Physical rehabilitation is not something that anyone does for fun. You do it grudgingly, after an illness or accident, to try and slowly drag your body back toward what it was able to do before. I've been there, and it sucks. I wasn't there for nearly as long as I should have been, however: rehab was hard and boring, so I didn't properly finish it. Researchers at Ben-Gurion University of the Negev in Israel, led by Shelly Levy-Tzedek, have been experimenting with ways of making rehab a bit more engaging with the addition of a friendly robot arm.
Is there an SDK to use TensorFlow Lite in Java/Kotlin? Also, is it possible to do other things in TensorFlow Lite, other than image related things? For example, teach it to win tick-tack-toe by itself? You'd think the leaders in machine learning would be able to prevent comment spam by now, especially on their own blog....
Is there an SDK to use TensorFlow Lite in Java/Kotlin? Also, is it possible to do other things in TensorFlow Lite, other than image related things? For example, teach it to win tick-tack-toe by itself? You'd think the leaders in machine learning would be able to prevent comment spam by now, especially on their own blog.... As spam goes, this one is pretty entertaining though...:D
The use of machine learning to teach computers to play board games has had a lot of interest lately. Big companies such as Facebook and Google have both made recent breakthroughs in teaching AI the complex board game, Go. However, people have been using machine learning to teach computers board games since the mid-twentieth century. In the early 1960s Donald Michie, a British computer scientist who helped break the German Tunny code during the Second World War, came up with Menace (the Machine Educable Noughts And Crosses Engine). Menace uses 304 matchboxes all filled with coloured beads in order to learn to play noughts and crosses.
"Tic-Tac-Toe Endgame" was the very first dataset I used to build a neural network some years ago. I didn't really know what I was doing at the time, and so things didn't go so well. As I have been spending a lot of time with Keras recently, I thought I would take another stab at this dataset in order to demonstrate building a simple neural network with Keras. The dataset, available here, is a collection of 958 possible tac-tac-toe end-of-game board configurations, with 9 variables representing the 9 squares of a tic-tac-toe board, and a tenth class variable which designates if the described board configuration is a winning (positive) or not (negative) ending configuration for player X. In short, does a particular collection of Xs and Os on a board mean a win for X? Incidentally, there are 255,168 possible ways of playing a game of tic-tac-toe.
In a previous post, I shared how to build a randomised Tic Tac Toe simulation. The computer plays against itself playing at random positions. In this post, I will share how to teach the computer to play the game strategically. I love the 1983 classic movie War Games. In this film, a computer plays Tic Tac Toe against itself to learn that it cannot win the game to prevent a nuclear war.
We also do a few other things, but we will hold off on discussing that until a little later in the article. The next method we discuss is the touchesBegan method. This method handles our user touches for selecting our moves and reseting the game. For each touch on the scene, we determine the location on the screen and which node was selected. For our case we either place our player's piece in a cell or we reset the game.
McKinsey analysts recently asked how traditional industries are now using machine learning to gather fresh business insights. With processing power so cheap, chewing through petabytes of data in order for machine learning to happen is no longer an activity restricted to science boffins or the cash-rich. By digitizing the past few seasons' games, it has created predictive models that allow a coach to distinguish between, as CEO Rajiv Maheswaran puts it, "a bad shooter who takes good shots and a good shooter who takes bad shots"--and to adjust his decisions accordingly". Colin Parris, the vice president of software research at GE Software reckons his company will "gain through machine learning the same sharpness of insight into the individual vagaries of a jet engine that Google has into the online behavior of a 24-year-old netizen from West Hollywood".
Simple board games, like Tic-Tac-Toe and CONNECT-4, play an important role not only in the development of mathematical and logical skills, but also in the emotional and social development. In this paper, we address the problem of generating targeted starting positions for such games. This can facilitate new approaches for bringing novice players to mastery, and also leads to discovery of interesting game variants. We present an approach that generates starting states of varying hardness levels for player 1 in a two-player board game, given rules of the board game, the desired number of steps required for player 1 to win, and the expertise levels of the two players. Our approach leverages symbolic methods and iterative simulation to efficiently search the extremely large state space. We present experimental results that include discovery of states of varying hardness levels for several simple grid-based board games. The presence of such states for standard game variants like 4 x 4 Tic-Tac-Toe opens up new games to be played that have never been played as the default start state is heavily biased.
Computer Science has a bad reputation among non-CS majors. This paper describes three assignments from a gentle introduction to programming course for non-majors that uses robots and simple game programming as a hook to get students interested in the subject. In each of the assignments presented, what might be considered a trivial twist to an instructor was a key factor in making an otherwise standard project into something that is more engaging.