Logic programming and the execution model of Prolog

1995 ◽  
Vol 4 (3) ◽  
pp. 167-191
Author(s):  
Dan E. Tamir ◽  
Abe Kandel
2014 ◽  
Vol 886 ◽  
pp. 637-641 ◽  
Author(s):  
Qian Yu ◽  
Ying Lin ◽  
Xuan Zhang ◽  
Fei Dai ◽  
Na Zhao

Software evolution process model (EPM) is created in terms of a formal evolution process meta-model (EPMM) and semi-formal approach to modeling based on EPMM. EPM is still abstract at higher abstract level and is general while software process is concrete, so EPM must be instantiated before its enactment. The method to transform any EPM to its execution model based on logic programming is proposed. Since activity contains the imports resource, roles resource, exports resource and tasks, the rules to transform the four parts of activity level of any EPM to its execution model based logic programming are respectively proposed by analyzing the execution semantics of the activities and the tasks in EPM. The converter program is realized and the correct results have presented to prove the correctness of the method.


2014 ◽  
Vol 989-994 ◽  
pp. 2144-2147
Author(s):  
Qian Yu ◽  
Tong Li ◽  
Xuan Zhang ◽  
Ying Lin ◽  
Yong Yu ◽  
...  

Software evolution process model (EPM) is a knowledge-intensive process which is described in EPDL(Software Evolution Process Description Language) and modelled by semi-formal approach based on EPMM(Software Evolution Process Meta-Model). EPM’s execution model (EEM) is represented by logic programming to create the knowledge base of EPM during constructing the EEM. Only its needing all kinds of resources are satisfied by system execution environment is activity in EEM implemented. The paper discussed the method and the algorithm of preparing resources is respectively presented in order to execute the EEM automatically. The converter program is realized and the correct results have presented to prove the correctness of the method.


2003 ◽  
Vol 3 (6) ◽  
pp. 717-763 ◽  
Author(s):  
PETER VAN ROY ◽  
PER BRAND ◽  
DENYS DUCHIER ◽  
SEIF HARIDI ◽  
CHRISTIAN SCHULTE ◽  
...  

Oz is a multiparadigm language that supports logic programming as one of its major paradigms. A multiparadigm language is designed to support different programming paradigms (logic, functional, constraint, object-oriented, sequential, concurrent, etc.) with equal ease. This paper has two goals: to give a tutorial of logic programming in Oz; and to show how logic programming fits naturally into the wider context of multiparadigm programming. Our experience shows that there are two classes of problems, which we call algorithmic and search problems, for which logic programming can help formulate practical solutions. Algorithmic problems have known efficient algorithms. Search problems do not have known efficient algorithms but can be solved with search. The Oz support for logic programming targets these two problem classes specifically, using the concepts needed for each. This is in contrast to the Prolog approach, which targets both classes with one set of concepts, which results in less than optimal support for each class. We give examples that can be run interactively on the Mozart system, which implements Oz. To explain the essential difference between algorithmic and search programs, we define the Oz execution model. This model subsumes both concurrent logic programming (committed-choice-style) and search-based logic programming (Prolog-style). Furthermore, as consequences of its multiparadigm nature, the model supports new abilities such as first-class top levels, deep guards, active objects, and sophisticated control of the search process. Instead of Horn clause syntax, Oz has a simple, fully compositional, higher-order syntax that accommodates the abilities of the language. We give a brief history of Oz that traces the development of its main ideas and we summarize the lessons learned from this work. Finally, we give many entry points into the Oz literature.


2006 ◽  
Vol 6 (5) ◽  
pp. 483-507 ◽  
Author(s):  
NENG-FA ZHOU

In this paper, we propose a new language, called AR (Action Rules), and describe how various propagators for finite-domain constraints can be implemented in it. An action rule specifies a pattern for agents, an action that the agents can carry out, and an event pattern for events that can activate the agents. AR combines the goal-oriented execution model of logic programming with the event-driven execution model. This hybrid execution model facilitates programming constraint propagators. A propagator for a constraint is an agent that maintains the consistency of the constraint and is activated by the updates of the domain variables in the constraint. AR has a much stronger descriptive power than indexicals, the language widely used in the current finite-domain constraint systems, and is flexible for implementing not only interval-consistency but also arc-consistency algorithms. As examples, we present a weak arc-consistency propagator for the all_distinct constraint and a hybrid algorithm for n-ary linear equality constraints. B-Prolog has been extended to accommodate action rules. Benchmarking shows that B-Prolog as a CLP(FD) system significantly outperforms other CLP(FD) systems.


2014 ◽  
Vol 24 (10) ◽  
pp. 2432-2459
Author(s):  
Yan-Ning DU ◽  
Yin-Liang ZHAO ◽  
Bo HAN ◽  
Yuan-Cheng LI

2009 ◽  
Vol 20 (9) ◽  
pp. 2495-2510 ◽  
Author(s):  
Zhi-Gang CHEN ◽  
Jin-Song GUI ◽  
Ying GUO

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