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Available from National Technical Information Service, Springfield, Va

We introduce a Three Tier Tree Calculus (3TC) that defines in a systematic way three tiers of tree structures underlying proof search in logic programming. We use 3TC to define a new -- structural -- version of resolution for logic programming.

The Journal of Logic and Algebraic Programming 1999: Volume 38 , Issue CONTENTS. Digitized from IA1652909-05 . Previous issue: sim_journal-of-logic-and-algebraic-programming_1999_38_index . Next issue: sim_journal-of-logic-and-algebraic-programming_1999-01_38_1 . Note: This issue has no cover

CoAlgebraic Logic Programming (CoALP) is a dialect of Logic Programming designed to bring a more precise compile-time and run-time analysis of termination and productivity for recursive and corecursive functions in Logic Programming. Its second goal is to introduce guarded lazy (co)recursion akin to functional theorem provers into logic programming. In this paper, we explain lazy features of CoALP, and compare them with the loop-analysis and eager execution in Coinductive Logic Programming (CoLP). We conclude by outlining the future directions in developing the guarded (co)recursion in logic programming.

This paper analyses the declarative readings of logic programming. Logic programming - and negation as failure - has no unique declarative reading. One common view is that logic programming is a logic for default reasoning, a sub-formalism of default logic or autoepistemic logic. In this view, negation as failure is a modal operator. In an alternative view, a logic program is interpreted as a definition. In this view, negation as failure is classical objective negation. From a commonsense point of view, there is definitely a difference between these views. Surprisingly though, both types of declarative readings lead to grosso modo the same model semantics. This note investigates the causes for this.

We present a declarative language, PP, for the high-level specification of preferences between possible solutions (or trajectories) of a planning problem. This novel language allows users to elegantly express non-trivial, multi-dimensional preferences and priorities over such preferences. The semantics of PP allows the identification of most preferred trajectories for a given goal. We also provide an answer set programming implementation of planning problems with PP preferences.

FLUX is a programming method for the design of agents that reason logically about their actions and sensor information in the presence of incomplete knowledge. The core of FLUX is a system of Constraint Handling Rules, which enables agents to maintain an internal model of their environment by which they control their own behavior. The general action representation formalism of the fluent calculus provides the formal semantics for the constraint solver. FLUX exhibits excellent computational behavior due to both a carefully restricted expressiveness and the inference paradigm of progression.

We have been developing a system for recognising human activity given a symbolic representation of video content. The input of our system is a set of time-stamped short-term activities detected on video frames. The output of our system is a set of recognised long-term activities, which are pre-defined temporal combinations of short-term activities. The constraints on the short-term activities that, if satisfied, lead to the recognition of a long-term activity, are expressed using a dialect of the Event Calculus. We illustrate the expressiveness of the dialect by showing the representation of several typical complex activities. Furthermore, we present a detailed evaluation of the system through experimentation on a benchmark dataset of surveillance videos.

In this paper we address an issue that has been brought to the attention of the database community with the advent of the Semantic Web, i.e. the issue of how ontologies (and semantics conveyed by them) can help solving typical database problems, through a better understanding of KR aspects related to databases. In particular, we investigate this issue from the ILP perspective by considering two database problems, (i) the definition of views and (ii) the definition of constraints, for a database whose schema is represented also by means of an ontology. Both can be reformulated as ILP problems and can benefit from the expressive and deductive power of the KR framework DL+log. We illustrate the application scenarios by means of examples. Keywords: Inductive Logic Programming, Relational Databases, Ontologies, Description Logics, Hybrid Knowledge Representation and Reasoning Systems. Note: To appear in Theory and Practice of Logic Programming (TPLP).

We propose a static and a dynamic approach to model biological signaling networks, and show how each can be used to answer relevant biological questions. For this we use the two different mathematical tools of Propositional Logic and Integer Programming. The power of discrete mathematics for handling qualitative as well as quantitative data has so far not been exploited in Molecular Biology, which is mostly driven by experimental research, relying on first-order or statistical models. The arising logic statements and integer programs are analyzed and can be solved with standard software. For a restricted class of problems the logic models reduce to a polynomial-time solvable satisfiability algorithm. Additionally, a more dynamic model enables enumeration of possible time resolutions in poly-logarithmic time. Computational experiments are included.

