logic processing of arbitrary data objects to the Java platform, without constraining the underlying object-oriented semantics. Here we present an extension to the Paisley framework that adds pattern-based control flow. It exploits recent additions to the Java language, namely functional interfaces and lambda expressions, for an explicit and transparent continuation-passing style approach to control. We evaluate the practical impact of the novel features on a real-world case study that reengineers a third-party open-source project to use Paisley in place of conventional object-oriented data query idioms. We find the approach viable for incremental refactoring of legacy code, with significant qualitative improvements regarding separation of concerns, clarity and intentionality, thus making for easier code understanding, testing and debugging.

In this thesis, we present two approaches to a rigorous mathematical and algorithmic foundation of quantitative and statistical inference in constraint-based natural language processing. The first approach, called quantitative constraint logic programming, is conceptualized in a clear logical framework, and presents a sound and complete system of quantitative inference for definite clauses annotated with subjective weights. This approach combines a rigorous formal semantics for quantitative inference based on subjective weights with efficient weight-based pruning for constraint-based systems. The second approach, called probabilistic constraint logic programming, introduces a log-linear probability distribution on the proof trees of a constraint logic program and an algorithm for statistical inference of the parameters and properties of such probability models from incomplete, i.e., unparsed data. The possibility of defining arbitrary properties of proof trees as properties of the log-linear probability model and efficiently estimating appropriate parameter values for them permits the probabilistic modeling of arbitrary context-dependencies in constraint logic programs. The usefulness of these ideas is evaluated empirically in a small-scale experiment on finding the correct parses of a constraint-based grammar. In addition, we address the problem of computational intractability of the calculation of expectations in the inference task and present various techniques to approximately solve this task. Moreover, we present an approximate heuristic technique for searching for the most probable analysis in probabilistic constraint logic programs.

(We apologize for pidgin LaTeX) Schlipf \cite{sch91} proved that Stable Logic Programming (SLP) solves all $\mathit{NP}$ decision problems. We extend Schlipf's result to prove that SLP solves all search problems in the class $\mathit{NP}$. Moreover, we do this in a uniform way as defined in \cite{mt99}. Specifically, we show that there is a single $\mathrm{DATALOG}^{\neg}$ program $P_{\mathit{Trg}}$ such that given any Turing machine $M$, any polynomial $p$ with non-negative integer coefficients and any input $\sigma$ of size $n$ over a fixed alphabet $\Sigma$, there is an extensional database $\mathit{edb}_{M,p,\sigma}$ such that there is a one-to-one correspondence between the stable models of $\mathit{edb}_{M,p,\sigma} \cup P_{\mathit{Trg}}$ and the accepting computations of the machine $M$ that reach the final state in at most $p(n)$ steps. Moreover, $\mathit{edb}_{M,p,\sigma}$ can be computed in polynomial time from $p$, $\sigma$ and the description of $M$ and the decoding of such accepting computations from its corresponding stable model of $\mathit{edb}_{M,p,\sigma} \cup P_{\mathit{Trg}}$ can be computed in linear time. A similar statement holds for Default Logic with respect to $\Sigma_2^\mathrm{P}$-search problems\footnote{The proof of this result involves additional technical complications and will be a subject of another publication.}.

