Programmers may be hesitant to use declarative systems, because of the associated learning curve. In this paper, we present an API that integrates the IDP Knowledge Base system into the Python programming language. IDP is a state-of-the-art logical system, which uses SAT, SMT, Logic Programming and Answer Set Programming technology. Python is currently one of the most widely used (teaching) languages for programming. The first goal of our API is to allow a Python programmer to use the declarative power of IDP, without needing to learn any new syntax or semantics. The second goal is allow IDP to be added to removed from an existing code base with minimal changes.
In @cite_5 , an approach is presented in which a constraint solver is not added to a single host language, but can be used in the development of a domain-specific language in Racket. Like ours, the motivation behind this work is to allow the power of declarative systems to be more widely used. However, their approach differs, because they count on an intermediary---the designer of the domain-specific language---to hide the complexity of the declarative system, whereas our approach focuses on creating an interface that is natural enough to offer KB functionality directly.
@cite_5
2018632979 2011124182 1234244774 2045196404
Declarative transfer from one domain to another can be observed in a systematic decrease in the time spent reading an instructional text and processing help during problem solving. Two experiments, done in the programming domain, tested the hypothesis that subjects introduced to a first programming language develop a representation of basic programming concepts that helps them integrate new declarative knowledge from a second programming language. This article shows that the effect on reading was greater for pages that were conceptually close across texts and for subjects who had fully mastered the basic concepts in the first language. A regression model of reading showed an effect on processes that are responsible for the analysis of novel words and examples, whereas general strategic reading processes remained unaffected. The increased reading speed was not accompanied by a greater understanding of the text. Effects of a common programming interface and transfer of procedural knowledge appeared to be negligible on the kind of problems considered. This study supports the distinction between procedural and declarative transfer. A novel logic program like language, weight constraint rules, is developed for answer set programming purposes. It generalizes normal logic programs by allowing weight constraints in place of literals to represent, e.g., cardinality and resource constraints and by providing optimization capabilities. A declarative semantics is developed which extends the stable model semantics of normal programs. The computational complexity of the language is shown to be similar to that of normal programs under the stable model semantics. A simple embedding of general weight constraint rules to a small subclass of the language called basic constraint rules is devised. An implementation of the language, the SMODELS system, is developed based on this embedding. It uses a two level architecture consisting of a front-end and a kernel language implementation. The front-end allows restricted use of variables and functions and compiles general weight constraint rules to basic constraint rules. A major part of the work is the development of an efficient search procedure for computing stable models for this kernel language. The procedure is compared with and empirically tested against satisfiability checkers and an implementation of the stable model semantics. It offers a competitive implementation of the stable model semantics for normal programs and attractive performance for problems where the new types of rules provide a compact representation. Declarative programming allows the expression of properties of the desired solution(s), while the computational task is delegated to a general-purpose algorithm. The freedom from explicit control is counter-balanced by the difficulty in working out what properties are missing or are incorrectly expressed, when the solutions do not meet expectations. This can be particularly problematic in the case of answer set semantics, because the absence of a key constraint rule could make the difference between none or thousands of answer sets, rather than the intended one (or handful). The debugging task then comprises adding or deleting conditions on the right hand sides of existing rules or, more far-reaching, adding or deleting whole rules. The contribution of this paper is to show how inductive logic programming (ILP) along with examples of (un)desirable properties of answer sets can be used to revise the original program semi-automatically so that it satisfies the stated properties, in effect providing debugging-by-example for programs under answer set semantics. There are a huge number of problems, from various areas, being solved by reducing them to SAT. However, for many applications, translation into SAT is performed by specialized, problem-specific tools. In this paper we describe a new system for uniform solving of a wide class of problems by reducing them to SAT. The system uses a new specification language URSA that combines imperative and declarative programming paradigms. The reduction to SAT is defined precisely by the semantics of the specification language. The domain of the approach is wide (e.g., many NP-complete problems can be simply specified and then solved by the system) and there are problems easily solvable by the proposed system, while they can be hardly solved by using other programming languages or constraint programming systems. So, the system can be seen not only as a tool for solving problems by reducing them to SAT, but also as a general-purpose constraint solving system (for finite domains). In this paper, we also describe an open-source implementation of the described approach. The performed experiments suggest that the system is competitive to state-of-the-art related modelling systems.