数据记录vs CLIPS vs Prolog [英] Datalog vs CLIPS vs Prolog

问题描述

与许多程序员一样，我在大学学习过Prolog，但很少.我了解Prolog和Datalog紧密相关，但是Datalog更简单吗?另外，我相信我读到Datalog不依赖于逻辑子句的顺序，但是我不确定为什么这是优点. CLIPS完全不同，但是对我来说太微妙了.有人可以提供其他语言的一般亮点吗?

解决方案

datalog是prolog的子集.数据日志携带的子集有两点注意:

1. 采用支持规则和查询的API
2. 确保所有查询终止

序言已完成图灵.数据日志不是.

摆脱数据记录的障碍，让我们看看prolog与片段的比较.

prolog的专长是问题解决"，而clips是专家系统".如果我理解正确，问题解决"涉及使用代码和数据的专业知识. 专家系统"通常使用数据结构来表达专业知识.参见 http://en.wikipedia.org/wiki/Expert_system#Comparison_to_problem-solving_systems

另一种查看方式是:

专家系统的运行前提是大多数(如果不是全部)结果都是已知的.所有这些结果都被汇编成数据，然后输入到专家系统中.给专家系统一个场景，专家系统从编译的数据(即知识库)计算结果.总是偶数加偶数总是偶数"的想法.

问题解决系统对问题的看法不完整.因此，一开始是对数据和行为进行建模，其中将包括知识库(这使术语拐角案例"公义)，最后以如果我们将2加到6，则最终得到8.可被8整除".两个?那么甚至是"

As many programmers I studied Prolog in university, but only very little. I understand that Prolog and Datalog are closely related, but Datalog is simpler? Also, I believe that I read that Datalog does not depend on ordering of the logic clauses, but I am not sure why this is advantages. CLIPS is supposedly altogether different, but it is too subtle for me to understand. Can someone please to provide a general highlights of the languages over the other languages?

解决方案

datalog is a subset of prolog. the subset which datalog carries has two things in mind:

1. adopt an API which would support rules and queries
2. make sure all queries terminate

prolog is Turing complete. datalog is not.

getting datalog out of the way, let's see how prolog compares with clips.

prolog's expertise is "problem solving" while clips is an "expert system". if i understand correctly, "problem solving" involves expertise using code and data. "expert systems" mostly use data structures to express expertise. see http://en.wikipedia.org/wiki/Expert_system#Comparison_to_problem-solving_systems

another way to look at it is:

expert systems operate on the premise that most (if not all) outcomes are known. all of these outcomes are compiled into data and then is fed into an expert system. give the expert system a scenario, the expert system computes the outcome from the compiled data, aka knowledge base. it's always a "an even number plus an even number is always even" kind of thinking.

problem solving systems have an incomplete view of the problem. so one starts out with modeling data and behavior, which would comprise the knowledge base (this gives justice to the term "corner case") and ends up with "if we add two to six, we end up with eight. is eight divisible by two? then it is even"

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