(We apologize for pidgin LaTeX) Schlipf \cite{sch91} proved that Stable Logic Programming (SLP) solves all $\mathit{NP}$ decision problems. We extend Schlipf's result to prove that SLP solves all search problems in the class $\mathit{NP}$. Moreover, we do this in a uniform way as defined in \cite{mt99}. Specifically, we show that there is a single $\mathrm{DATALOG}^{\neg}$ program $P_{\mathit{Trg}}$ such that given any Turing machine $M$, any polynomial $p$ with non-negative integer coefficients and any input $\sigma$ of size $n$ over a fixed alphabet $\Sigma$, there is an extensional database $\mathit{edb}_{M,p,\sigma}$ such that there is a one-to-one correspondence between the stable models of $\mathit{edb}_{M,p,\sigma} \cup P_{\mathit{Trg}}$ and the accepting computations of the machine $M$ that reach the final state in at most $p(n)$ steps. Moreover, $\mathit{edb}_{M,p,\sigma}$ can be computed in polynomial time from $p$, $\sigma$ and the description of $M$ and the decoding of such accepting computations from its corresponding stable model of $\mathit{edb}_{M,p,\sigma} \cup P_{\mathit{Trg}}$ can be computed in linear time. A similar statement holds for Default Logic with respect to $\Sigma_2^\mathrm{P}$-search problems\footnote{The proof of this result involves additional technical complications and will be a subject of another publication.}.

We present the CIFF proof procedure for abductive logic programming with constraints, and we prove its correctness. CIFF is an extension of the IFF proof procedure for abductive logic programming, relaxing the original restrictions over variable quantification (allowedness conditions) and incorporating a constraint solver to deal with numerical constraints as in constraint logic programming. Finally, we describe the CIFF system, comparing it with state of the art abductive systems and answer set solvers and showing how to use it to program some applications. (To appear in Theory and Practice of Logic Programming - TPLP).

The Journal of Logic Programming 1993: Volume 17 , Issue CONTENTS. Digitized from IA1652909-07 . Previous issue: sim_journal-of-logic-and-algebraic-programming_1993_17_index . Next issue: sim_journal-of-logic-and-algebraic-programming_1993-10_17_1 .

The Journal of Logic Programming 1994: Volume 21 , Issue CONTENTS. Digitized from IA1652909-07 . Previous issue: sim_journal-of-logic-and-algebraic-programming_1994_21_index . Next issue: sim_journal-of-logic-and-algebraic-programming_1994-08_21_1 .

The Journal of Logic and Algebraic Programming 1999: Volume 38 , Issue Index. Digitized from IA1652909-05 . Previous issue: sim_journal-of-logic-and-algebraic-programming_october-december-1998_37_1-3 . Next issue: sim_journal-of-logic-and-algebraic-programming_1999_38_contents . Note: This issue has no cover

The Journal of Logic and Algebraic Programming 1999: Volume 38 , Issue Index. Digitized from IA1652909-05 . Previous issue: sim_journal-of-logic-and-algebraic-programming_october-december-1998_37_1-3 . Next issue: sim_journal-of-logic-and-algebraic-programming_1999_38_contents . Note: This issue has no cover

The Journal of Logic and Algebraic Programming 1999: Volume 38 , Issue Index. Digitized from IA1652909-05 . Previous issue: sim_journal-of-logic-and-algebraic-programming_october-december-1998_37_1-3 . Next issue: sim_journal-of-logic-and-algebraic-programming_1999_38_contents . Note: This issue has no cover

The Journal of Logic and Algebraic Programming 1999: Volume 38 , Issue Index. Digitized from IA1652909-05 . Previous issue: sim_journal-of-logic-and-algebraic-programming_october-december-1998_37_1-3 . Next issue: sim_journal-of-logic-and-algebraic-programming_1999_38_contents . Note: This issue has no cover

The Journal of Logic and Algebraic Programming 1999: Volume 38 , Issue Index. Digitized from IA1652909-05 . Previous issue: sim_journal-of-logic-and-algebraic-programming_october-december-1998_37_1-3 . Next issue: sim_journal-of-logic-and-algebraic-programming_1999_38_contents . Note: This issue has no cover