Язык программирования Форт (англ. forth вперед и одновременное сокращение от fourth четвертый), который лежит в основе языка ForthLogicTM, появился в начале 1970-х гг. в США. Его изобретатель Чарльз Мур сначала применил его для разработки программного обеспечения микро-ЭВМ, которая управляла работой радиотелескопа. Преимущества работы с языком Форт б

The Journal of Logic Programming 1991: Volume 10 , Issue Index. Digitized from IA1652909-07 . Previous issue: sim_journal-of-logic-and-algebraic-programming_september-november-1990_9_2-3 . Next issue: sim_journal-of-logic-and-algebraic-programming_1991_10_contents .

The Journal of Logic Programming 1991: Volume 10 , Issue Index. Digitized from IA1652909-07 . Previous issue: sim_journal-of-logic-and-algebraic-programming_september-november-1990_9_2-3 . Next issue: sim_journal-of-logic-and-algebraic-programming_1991_10_contents .

sucking up to music promoters with willin turing hardliners

Two different paradoxes of the fuzzy logic programming system of [29] are presented. The first paradox is due to two distinct (contradictory) truth values for every ground atom of FLP, one is syntactical, the other is semantical. The second paradox concerns the cardinality of the valid FLP formulas which is found to have contradictory values: both $\aleph_0$ the cardinality of the natural numbers, and $c$, the cardinality of the continuum. The result is that CH="False" and Axiom of Choice="False". Hence, ZFC is inconsistent.

Two different paradoxes of the fuzzy logic programming system of [29] are presented. The first paradox is due to two distinct (contradictory) truth values for every ground atom of FLP, one is syntactical, the other is semantical. The second paradox concerns the cardinality of the valid FLP formulas which is found to have contradictory values: both $\aleph_0$ the cardinality of the natural numbers, and $c$, the cardinality of the continuum. The result is that CH="False" and Axiom of Choice="False". Hence, ZFC is inconsistent.

Two different paradoxes of the fuzzy logic programming system of [29] are presented. The first paradox is due to two distinct (contradictory) truth values for every ground atom of FLP, one is syntactical, the other is semantical. The second paradox concerns the cardinality of the valid FLP formulas which is found to have contradictory values: both $\aleph_0$ the cardinality of the natural numbers, and $c$, the cardinality of the continuum. The result is that CH="False" and Axiom of Choice="False". Hence, ZFC is inconsistent.

Book by V.E. Wolfengagen "Combinatory Logic in Programming. Computations with Objects Through Examples and Exercises". 2-nd ed. -- Moscow, Center JurInfoR, 2003. X+336 p. The book is intended for computer science students, programmers and professionals who have already got acquainted with the basic courses and background on discrete mathematics. It may be used as a textbook for graduate course on theoretical computer science. The book introduces a reader to the conceptual framework for thinking about computations with the objects. The several areas of theoretical computer science are covered, including the following: type free and typed lambda-calculus and combinatory logic with applications, evaluation of expressions, computations in a category. The topics, covered in the book accumulated much experience in teaching these subjects in graduate computer science courses. A rich set of examples and exercises, including solutions, has been prepared to stimulate the self studying and to make easier the job of instructor.

