This paper presents a demonstration of how AI can be useful in the game design and development process of a modern board game. By using an artificial intelligence algorithm to play a substantial amount of matches of the Ticket to Ride board game and collecting data, we can analyze several features of the gameplay as well as of the game board. Results revealed loopholes in the game's rules and pointed towards trends in how the game is played. We are then led to the conclusion that large scale simulation utilizing artificial intelligence can offer valuable information regarding modern board games and their designs that would ordinarily be prohibitively expensive or time-consuming to discover manually.

If you're looking to play around with robotics, Lego's Mindstorms EV3 is a great way to get started. So is the ultra-versatile Raspberry Pi. Combining the two to create a Rubik's Cube-solving robot? That sounds like a good time to us! The Lego bricks take care of the physical moves required to solve the puzzle.

A typical viral marketing model identifies influential users in a social network to maximize a single product adoption assuming unlimited user attention, campaign budgets, and time. In reality, multiple products need campaigns, users have limited attention, convincing users incurs costs, and advertisers have limited budgets and expect the adoptions to be maximized soon. Facing these user, monetary, and timing constraints, we formulate the problem as a submodular maximization task in a continuous-time diffusion model under the intersection of a matroid and multiple knapsack constraints. We propose a randomized algorithm estimating the user influence in a network ($|\mathcal{V}|$ nodes, $|\mathcal{E}|$ edges) to an accuracy of $\epsilon$ with $n=\mathcal{O}(1/\epsilon^2)$ randomizations and $\tilde{\mathcal{O}}(n|\mathcal{E}|+n|\mathcal{V}|)$ computations. By exploiting the influence estimation algorithm as a subroutine, we develop an adaptive threshold greedy algorithm achieving an approximation factor $k_a/(2+2 k)$ of the optimal when $k_a$ out of the $k$ knapsack constraints are active. Extensive experiments on networks of millions of nodes demonstrate that the proposed algorithms achieve the state-of-the-art in terms of effectiveness and scalability.

Finding the right classifier to use for your data can be hard. Once you have chosen a classifier, tuning all of the parameters to get the best results is tedious and time consuming. Even after all of your hard work, you may have chosen the wrong classifier to begin with. Hyperopt-sklearn provides a solution to this problem. Any search algorithm available in hyperopt can be used to drive the estimator.

Subset selection in multiple linear regression is to choose a subset of candidate explanatory variables that tradeoff error and the number of variables selected. We built mathematical programming models for subset selection and compare the performance of an LP-based branch-and-bound algorithm with tailored valid inequalities to known heuristics. We found that our models quickly find a quality solution while the rest of the time is spent to prove optimality. Our models are also applicable with slight modifications to the case with more candidate explanatory variables than observations. For this case, we provide mathematical programming models, propose new criteria, and develop heuristic algorithms based on mathematical programming.

This paper presents a particular model of heuristic search as a path-finding problem in a directed graph. A class of graph-searching procedures is described which uses a heuristic function to guide search. Heuristic functions are estimates of the number of edges that remain to be traversed in reaching a goal node. A number of theoretical results for this model, and the intuition for these results, are presented. They relate the efficiency of search to the accuracy of the heuristic function.

Abstract: Real-time situated agents, including characters in real-time computer games, often do not know the terrain in advance but automatically observe it within a certain range around them. They have to interleave planning with movement to make planning tractable when moving autonomously to user-specified coordinates. Planning faces real-time requirements since it is important that the agents be responsive to the commands of the users and move smoothly. In this paper, we compare two fast search methods for this task that speed up planning in different ways, namely real-time heuristic search (LRTA*) and incremental heuristic search (D* Lite), resulting in the first comparison of real-time and incremental heuristic search in the literature. We characterize when to choose which search method, depending on the kind of terrain and the planning objective.

Board Games Research Group: develops high performance search algorithms and game playing programs such as Fuego, the first Go program to beat a top human player in 9x9 Go. Games Research Group: engages in the design, analysis, and implementation of artificial intelligence technology that is suitable for use in high-performance game-playing programs. Intelligent Reasoning Critiquing and Learning (IRCL) Group: conducts Artificial Intelligence research on real-time heuristic search, interactive story-telling and cognitive modeling. Our recent applications have been with video games. We have on-going collaborations with the Department of Psychology, UBC Okanagan, Reykjavik University and Disney Research.

In the popular imagination, chess isn't like a spelling bee or Trivial Pursuit, a competition to see who can hold the most facts in memory and consult them quickly. In chess, as in the arts and sciences, there is plenty of room for beauty, subtlety, and deep originality. Chess requires brilliant thinking, supposedly the one feat that would be–forever–beyond the reach of any computer. But for a decade, human beings have had to live with the fact that one of our species' most celebrated intellectual summits–the title of world chess champion–has to be shared with a machine, Deep Blue, which beat Garry Kasparov in a highly publicized match in 1997. What lessons could be gleaned from this shocking upset?

They said it couldn't be done, but Google's AI technology has proved them wrong by mastering the ancient Chinese game of Go roughly a decade earlier than anyone expected. Tapping neural networks and advanced "tree search" programs, researchers from Google DeepMind created a system called AlphaGo that takes a different approach to the game than had been tried before. In Go, the player's objective is to surround the opponent's pieces by alternately placing black and white pieces on a 19-by-19-line grid while simultaneously avoiding having one's own pieces surrounded. With more possible positions than there are atoms in the universe, Go has long been considered an ultimate challenge for artificial intelligence researchers. Traditional AI efforts to master Go have focused on using search trees, a computer science technique used for locating specific values from within a set. AlphaGo, on the other hand, uses the more advanced Monte Carlo tree search approach often used in game playing.