AGS TechBlog
A source and journal of ideas

Check out the beginning of the new surveys page: http://www.agstechnet.com/surveys.html

I will be using the surveys to best focus on the AI tools, systems, and techniques that you think matter most.

This is intended to follow up on my previous post: http://www.agstechnet.com/blog/2007/11/29/the-really-big-list-of-ai-tools/

If you have thoughts or questions concerning a community feedback site for artificial intelligence tools and techniques, please send me a note.

-Stu 

A client recently asked me what I meant when I refered to “heuristic search.”  Heuristic search is performed when “brute force search” (or all-possible-paths-search) is computationally intractable.  Essentially, you can think of a heuristic as a “rule of thumb” or an approach to calculate an approximate answer.  Here’s a more detailed description…

A heuristic is defined by Meriam Webster’s on line dictionary as follows:

Main Entry: heu·ris·tic
Pronunciation: hyu-’ris-tik
Function: adjective
Etymology: German heuristisch, from New Latin heuristicus, from Greek heuriskein to discover; akin to Old Irish fo-fúair he found
: involving or serving as an aid to learning, discovery, or problem-solving by experimental and especially trial-and-error methods heuristic assumption>; also
: of or relating to exploratory problem-solving techniques that utilize self-educating techniques (as the evaluation of feedback) to improve performance heuristic computer program>

Heuristics are used in the context of high complexity to find good solutions to problems that might be impossible to solve for an exact or the globally optimum solution.

Examples of the use of heuristic search are found in many common optimization problems.  In many optimization efforts, computations can take billions of billions of years to find the exact solution even on the fastest computers. This is a case where using heuristics may allow you to find a good solution even if the optimal solution is unknown.

-Stu

Researchers at the National Institute of Standards and Technology (NIST) and the University of Texas, Arlington have released a variety of briefings, reports, and results concerning the automated combinatorial testing for software.   Many of the major news feeds were carrying the stories yesterday.

After reviewing their reports and findings, it sounds like good news for automated testing systems.

There never has been enough time or money to do all the testing that could or should be done for any non-trivial project.  But these results at least provide some support to the idea that a substantial majority of software defects can be effectively captured by automated testing with something less than full-exhaustive testing.  See the following link for the full article, the reports and the results: http://csrc.nist.gov/groups/SNS/acts/index.html

It is normal in the software industry to adopt approaches to testing that cover the most commonly used features and cases.  In my experience, you normally get the most bang for the buck by focusing on expected use cases. 

Still, these results were encouraging enough to motivate me to look into the new tool to be released to see how I like it.  I sent Rick Kuhn a note today to get some info.  I will post the results of my assessment of the tool when I am finished.

-Stu