aima-python: Structure of the Project

Python code for the book Artificial Intelligence: A Modern Approach. When complete, this project will cover all the major topics in the book, for each topic, such as logic, we will have the following Python 3.5 files:

• logic.py: Implementations of all the pseudocode algorithms in the book.
• logic_test.py: A lightweight test suite, using assert statements, designed for use with py.test.
• logic.ipynb: A Jupyter notebook, with examples of usage. Does a from logic import * to get the code.

Style Guide

There are a few style rules that are unique to this project:

• The first rule is that the code should correspond directly to the pseudocode in the book. When possible this will be almost one-to-one, just allowing for the syntactic differences between Python and pseudocode, and for different library functions.
• Don't make a function more complicated than the pseudocode in the book, even if the complication would add a nice feature, or give an efficiency gain. Instead, remain faithful to the pseudocode, and if you must, add a new function (not in the book) with the added feature.
• I use functional programming in many cases, but not exclusively; let the book's pseudocode be the guide. In general, pure functions (with no side effects) are prefered, but functions with side effects, and classes, can be used where appropriate.

Beyond the above rules, we default to Pep 8, with a few minor exceptions:

• I'm not too worried about an occasional line longer than 79 characters.

• You don't need two spaces after a sentence-ending period.

• Strunk and White is not a good guide for English.

• I prefer more concise docstrings; I don't follow Pep 257.

• Not all constants have to be UPPERCASE.

• Pep 484 type annotations are allowed but not required. If your parameter name is already suggestive of the name of a type, you don't need an annotation, e.g.:

    def retry(url: Url) -> None: # This 'Url' annotation should be avoided; but '-> None' is useful
  

Language Popularity

Are we right to concentrate on Java and Python versions of the code? What languages do students already know? The TIOBE Index says the top five are:

    Java, C, C++, C#, Python

What languages are instructors recommending for their AI class?
To get an approximate idea, I gave the query norvig russell "Modern Approach" along with the names of various languages and looked at the estimated counts of results on various dates. However, I don't have much confidence in these figures...

Language	2004	2005	2007	2010	2016
none	8,080	20,100	75,200	150,000	132,000
java	1,990	4,930	44,200	37,000	50,000
c++	875	1,820	35,300	105,000	35,000
lisp	844	974	30,100	19,000	14,000
prolog	789	2,010	23,200	17,000	16,000
python	785	1,240	18,400	11,000	12,000