This is a first demo of my chatbot called Dr. Stat. It can answer questions about statistics and help a user select an appropriate statistical technique.
I’ve been working on this for quite a while now. Chatbots certainly aren’t anything new, but chatbots that are useful and display a more ‘natural’ way of conversing are rare. I chose the domain of statistics for two reasons. Firstly, many of my students (and researchers I know) struggle with statistical concepts and I wanted to see if I could create something that would make their lives easier. Secondly, this domain has proven to be suitable for trying out different ideas about dialogue management.
One of my colleagues brought the following puzzle to work:
The puzzle is called Skyline and it’s a packing puzzle. The objective is to place the metal rod in one of the holes in the base and place the nine wooden pieces around it. It was designed by Jean Claude Constantin. When solved, the puzzle looks something like this:
Sometimes with these kinds of puzzles it’s quicker to write a program that finds a solution than trying to solve it by hand. Check out this github repository for a Prolog program that finds solutions for a given rod location.
To use this program open the file skyline.pl in your favorite Prolog interpreter (e.g. SWI-Prolog) and execute the following:
You can press ; to find alternative solutions. The pos(X,Y) part refers to the location of the metal rod.
I mainly wrote this to get some practice with some of the new C++11 features such as variadic templates and lambda functions. It uses template metaprogramming to construct (but not train) the neural network at compile time. You can download the code from its github repository. It’s lacking proper documentation, but I’ve included two examples that should get you started: the xor problem and Fisher’s Iris data set.