‘I can teach you in a minute…’ In a recent post I simulated some simple dice games and promised (or threatened) that this was the first of a series of posts about games of combined luck and chance. The main aim of that post was to show how even simple probabilistic games can become complicated with tweaks to the rules, but I also mentioned a key concept that “any game of chance can be converted to a complex game of skill by adding gambling”.

Automated theorem proving has long been a key task of artificial intelligence. Proofs form the bedrock of rigorous scientific inquiry. Many tools for both partially and fully automating their derivations have been developed over the last half a century. Some examples of state-of-the-art provers are E (Schulz, 2013), VAMPIRE (Kov\’acs & Voronkov, 2013), and Prover9 (McCune, 2005-2010). Newer theorem provers, such as E, use superposition calculus in place of more traditional resolution and tableau based methods. There have also been a number of past attempts to apply machine learning methods to guiding proof search. Suttner & Ertel proposed a multilayer-perceptron based method using hand-engineered features as far back as 1990; Urban et al (2011) apply machine learning to tableau calculus; and Loos et al (2017) recently proposed a method for guiding the E theorem prover using deep nerual networks. All of this prior work, however, has one common limitation: they all rely on the axioms of classical first-order logic. Very little attention has been paid to automated theorem proving for non-classical logics. One of the only recent examples is McLaughlin & Pfenning (2008) who applied the polarized inverse method to intuitionistic propositional logic. The literature is otherwise mostly silent. This is truly unfortunate, as there are many reasons to desire non-classical proofs over classical. Constructive/intuitionistic proofs should be of particular interest to computer scientists thanks to the well-known Curry-Howard correspondence (Howard, 1980) which tells us that all terminating programs correspond to a proof in intuitionistic logic and vice versa. This work explores using Q-learning (Watkins, 1989) to inform proof search for a specific system called non-classical logic called Core Logic (Tennant, 2017). Learning to Reason

It is Christmas time! And what better time than this to write about the great tools that are available to all who like R and would like to publish their R work or even blog about it. This post is meant as a praise to the tools that are helping me to write this blog and make it a very nice experience, allowing me to focus on the content.

Tableau has chart type called “Packed Bubble Chart”, while I haven’t really utilized packed bubble chart much, I always thought they are fun and beautiful. I wanted to try creating same chart using R, and I came across package called packcircles.

I am not by any stretch of the imagination an expert on structural equation modeling but here is what I believe that this diagram is attempting to convey. But first, a bit on the relevant SEM symbol conventions:

PyText: A Seamless Path from NLP research to production

We at STATWORX work a lot with R and we often use the same little helper functions within our projects. These functions ease our daily work life by reducing repetitive code parts or by creating overviews of our projects. At first, there was no plan to make a package, but soon I realised, that it will be much easier to share and improve those functions, if they are within a package. Up till the 24th December I will present one function each day from helfRlein. So, on the 22th day of Christmas my true love gave to me…

October and December have been devastating for stocks. It wasn’t until Friday though that we officially reached the depths of a bear market.

Decision Tree Analysis for Probabilistic Subgroup Identification with Multiple Treatments (psica)In the situation when multiple alternative treatments or interventions available, different population groups may respond differently to different trea …

I’ve written some “tutorial”-like content recently—seehere,here, andhere—butI’ve been lacking on ideas for “original” content since then. With that said,I thought it would to try to re-create something with R. (Not too long agoI saw thatAndrew Heiss did something akin to this withCharles Minard’s well-known visualization of Napoleon’s 1812.)