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.

论文介绍了 Picat 计划器及其实现，以及在国际计划竞赛（IPC）2014 中使用的几个领域的规划模型，证明表格逻辑编程对计划的有效性，强调建模的重要性，并在示例模型中提出了几种建模技术，从设计状态表示以促进数据共享和对称性打破，用运算编码操作以进行有效的前置条件检查和状态更新，到整合领域知识和启发式算法。

表格逻辑编程中的规划

Planning as Tabled Logic Programming

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.

本研究介绍了在 Picat 语言中开发的计划程序，利用 Prolog 和 tabling 的核心建模和求解功能，以优化 Petrobras 方案的运输计划，通过使用 tabling 技术，我们可以实现更好的运行时效率和更好的计划质量。

使用表格逻辑编程解决 Petrobras 规划问题

Using Tabled Logic Programming to Solve the Petrobras Planning Problem

B-Prolog is a high-performance implementation of the standard Prolog language
with several extensions including matching clauses, action rules for event
handling, finite-domain constraint solving, arrays and hash tables, declarative
loop constructs, and tabling. The B-Prolog system is based on the TOAM
architecture which differs from the WAM mainly in that (1) arguments are passed
old-fashionedly through the stack, (2) only one frame is used for each
predicate call, and (3) instructions are provided for encoding matching trees.
The most recent architecture, called TOAM Jr., departs further from the WAM in
that it employs no registers for arguments or temporary variables, and provides
variable-size instructions for encoding predicate calls. This paper gives an
overview of the language features and a detailed description of the TOAM Jr.
architecture, including architectural support for action rules and tabling.

B-Prolog 语言实现了标准 Prolog 语言，并引入了匹配子句，有限域约束求解，循环结构等特性，同时使用了 TOAM 和 TOAM Jr. 体系结构，支持操作规则和表格

B-Prolog 的语言特征和架构

The Language Features and Architecture of B-Prolog

Yet Another Prolog (YAP) is a Prolog system originally developed in the
mid-eighties and that has been under almost constant development since then.
This paper presents the general structure and design of the YAP system,
focusing on three important contributions to the Logic Programming community.
First, it describes the main techniques used in YAP to achieve an efficient
Prolog engine. Second, most Logic Programming systems have a rather limited
indexing algorithm. YAP contributes to this area by providing a dynamic
indexing mechanism, or just-in-time indexer (JITI). Third, a important
contribution of the YAP system has been the integration of both or-parallelism
and tabling in a single Logic Programming system.

本文介绍了 Prolog 系统 YAP 的结构和设计，重点介绍了 YAP 为逻辑编程社区做出的三个重要贡献：实现高效的 Prolog 引擎、提供动态索引机制并在单个逻辑编程系统中集成或并行和 tabling。