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Intuitionistic logic programming provides the notion of embedded implication in rule bodies, which can be used to reason about a current database modified by the antecedent. This can be applied to a system that translates SQL to Datalog to solve SQL WITH queries, for which relations are locally defined and can therefore be understood as added to the current database. In addition, assumptions in SQL queries as either adding or removing data can be modelled in this way as well, which is an interesting feature for decision-support scenarios. This work suggests a way to apply intuitionistic logic programming to SQL, and provides a pointer to a working system implementing this idea.

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.

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.

Extensional higher-order logic programming has been introduced as a generalization of classical logic programming. An important characteristic of this paradigm is that it preserves all the well-known properties of traditional logic programming. In this paper we consider the semantics of negation in the context of the new paradigm. Using some recent results from non-monotonic fixed-point theory, we demonstrate that every higher-order logic program with negation has a unique minimum infinite-valued model. In this way we obtain the first purely model-theoretic semantics for negation in extensional higher-order logic programming. Using our approach, we resolve an old paradox that was introduced by W. W. Wadge in order to demonstrate the semantic difficulties of higher-order logic programming.

Extensional higher-order logic programming has been introduced as a generalization of classical logic programming. An important characteristic of this paradigm is that it preserves all the well-known properties of traditional logic programming. In this paper we consider the semantics of negation in the context of the new paradigm. Using some recent results from non-monotonic fixed-point theory, we demonstrate that every higher-order logic program with negation has a unique minimum infinite-valued model. In this way we obtain the first purely model-theoretic semantics for negation in extensional higher-order logic programming. Using our approach, we resolve an old paradox that was introduced by W. W. Wadge in order to demonstrate the semantic difficulties of higher-order logic programming.

Extensional higher-order logic programming has been introduced as a generalization of classical logic programming. An important characteristic of this paradigm is that it preserves all the well-known properties of traditional logic programming. In this paper we consider the semantics of negation in the context of the new paradigm. Using some recent results from non-monotonic fixed-point theory, we demonstrate that every higher-order logic program with negation has a unique minimum infinite-valued model. In this way we obtain the first purely model-theoretic semantics for negation in extensional higher-order logic programming. Using our approach, we resolve an old paradox that was introduced by W. W. Wadge in order to demonstrate the semantic difficulties of higher-order logic programming.

Extensional higher-order logic programming has been introduced as a generalization of classical logic programming. An important characteristic of this paradigm is that it preserves all the well-known properties of traditional logic programming. In this paper we consider the semantics of negation in the context of the new paradigm. Using some recent results from non-monotonic fixed-point theory, we demonstrate that every higher-order logic program with negation has a unique minimum infinite-valued model. In this way we obtain the first purely model-theoretic semantics for negation in extensional higher-order logic programming. Using our approach, we resolve an old paradox that was introduced by W. W. Wadge in order to demonstrate the semantic difficulties of higher-order logic programming.

CLP(H) is an instantiation of the general constraint logic programming scheme with the constraint domain of hedges. Hedges are finite sequences of unranked terms, built over variadic function symbols and three kinds of variables: for terms, for hedges, and for function symbols. Constraints involve equations between unranked terms and atoms for regular hedge language membership. We study algebraic semantics of CLP(H) programs, define a sound, terminating, and incomplete constraint solver, investigate two fragments of constraints for which the solver returns a complete set of solutions, and describe classes of programs that generate such constraints.

CLP(H) is an instantiation of the general constraint logic programming scheme with the constraint domain of hedges. Hedges are finite sequences of unranked terms, built over variadic function symbols and three kinds of variables: for terms, for hedges, and for function symbols. Constraints involve equations between unranked terms and atoms for regular hedge language membership. We study algebraic semantics of CLP(H) programs, define a sound, terminating, and incomplete constraint solver, investigate two fragments of constraints for which the solver returns a complete set of solutions, and describe classes of programs that generate such constraints.

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).

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).

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).

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).

logic with random max notes programming

Includes index

This final report presents a summary of research accomplished to investigate parallel problem solving. Under the current grant a parallel problem solving system, PRISM (Parallel Inference System), that was implemented on the VAX/11-780, the PYRAMID and SUN machines, was ported successfully to McMOB and then to the BBN Butterfly parallel architecture. The McMOB architecture is essentially the ZMOB architecture with 16 Motorola 68000 processors, upgrading the Z80A microprocessors, interconnected in a ring structure. Experimental testing of PRISM on McMOB was undertaken in the current year. In addition, several enhancements were made to PRISM to permit experimental analyses to be made, and to incorporate additional features to take full advantage of parallelism in a problem solving environment. The tracing and statistical gathering packages were extended. An ability to display AND-parallelism was added to the trace program which displays the execution of a program on the parallel machines. In addition to the above, work continued in the area of informative answers to be presented to a user. Heuristic techniques were developed to determine which information to display. Keywords: Splicing compilers, Debugging software, Artificial intelligence.