Lifted inference has been proposed for various probabilistic logical frameworks in order to compute the probability of queries in a time that depends on the size of the domains of the random variables rather than the number of instances. Even if various authors have underlined its importance for probabilistic logic programming (PLP), lifted inference has been applied up to now only to relational languages outside of logic programming. In this paper we adapt Generalized Counting First Order Variable Elimination (GC-FOVE) to the problem of computing the probability of queries to probabilistic logic programs under the distribution semantics. In particular, we extend the Prolog Factor Language (PFL) to include two new types of factors that are needed for representing ProbLog programs. These factors take into account the existing causal independence relationships among random variables and are managed by the extension to variable elimination proposed by Zhang and Poole for dealing with convergent variables and heterogeneous factors. Two new operators are added to GC-FOVE for treating heterogeneous factors. The resulting algorithm, called LP$^2$ for Lifted Probabilistic Logic Programming, has been implemented by modifying the PFL implementation of GC-FOVE and tested on three benchmarks for lifted inference. A comparison with PITA and ProbLog2 shows the potential of the approach.

Lifted inference has been proposed for various probabilistic logical frameworks in order to compute the probability of queries in a time that depends on the size of the domains of the random variables rather than the number of instances. Even if various authors have underlined its importance for probabilistic logic programming (PLP), lifted inference has been applied up to now only to relational languages outside of logic programming. In this paper we adapt Generalized Counting First Order Variable Elimination (GC-FOVE) to the problem of computing the probability of queries to probabilistic logic programs under the distribution semantics. In particular, we extend the Prolog Factor Language (PFL) to include two new types of factors that are needed for representing ProbLog programs. These factors take into account the existing causal independence relationships among random variables and are managed by the extension to variable elimination proposed by Zhang and Poole for dealing with convergent variables and heterogeneous factors. Two new operators are added to GC-FOVE for treating heterogeneous factors. The resulting algorithm, called LP$^2$ for Lifted Probabilistic Logic Programming, has been implemented by modifying the PFL implementation of GC-FOVE and tested on three benchmarks for lifted inference. A comparison with PITA and ProbLog2 shows the potential of the approach.

Lifted inference has been proposed for various probabilistic logical frameworks in order to compute the probability of queries in a time that depends on the size of the domains of the random variables rather than the number of instances. Even if various authors have underlined its importance for probabilistic logic programming (PLP), lifted inference has been applied up to now only to relational languages outside of logic programming. In this paper we adapt Generalized Counting First Order Variable Elimination (GC-FOVE) to the problem of computing the probability of queries to probabilistic logic programs under the distribution semantics. In particular, we extend the Prolog Factor Language (PFL) to include two new types of factors that are needed for representing ProbLog programs. These factors take into account the existing causal independence relationships among random variables and are managed by the extension to variable elimination proposed by Zhang and Poole for dealing with convergent variables and heterogeneous factors. Two new operators are added to GC-FOVE for treating heterogeneous factors. The resulting algorithm, called LP$^2$ for Lifted Probabilistic Logic Programming, has been implemented by modifying the PFL implementation of GC-FOVE and tested on three benchmarks for lifted inference. A comparison with PITA and ProbLog2 shows the potential of the approach.

Lifted inference has been proposed for various probabilistic logical frameworks in order to compute the probability of queries in a time that depends on the size of the domains of the random variables rather than the number of instances. Even if various authors have underlined its importance for probabilistic logic programming (PLP), lifted inference has been applied up to now only to relational languages outside of logic programming. In this paper we adapt Generalized Counting First Order Variable Elimination (GC-FOVE) to the problem of computing the probability of queries to probabilistic logic programs under the distribution semantics. In particular, we extend the Prolog Factor Language (PFL) to include two new types of factors that are needed for representing ProbLog programs. These factors take into account the existing causal independence relationships among random variables and are managed by the extension to variable elimination proposed by Zhang and Poole for dealing with convergent variables and heterogeneous factors. Two new operators are added to GC-FOVE for treating heterogeneous factors. The resulting algorithm, called LP$^2$ for Lifted Probabilistic Logic Programming, has been implemented by modifying the PFL implementation of GC-FOVE and tested on three benchmarks for lifted inference. A comparison with PITA and ProbLog2 shows the potential of the approach.