This document describes the contributions of the 2016 Applications of Logic Programming Workshop (AppLP), which was held on October 17 and associated with the International Conference on Logic Programming (ICLP) in Flushing, New York City.

xvi, 367 pages : 24 cm

Techniques derived from mathematical logic promise to provide an alternative to the conventional methodology for constructing, debugging, and optimizing computer programs. Ultimately, these techniques are intended to lead to the automation of many of the facets of the programming process. This paper provides a unified tutorial exposition of the logical techniques, illustrating each with examples. The strengths and limitations of each technique as a practical programming aid are assessed and attempts to implement these methods in experimental systems are discussed. (Author)

Techniques derived from mathematical logic promise to provide an alternative to the conventional methodology for constructing, debugging, and optimizing computer programs. Ultimately, these techniques are intended to lead to the automation of many of the facets of the programming process. This paper provides a unified tutorial exposition of the logical techniques, illustrating each with examples. The strengths and limitations of each technique as a practical programming aid are assessed and attempts to implement these methods in experimental systems are discussed. (Author)

Partial functions are common abstractions in formal specification notations such as Z, B and Alloy. Conversely, executable programming languages usually provide little or no support for them. In this paper we propose to add partial functions as a primitive feature to a Constraint Logic Programming (CLP) language, namely {log}. Although partial functions could be programmed on top of {log}, providing them as first-class citizens adds valuable flexibility and generality to the form of set-theoretic formulas that the language can safely deal with. In particular, the paper shows how the {log} constraint solver is naturally extended in order to accommodate for the new primitive constraints dealing with partial functions. Efficiency of the new version is empirically assessed by running a number of non-trivial set-theoretical goals involving partial functions, obtained from specifications written in Z.

Logic programming with fixed-point definitions is a useful extension of traditional logic programming. Fixed-point definitions can capture simple model checking problems and closed-world assumptions. Its operational semantics is typically based on intuitionistic provability. We extend the operational semantics of these languages with game semantics. This extended semantics has several interesting aspects: in particular, it gives a logical status to the $read$ predicate.

In order to design and implement tracers, one must decide what exactly to trace and how to produce this trace. On the one hand, trace designs are too often guided by implementation concerns and are not as useful as they should be. On the other hand, an interesting trace which cannot be produced efficiently, is not very useful either. In this article we propose a methodology which helps to efficiently produce accurate traces. Firstly, design a formal specification of the trace model. Secondly, derive a prototype tracer from this specification. Thirdly, analyze the produced traces. Fourthly, implement an efficient tracer. Lastly, compare the traces of the two tracers. At each step, problems can be found. In that case one has to iterate the process. We have successfully applied the proposed methodology to the design and implementation of a real tracer for constraint logic programming which is able to efficiently generate information required to build interesting graphical views of executions.

Constraint logic grammars provide a powerful formalism for expressing complex logical descriptions of natural language phenomena in exact terms. Describing some of these phenomena may, however, require some form of graded distinctions which are not provided by such grammars. Recent approaches to weighted constraint logic grammars attempt to address this issue by adding numerical calculation schemata to the deduction scheme of the underlying CLP framework. Currently, these extralogical extensions are not related to the model-theoretic counterpart of the operational semantics of CLP, i.e., they do not come with a formal semantics at all. The aim of this paper is to present a clear formal semantics for weighted constraint logic grammars, which abstracts away from specific interpretations of weights, but nevertheless gives insights into the parsing problem for such weighted grammars. Building on the formalization of constraint logic grammars in the CLP scheme of Hoehfeld and Smolka 1988, this formal semantics will be given by a quantitative version of CLP. Such a quantitative CLP scheme can also be valuable for CLP tasks independent of grammars.

The Journal of Logic Programming 1986: Volume 3 , Issue Index. Digitized from IA1652909-07 . Previous issue: sim_journal-of-logic-and-algebraic-programming_1985-12_2_4 . Next issue: sim_journal-of-logic-and-algebraic-programming_1986_3_contents .

The Journal of Logic Programming 1986: Volume 3 , Issue Index. Digitized from IA1652909-07 . Previous issue: sim_journal-of-logic-and-algebraic-programming_1985-12_2_4 . Next issue: sim_journal-of-logic-and-algebraic-programming_1986_3_contents .

The Journal of Logic Programming 1986: Volume 3 , Issue Index. Digitized from IA1652909-07 . Previous issue: sim_journal-of-logic-and-algebraic-programming_1985-12_2_4 . Next issue: sim_journal-of-logic-and-algebraic-programming_1986_3_contents .