This grant was funded under the Syracuse University Research Instrumentation program to purchase equipment in support of research in advanced logic programming and its application to artificial intelligence, especially extensions and enhancements to logic programming which include metalevel programming capabilities and concurrent execution. Equipment obtained under this grant included two Sun workstations, one Motorola C3I workstation testbed, and three Xenologic Prolog Accelerator boards. This equipment has led completion of research in theoretical capabilities of metaProlog and enabled the design and implementation of metaProlog and to carry out moderate scale knowledge base maintenance experiments. Keywords: Interfaces, Expert systems.

This grant was funded under the Syracuse University Research Instrumentation program to purchase equipment in support of research in advanced logic programming and its application to artificial intelligence, especially extensions and enhancements to logic programming which include metalevel programming capabilities and concurrent execution. Equipment obtained under this grant included two Sun workstations, one Motorola C3I workstation testbed, and three Xenologic Prolog Accelerator boards. This equipment has led completion of research in theoretical capabilities of metaProlog and enabled the design and implementation of metaProlog and to carry out moderate scale knowledge base maintenance experiments. Keywords: Interfaces, Expert systems.

The Journal of Logic and Algebraic Programming 1999: Volume 41 , Issue Index. Digitized from IA1652909-05 . Previous issue: sim_journal-of-logic-and-algebraic-programming_august-september-1999_40_2-3 . Next issue: sim_journal-of-logic-and-algebraic-programming_1999_41_contents . Note: This issue has no cover

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.

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 .

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 .

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

FASILL (acronym of "Fuzzy Aggregators and Similarity Into a Logic Language") is a fuzzy logic programming language with implicit/explicit truth degree annotations, a great variety of connectives and unification by similarity. FASILL integrates and extends features coming from MALP (Multi-Adjoint Logic Programming, a fuzzy logic language with explicitly annotated rules) and Bousi~Prolog (which uses a weak unification algorithm and is well suited for flexible query answering). Hence, it properly manages similarity and truth degrees in a single framework combining the expressive benefits of both languages. This paper presents the main features and implementations details of FASILL. Along the paper we describe its syntax and operational semantics and we give clues of the implementation of the lattice module and the similarity module, two of the main building blocks of the new programming environment which enriches the FLOPER system developed in our research group.

FASILL (acronym of "Fuzzy Aggregators and Similarity Into a Logic Language") is a fuzzy logic programming language with implicit/explicit truth degree annotations, a great variety of connectives and unification by similarity. FASILL integrates and extends features coming from MALP (Multi-Adjoint Logic Programming, a fuzzy logic language with explicitly annotated rules) and Bousi~Prolog (which uses a weak unification algorithm and is well suited for flexible query answering). Hence, it properly manages similarity and truth degrees in a single framework combining the expressive benefits of both languages. This paper presents the main features and implementations details of FASILL. Along the paper we describe its syntax and operational semantics and we give clues of the implementation of the lattice module and the similarity module, two of the main building blocks of the new programming environment which enriches the FLOPER system developed in our research group.

FASILL (acronym of "Fuzzy Aggregators and Similarity Into a Logic Language") is a fuzzy logic programming language with implicit/explicit truth degree annotations, a great variety of connectives and unification by similarity. FASILL integrates and extends features coming from MALP (Multi-Adjoint Logic Programming, a fuzzy logic language with explicitly annotated rules) and Bousi~Prolog (which uses a weak unification algorithm and is well suited for flexible query answering). Hence, it properly manages similarity and truth degrees in a single framework combining the expressive benefits of both languages. This paper presents the main features and implementations details of FASILL. Along the paper we describe its syntax and operational semantics and we give clues of the implementation of the lattice module and the similarity module, two of the main building blocks of the new programming environment which enriches the FLOPER system developed in our research group.

FASILL (acronym of "Fuzzy Aggregators and Similarity Into a Logic Language") is a fuzzy logic programming language with implicit/explicit truth degree annotations, a great variety of connectives and unification by similarity. FASILL integrates and extends features coming from MALP (Multi-Adjoint Logic Programming, a fuzzy logic language with explicitly annotated rules) and Bousi~Prolog (which uses a weak unification algorithm and is well suited for flexible query answering). Hence, it properly manages similarity and truth degrees in a single framework combining the expressive benefits of both languages. This paper presents the main features and implementations details of FASILL. Along the paper we describe its syntax and operational semantics and we give clues of the implementation of the lattice module and the similarity module, two of the main building blocks of the new programming environment which enriches the FLOPER system developed in our research group.

