Declaratively solving Google Code Jam problems with Picat

In this paper we present several examples of solving algorithmic problems from the Google Code Jam programming contest with Picat programming language using declarative techniques: constraint logic programming and tabled logic programming. In some cases the use of Picat simplifies the implementation compared to conventional imperative programming languages, while in others it allows to directly convert the problem statement into an efficiently solvable declarative problem specification without inventing an imperative algorithm

Planning as Tabled Logic Programming

This paper describes Picat's planner, its implementation, and planning models for several domains used in International Planning Competition (IPC) 2014. Picat's planner is implemented by use of tabling. During search, every state encountered is tabled, and tabled states are used to effectively perform resource-bounded search. In Picat, structured data can be used to avoid enumerating all possible permutations of objects, and term sharing is used to avoid duplication of common state data. This paper presents several modeling techniques through the example models, ranging from designing state representations to facilitate data sharing and symmetry breaking, encoding actions with operations for efficient precondition checking and state updating, to incorporating domain knowledge and heuristics. Broadly, this paper demonstrates the effectiveness of tabled logic programming for planning, and argues the importance of modeling despite recent significant progress in domain-independent PDDL planners.Comment: 27 pages in TPLP 201

Tabling with Sound Answer Subsumption

Tabling is a powerful resolution mechanism for logic programs that captures their least fixed point semantics more faithfully than plain Prolog. In many tabling applications, we are not interested in the set of all answers to a goal, but only require an aggregation of those answers. Several works have studied efficient techniques, such as lattice-based answer subsumption and mode-directed tabling, to do so for various forms of aggregation. While much attention has been paid to expressivity and efficient implementation of the different approaches, soundness has not been considered. This paper shows that the different implementations indeed fail to produce least fixed points for some programs. As a remedy, we provide a formal framework that generalises the existing approaches and we establish a soundness criterion that explains for which programs the approach is sound. This article is under consideration for acceptance in TPLP.Comment: Paper presented at the 32nd International Conference on Logic Programming (ICLP 2016), New York City, USA, 16-21 October 2016, 15 pages, LaTeX, 0 PDF figure

Mode-Directed Tabling and Applications in the YapTab System

Tabling is an implementation technique that solves some limitations of Prolog\u27s operational semantics in dealing with recursion and redundant sub-computations. Tabling works by memorizing generated answers and then by reusing them on similar calls that appear during the resolution process. In a traditional tabling system, all the arguments of a tabled subgoal call are considered when storing answers into the table space. Traditional tabling systems are thus very good for problems that require finding all answers. Mode-directed tabling is an extension to the tabling technique that supports the definition of selective criteria for specifying how answers are inserted into the table space. Implementations of mode-directed tabling are already available in systems like ALS-Prolog, B-Prolog and XSB. In this paper, we propose a more general approach to the declaration and use of mode-directed tabling, implemented on top of the YapTab tabling system, and we show applications of our approach to problems involving Justification, Preferences and Answer Subsumption