Extensible Markup Language (XML) is a simple, very flexible text format derived from SGML. Originally designed to meet the challenges of large-scale electronic publishing, XML is also playing an increasingly important role in the exchange of a wide variety of data on the Web and elsewhere. XPath language is the result of an effort to provide address parts of an XML document. In support of this primary purpose, it becomes in a query language against an XML document. In this paper we present a proposal for the implementation of the XPath language in logic programming. With this aim we will describe the representation of XML documents by means of a logic program. Rules and facts can be used for representing the document schema and the XML document itself. In particular, we will present how to index XML documents in logic programs: rules are supposed to be stored in main memory, however facts are stored in secondary memory by using two kind of indexes: one for each XML tag, and other for each group of terminal items. In addition, we will study how to query by means of the XPath language against a logic program representing an XML document. It evolves the specialization of the logic program with regard to the XPath expression. Finally, we will also explain how to combine the indexing and the top-down evaluation of the logic program. To appear in Theory and Practice of Logic Programming (TPLP)"

Logic Programming can be broadly defined as "using logic to infer computational steps from existing propositions" However, mathematical logic cannot always infer computational steps because computational systems make use of arbitration for determining which message is processed next by a recipient that is sent multiple messages concurrently. Since arrival orders are in general indeterminate, they cannot be inferred from prior information by mathematical logic alone. Therefore mathematical logic cannot in general implement computation. Consequently, Procedural Embedding of Knowledge is strictly more general than Logic Programming. This conclusion is contrary to Robert Kowalski who stated "Looking back on our early discoveries, I value most the discovery that computation could be subsumed by deduction." Nevertheless, logic programming (like functional programming) can be a useful programming idiom. Over the course of history, the term "functional programming" has grown more precise and technical as the field has matured. Logic Programming should be on a similar trajectory. Accordingly, "Logic Programming" should have a general precise characterization. Kowalski's approach has been to advocate limiting Logic Programming to backward-chaining only inference based on resolution using reduction to conjunctive normal form in a global states model. In contrast, our approach is explore Logic Programming building on the logical inference of computational steps using inconsistency-robust reasoning in a configurations model. Because contemporary large software systems are pervasively inconsistent, it is not safe to reason about them using classical logic, e.g., using resolution theorem proving.

Logic Programming can be broadly defined as "using logic to infer computational steps from existing propositions" However, mathematical logic cannot always infer computational steps because computational systems make use of arbitration for determining which message is processed next by a recipient that is sent multiple messages concurrently. Since arrival orders are in general indeterminate, they cannot be inferred from prior information by mathematical logic alone. Therefore mathematical logic cannot in general implement computation. Consequently, Procedural Embedding of Knowledge is strictly more general than Logic Programming. This conclusion is contrary to Robert Kowalski who stated "Looking back on our early discoveries, I value most the discovery that computation could be subsumed by deduction." Nevertheless, logic programming (like functional programming) can be a useful programming idiom. Over the course of history, the term "functional programming" has grown more precise and technical as the field has matured. Logic Programming should be on a similar trajectory. Accordingly, "Logic Programming" should have a general precise characterization. Kowalski's approach has been to advocate limiting Logic Programming to backward-chaining only inference based on resolution using reduction to conjunctive normal form in a global states model. In contrast, our approach is explore Logic Programming building on the logical inference of computational steps using inconsistency-robust reasoning in a configurations model. Because contemporary large software systems are pervasively inconsistent, it is not safe to reason about them using classical logic, e.g., using resolution theorem proving.

The sixteenth lecture of twenty from MIT's Structure and Interpretation of Computer Programs course.

The past few years have seen a surge of interest in the field of probabilistic logic learning and statistical relational learning. In this endeavor, many probabilistic logics have been developed. ProbLog is a recent probabilistic extension of Prolog motivated by the mining of large biological networks. In ProbLog, facts can be labeled with probabilities. These facts are treated as mutually independent random variables that indicate whether these facts belong to a randomly sampled program. Different kinds of queries can be posed to ProbLog programs. We introduce algorithms that allow the efficient execution of these queries, discuss their implementation on top of the YAP-Prolog system, and evaluate their performance in the context of large networks of biological entities.