FASILL (acronym of "Fuzzy Aggregators and Similarity Into a Logic Language") is a fuzzy logic programming language with implicit/explicit truth degree annotations, a great variety of connectives and unification by similarity. FASILL integrates and extends features coming from MALP (Multi-Adjoint Logic Programming, a fuzzy logic language with explicitly annotated rules) and Bousi~Prolog (which uses a weak unification algorithm and is well suited for flexible query answering). Hence, it properly manages similarity and truth degrees in a single framework combining the expressive benefits of both languages. This paper presents the main features and implementations details of FASILL. Along the paper we describe its syntax and operational semantics and we give clues of the implementation of the lattice module and the similarity module, two of the main building blocks of the new programming environment which enriches the FLOPER system developed in our research group.

The Journal of Logic and Algebraic Programming 1998: Volume 37 , Issue CONTENTS. Digitized from IA1652909-05 . Previous issue: sim_journal-of-logic-and-algebraic-programming_1998_37_index . Next issue: sim_journal-of-logic-and-algebraic-programming_october-december-1998_37_1-3 . Note: This issue has no cover

Tandy/Radio Shack Book: Z80 Programming for Logic Design (1978)(Osborne and Associates)

Video content can be found here: https://www.youtube.com/channel/UC0BAd8tPlDqFvDYBemHcQPQ/

Video content can be found here: https://www.youtube.com/channel/UC0BAd8tPlDqFvDYBemHcQPQ/

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.

From the bitsavers.org collection, a scanned-in computer-related document. test equipment :: hp :: 166x :: 01660-90902 1660-Series 100-MHz State 500-MHz Timing Logic Analyzers Programming Guide Aug92

The Journal of Logic Programming 1995: Volume 24 , Issue CONTENTS. Digitized from IA1652909-07 . Previous issue: sim_journal-of-logic-and-algebraic-programming_1995_24_index . Next issue: sim_journal-of-logic-and-algebraic-programming_july-august-1995_24_1-2 .

The Journal of Logic and Algebraic Programming 1998: Volume 35 , Issue CONTENTS. Digitized from IA1652909-05 . Previous issue: sim_journal-of-logic-and-algebraic-programming_1998_35_index . Next issue: sim_journal-of-logic-and-algebraic-programming_1998-04_35_1 . Note: This issue has no cover

The Journal of Logic and Algebraic Programming 1998: Volume 35 , Issue CONTENTS. Digitized from IA1652909-05 . Previous issue: sim_journal-of-logic-and-algebraic-programming_1998_35_index . Next issue: sim_journal-of-logic-and-algebraic-programming_1998-04_35_1 . Note: This issue has no cover

The Journal of Logic and Algebraic Programming 1998: Volume 35 , Issue CONTENTS. Digitized from IA1652909-05 . Previous issue: sim_journal-of-logic-and-algebraic-programming_1998_35_index . Next issue: sim_journal-of-logic-and-algebraic-programming_1998-04_35_1 . Note: This issue has no cover

The Journal of Logic and Algebraic Programming 1998: Volume 35 , Issue CONTENTS. Digitized from IA1652909-05 . Previous issue: sim_journal-of-logic-and-algebraic-programming_1998_35_index . Next issue: sim_journal-of-logic-and-algebraic-programming_1998-04_35_1 . Note: This issue has no cover

The Journal of Logic and Algebraic Programming 1998: Volume 35 , Issue CONTENTS. Digitized from IA1652909-05 . Previous issue: sim_journal-of-logic-and-algebraic-programming_1998_35_index . Next issue: sim_journal-of-logic-and-algebraic-programming_1998-04_35_1 . Note: This issue has no cover

1v.(various pagings) ; 21cm

The Journal of Logic Programming 1992: Volume 12 , Issue Index. Digitized from IA1652909-07 . Previous issue: sim_journal-of-logic-and-algebraic-programming_october-november-1991_11_3-4 . Next issue: sim_journal-of-logic-and-algebraic-programming_1992_12_contents .

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 CONTENTS. Digitized from IA1652909-07 . Previous issue: sim_journal-of-logic-and-algebraic-programming_1995_25_index . Next issue: sim_journal-of-logic-and-algebraic-programming_1995-10_25_1 .

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 1996: Volume 27 , Issue Index. Digitized from IA1652909-05 . Previous issue: sim_journal-of-logic-and-algebraic-programming_1996-03_26_3 . Next issue: sim_journal-of-logic-and-algebraic-programming_1996_27_contents . Note: This issue has no cover

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