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Game On! MIT, Allen AI & Microsoft Open-Source a Suite of AI Programming Puzzles

#artificialintelligence

Programming competition problems are pervasive in the AI community. They can be used to evaluate programmers' abilities to solve artificial tasks as well as to test the limits of state-of-the-art algorithms. A research team from MIT, Allen Institute for AI and Microsoft Research recently introduced Python Programming Puzzles (P3), a novel and open-source collection of programming challenges that capture the essence of puzzles and can be used to teach and evaluate an AI's programming proficiency. The proposed puzzles take the form of a Python function with the answer as an argument. The goal is to find an input x that makes the output of the function true, i.e., a valid answer x satisfies f(x) True.


What It Takes to Turn a Video Game Into a Tabletop One

WIRED

Andrew Fischer was facing a conundrum. Gamers sink literally hundreds of hours into Bethesda's Fallout games. The twisted steel and charred homesteads sprawl out in every direction, and it's hard to feel fully satiated by your save file until you've explored every square inch of the atlas. To truly appreciate Fallout, one must commune with the ghouls, and ride with the raiders, and spelunk through blown-out cafeterias and coffee shops long before we see the credits roll. Hell, sometimes we even set the side quests aside in order to bask peacefully under the wide open night sky during the brief breaks between super mutant assaults.


The 15 greatest video games of the 80s – ranked!

The Guardian

The 1980s were crammed with wonderful adventure games – The Hobbit, King's Quest, Leather Goddesses of Phobos – but the first point-and-click title to be designed by comic genius Ron Gilbert using the SCUMM scripting language is the classic that busted out of the genre ghetto. Filled with great jokes and B-movie cliches, the game made brilliant use of its accessible and intuitive interface, as well as seamlessly integrating cutscenes and non-sequential puzzles. Among the formative home computer platformers of the 80s – the likes of Lode Runner, Chuckie Egg and Pitfall – Jet Set Willy stands out for its surreal sense of humour and genuinely disturbing atmosphere. Like that other 8-bit pioneer Jeff Minter, Matthew Smith created his own idiosyncratic dream worlds with distinct rules and twisted logic, and as you battled through the bizarre house with its haunted wine cellars, priest holes and watchtowers, you had to contend with truly monstrous visions, from spinning razor blades to giant demon heads. Smith only made a handful of games, but with Jet Set Willy, he combined Monty Python and Hammer House of Horror to unforgettable effect.


Solving a Puzzle -- Machine Learning Algorithm Selection

#artificialintelligence

There are many types of machine learning models, from linear models, tree-based, ensembles, to neural networks. Knowing which model to pick up while approaching a given problem can be a battle. In this write-up, I want to share some takes that can hopefully reduce your modeling curve. While I will list different factors to consider, a model selection is a no-free lunch scenario -- There is no model that is guaranteed to solve a problem before you try and evaluate different models. The first thing to consider is the scope of the project.


What a Crossword AI Reveals About Humans' Way With Words

WIRED

At last week's American Crossword Puzzle Tournament, held as a virtual event with more than 1,000 participants, one impressive competitor made news. For the first time, artificial intelligence managed to outscore the human solvers in the race to fill the grids with speed and accuracy. It was a triumph for Dr. Fill, a crossword-solving automaton that has been vying against carbon-based cruciverbalists for nearly a decade. For some observers, this may have seemed like just another area of human endeavor where AI now has the upper hand. Reporting on Dr. Fill's achievement for Slate, Oliver Roeder wrote, "Checkers, backgammon, chess, Go, poker, and other games have witnessed the machines' invasions, falling one by one to dominant AIs. Now crosswords have joined them."


Are Crossword Solvers About to Go the Way of Chess Players?

#artificialintelligence

Nearly 1,300 people spent this past weekend racing to fill little boxes inside larger boxes, ever mindful of spelling, trivia, wordplay, and a ticking clock. They were competitors--newcomers, ardent hobbyists, and elite speed solvers--in the American Crossword Puzzle Tournament, the pastime's most prestigious competition. And most of them got creamed by some software. The annual event, normally set in a packed hotel ballroom with solvers separated by yellow dividers, was virtual this year, pencils swapped for keyboards. After millions of little boxes had been filled, a computer program topped the leaderboard for the first time.


Competitive 'Tetris' was soaring, then it lost a legend. What comes next is a puzzle.

Washington Post - Technology News

In the process, he became one of esports' first and unlikeliest celebrities. A longtime taproom manager from California, Neubauer had never imagined he would spend his 30s becoming a global ambassador for the video game he grew up playing obsessively. He made guest appearances at Tetris tournaments in Taiwan, Germany, and Denmark. He was featured in Rolling Stone and Vice, hailed by the latter as the bartender who was "secretly the greatest Tetris player in the world." And he won over fans with his sense of humor ("You never want to be that guy with the Tinder profile with the Tetris trophy," he joked to Rolling Stone in 2014).


Using Small MUSes to Explain How to Solve Pen and Paper Puzzles

arXiv.org Artificial Intelligence

Pen and paper puzzles like Sudoku, Futoshiki and Skyscrapers are hugely popular. Solving such puzzles can be a trivial task for modern AI systems. However, most AI systems solve problems using a form of backtracking, while people try to avoid backtracking as much as possible. This means that existing AI systems do not output explanations about their reasoning that are meaningful to people. We present Demystify, a tool which allows puzzles to be expressed in a high-level constraint programming language and uses MUSes to allow us to produce descriptions of steps in the puzzle solving. We give several improvements to the existing techniques for solving puzzles with MUSes, which allow us to solve a range of significantly more complex puzzles and give higher quality explanations. We demonstrate the effectiveness and generality of Demystify by comparing its results to documented strategies for solving a range of pen and paper puzzles by hand, showing that our technique can find many of the same explanations.


Are Crossword Solvers About to Go the Way of Chess Players?

Slate

Nearly 1,300 people spent this past weekend racing to fill little boxes inside larger boxes, ever mindful of spelling, trivia, wordplay, and a ticking clock. They were competitors--newcomers, ardent hobbyists, and elite speed solvers--in the American Crossword Puzzle Tournament, the pastime's most prestigious competition. And most of them got creamed by some software. The annual event, normally set in a packed hotel ballroom with solvers separated by yellow dividers, was virtual this year, pencils swapped for keyboards. After millions of little boxes had been filled, a computer program topped the leaderboard for the first time.


Decrypting Cryptic Crosswords: Semantically Complex Wordplay Puzzles as a Target for NLP

arXiv.org Artificial Intelligence

Cryptic crosswords, the dominant English-language crossword variety in the United Kingdom, can be solved by expert humans using flexible, creative intelligence and knowledge of language. Cryptic clues read like fluent natural language, but they are adversarially composed of two parts: a definition and a wordplay cipher requiring sub-word or character-level manipulations. As such, they are a promising target for evaluating and advancing NLP systems that seek to process language in more creative, human-like ways. We present a dataset of cryptic crossword clues from a major newspaper that can be used as a benchmark and train a sequence-to-sequence model to solve them. We also develop related benchmarks that can guide development of approaches to this challenging task. We show that performance can be substantially improved using a novel curriculum learning approach in which the model is pre-trained on related tasks involving, e.g, unscrambling words, before it is trained to solve cryptics. However, even this curricular approach does not generalize to novel clue types in the way that humans can, and so cryptic crosswords remain a challenge for NLP systems and a potential source of future innovation.