Z80 Programming for Logic Design 1978 - Osborne

The work reported here introduces Defeasible Logic Programming (DeLP), a formalism that combines results of Logic Programming and Defeasible Argumentation. DeLP provides the possibility of representing information in the form of weak rules in a declarative manner, and a defeasible argumentation inference mechanism for warranting the entailed conclusions. In DeLP an argumentation formalism will be used for deciding between contradictory goals. Queries will be supported by arguments that could be defeated by other arguments. A query q will succeed when there is an argument A for q that is warranted, ie, the argument A that supports q is found undefeated by a warrant procedure that implements a dialectical analysis. The defeasible argumentation basis of DeLP allows to build applications that deal with incomplete and contradictory information in dynamic domains. Thus, the resulting approach is suitable for representing agent's knowledge and for providing an argumentation based reasoning mechanism to agents.

We propose a new formal language for the expressive representation of probabilistic knowledge based on Answer Set Programming (ASP). It allows for the annotation of first-order formulas as well as ASP rules and facts with probabilities and for learning of such weights from data (parameter estimation). Weighted formulas are given a semantics in terms of soft and hard constraints which determine a probability distribution over answer sets. In contrast to related approaches, we approach inference by optionally utilizing so-called streamlining XOR constraints, in order to reduce the number of computed answer sets. Our approach is prototypically implemented. Examples illustrate the introduced concepts and point at issues and topics for future research.

Tabling has been used for some time to improve efficiency of Prolog programs by memorizing answered queries. The same idea can be naturally used to memorize visited states during search for planning. In this paper we present a planner developed in the Picat language to solve the Petrobras planning problem. Picat is a novel Prolog-like language that provides pattern matching, deterministic and non-deterministic rules, and tabling as its core modelling and solving features. We demonstrate these capabilities using the Petrobras problem, where the goal is to plan transport of cargo items from ports to platforms using vessels with limited capacity. Monte Carlo Tree Search has been so far the best technique to tackle this problem and we will show that by using tabling we can achieve much better runtime efficiency and better plan quality.

Since the seminal work of J. A. Robinson on resolution, many lifting lemmas for simplifying proofs of completeness of resolution have been proposed in the literature. In the logic programming framework, they may also help to detect some infinite derivations while proving goals under the SLD-resolution. In this paper, we first generalize a version of the lifting lemma, by extending the relation "is more general than" so that it takes into account only some arguments of the atoms. The other arguments, which we call neutral arguments, are disregarded. Then we propose two syntactic conditions of increasing power for identifying neutral arguments from mere inspection of the text of a logic program.

Defeasible reasoning is a simple but efficient approach to nonmonotonic reasoning that has recently attracted considerable interest and that has found various applications. Defeasible logic and its variants are an important family of defeasible reasoning methods. So far no relationship has been established between defeasible logic and mainstream nonmonotonic reasoning approaches. In this paper we establish close links to known semantics of logic programs. In particular, we give a translation of a defeasible theory D into a meta-program P(D). We show that under a condition of decisiveness, the defeasible consequences of D correspond exactly to the sceptical conclusions of P(D) under the stable model semantics. Without decisiveness, the result holds only in one direction (all defeasible consequences of D are included in all stable models of P(D)). If we wish a complete embedding for the general case, we need to use the Kunen semantics of P(D), instead.

Defeasible reasoning is a simple but efficient approach to nonmonotonic reasoning that has recently attracted considerable interest and that has found various applications. Defeasible logic and its variants are an important family of defeasible reasoning methods. So far no relationship has been established between defeasible logic and mainstream nonmonotonic reasoning approaches. In this paper we establish close links to known semantics of logic programs. In particular, we give a translation of a defeasible theory D into a meta-program P(D). We show that under a condition of decisiveness, the defeasible consequences of D correspond exactly to the sceptical conclusions of P(D) under the stable model semantics. Without decisiveness, the result holds only in one direction (all defeasible consequences of D are included in all stable models of P(D)). If we wish a complete embedding for the general case, we need to use the Kunen semantics of P(D), instead.

Defeasible reasoning is a simple but efficient approach to nonmonotonic reasoning that has recently attracted considerable interest and that has found various applications. Defeasible logic and its variants are an important family of defeasible reasoning methods. So far no relationship has been established between defeasible logic and mainstream nonmonotonic reasoning approaches. In this paper we establish close links to known semantics of logic programs. In particular, we give a translation of a defeasible theory D into a meta-program P(D). We show that under a condition of decisiveness, the defeasible consequences of D correspond exactly to the sceptical conclusions of P(D) under the stable model semantics. Without decisiveness, the result holds only in one direction (all defeasible consequences of D are included in all stable models of P(D)). If we wish a complete embedding for the general case, we need to use the Kunen semantics of P(D), instead.

Defeasible reasoning is a simple but efficient approach to nonmonotonic reasoning that has recently attracted considerable interest and that has found various applications. Defeasible logic and its variants are an important family of defeasible reasoning methods. So far no relationship has been established between defeasible logic and mainstream nonmonotonic reasoning approaches. In this paper we establish close links to known semantics of logic programs. In particular, we give a translation of a defeasible theory D into a meta-program P(D). We show that under a condition of decisiveness, the defeasible consequences of D correspond exactly to the sceptical conclusions of P(D) under the stable model semantics. Without decisiveness, the result holds only in one direction (all defeasible consequences of D are included in all stable models of P(D)). If we wish a complete embedding for the general case, we need to use the Kunen semantics of P(D), instead.

Defeasible reasoning is a simple but efficient approach to nonmonotonic reasoning that has recently attracted considerable interest and that has found various applications. Defeasible logic and its variants are an important family of defeasible reasoning methods. So far no relationship has been established between defeasible logic and mainstream nonmonotonic reasoning approaches. In this paper we establish close links to known semantics of logic programs. In particular, we give a translation of a defeasible theory D into a meta-program P(D). We show that under a condition of decisiveness, the defeasible consequences of D correspond exactly to the sceptical conclusions of P(D) under the stable model semantics. Without decisiveness, the result holds only in one direction (all defeasible consequences of D are included in all stable models of P(D)). If we wish a complete embedding for the general case, we need to use the Kunen semantics of P(D), instead.

In designing the interface between a database and a logic system with inference such as Prolog, efficiency is the major issue. Presented here are three of the methods that are considered most promising and in which much research is focused. The first method explores extending an inference machine to include a database manager, the second couples the inference mechanism with a database management system; and the third extends a database management mechanism to include inference. Acknowledging up front that no method can be claimed best, the major emphasis of this study will be to determine the strengths and limitations of all three methods and thereby help to clarify many uncertain and sometimes conflicting issues caused by the parallel lines of development from the database and artificial intelligence communities.

In designing the interface between a database and a logic system with inference such as Prolog, efficiency is the major issue. Presented here are three of the methods that are considered most promising and in which much research is focused. The first method explores extending an inference machine to include a database manager, the second couples the inference mechanism with a database management system; and the third extends a database management mechanism to include inference. Acknowledging up front that no method can be claimed best, the major emphasis of this study will be to determine the strengths and limitations of all three methods and thereby help to clarify many uncertain and sometimes conflicting issues caused by the parallel lines of development from the database and artificial intelligence communities.

The Journal of Logic Programming 1989: Volume 6 , Issue CONTENTS. Digitized from IA1652909-07 . Previous issue: sim_journal-of-logic-and-algebraic-programming_1989_6_index . Next issue: sim_journal-of-logic-and-algebraic-programming_january-march-1989_6_1-2 .

The Journal of Logic Programming 1995: Volume 22 , Issue CONTENTS. Digitized from IA1652909-07 . Previous issue: sim_journal-of-logic-and-algebraic-programming_1995_22_index . Next issue: sim_journal-of-logic-and-algebraic-programming_1995-01_22_1 .