A minimum dominating set in a graph is a minimum set of vertices such that every vertex of the graph either belongs to it, or is adjacent to one vertex of this set. This mathematical object is of high relevance in a number of applications related to social networks analysis, design of wireless networks, coding theory, and data mining, among many others. When vertex weights are given, minimizing the total weight of the dominating set gives rise to a problem variant known as the minimum weight dominating set problem. To solve this problem, we introduce a hybrid matheuristic combining a tabu search with an integer programming solver. The latter is used to solve subproblems in which only a fraction of the decision variables, selected relatively to the search history, are left free while the others are fixed. Moreover, we introduce an adaptive penalty to promote the exploration of intermediate infeasible solutions during the search, enhance the algorithm with perturbations and node elimination procedures, and exploit richer neighborhood classes. Extensive experimental analyses on a variety of instance classes demonstrate the good performance of the algorithm, and the contribution of each component in the success of the search is analyzed.

DeepMind has shaken the world of Reinforcement Learning and Go with its creation AlphaGo, and later AlphaGo Zero. It is the first computer program to beat a human professional Go player without handicap on a 19 x 19 board. It has also beaten the world champion Lee Sedol 4 games to 1, Ke Jie (number one world ranked player at the time) and many other top ranked players with the Zero version. The game of Go is a difficult environment because of its very large branching factor at every move which makes classical techniques such as alpha-beta pruning and heuristic search unrealistic. I will present my work on reproducing the paper as closely as I could. This article will again require background knowledge in Machine Learning and Python, as I will make references to my own implementation.

We investigate the impact of supervised prediction models on the strength and efficiency of artificial agents that use the Monte-Carlo Tree Search (MCTS) algorithm to play a popular video game Hearthstone: Heroes of Warcraft. We overview our custom implementation of the MCTS that is well-suited for games with partially hidden information and random effects. We also describe experiments which we designed to quantify the performance of our Hearthstone agent's decision making. We show that even simple neural networks can be trained and successfully used for the evaluation of game states. Moreover, we demonstrate that by providing a guidance to the game state search heuristic, it is possible to substantially improve the win rate, and at the same time reduce the required computations.

This article will introduce you to a range of introductory concepts used in artificial intelligence for games (or'Game AI' for short) so that you can understand what tools are available for approaching your AI problems, how they work together, and how you might start to implement them in the language or engine of your choice. We're going to assume you have a basic knowledge of video games, and some grasp on mathematical concepts like geometry, trigonometry, etc. Most code examples will be in pseudo-code, so no specific programming language knowledge should be required. Game AI is mostly focused on which actions an entity should take, based on the current conditions. This is what the traditional AI literature refers to as controlling'intelligent agents' where the agent is usually a character in the game – but could also be a vehicle, a robot, or occasionally something more abstract such as a whole group of entities, or even a country or civilization. In each case it is a thing that ...

In this paper we study the limitations of parallelization in convex optimization. A convenient approach to study parallelization is through the prism of \emph{adaptivity} which is an information theoretic measure of the parallel runtime of an algorithm. Informally, adaptivity is the number of sequential rounds an algorithm needs to make when it can execute polynomially-many queries in parallel at every round. For combinatorial optimization with black-box oracle access, the study of adaptivity has recently led to exponential accelerations in parallel runtime and the natural question is whether dramatic accelerations are achievable for convex optimization. Our main result is a spoiler. We show that, in general, parallelization does not accelerate convex optimization. In particular, for the problem of minimizing a non-smooth Lipschitz and strongly convex function with black-box oracle access we give information theoretic lower bounds that indicate that the number of adaptive rounds of any randomized algorithm exactly match the upper bounds of single-query-per-round (i.e. non-parallel) algorithms.

This thesis employs statistical learning technique to analyze, predict and solve the fixed charge network flow (FCNF) problem, which is common encountered in many real-world network problems. The cost structure for flows in the FCNF involves both fixed and variable costs. The FCNF problem is modeled mixed binary linear programs and can be solved with standard commercial solvers, which use branch and bound algorithm. This problem is important for its widely applications and solving challenges. There does not exist a efficient algorithm to solve this problem optimally due to lacking tight bounds. To the best of our knowledge, this is the first work that employs statistical learning technique to analyze the optimal flow of the FCNF problem. Most algorithms developed to solve the FCNF problem are based on the cost structure, relaxation, etc. We start from the network characteristics and explore the relationship between properties of nodes, arcs and networks and the optimal flow. This is a bi-direction approach and the findings can be used to locate the features that affect the optimal flow most significantly, predict the optimal arcs and provide information to solve the FCNF problem. In particular, we define 33 features based on the network characteristics, from which using step wise regression, we identify 26 statistical significant predictors for logistic regression to predict which arcs will have positive flow in the optimal solutions. The predictive model achieves 88% accuracy and the area under receiver operating characteristic curve is 0.95. Two applications are investigated. Firstly, the predictive results can be used directly as component critical index. The failure of arcs with higher critical index result in more cost increase over the entire network.

Finding the diameter of a dataset in multidimensional Euclidean space is a well-established problem, with well-known algorithms. However, most of the algorithms found in the literature do not scale well with large values of data dimension, so the time complexity grows exponentially in most cases, which makes these algorithms impractical. Therefore, we implemented 4 simple greedy algorithms to be used for approximating the diameter of a multidimensional dataset; these are based on minimum/maximum l2 norms, hill climbing search, Tabu search and Beam search approaches, respectively. The time complexity of the implemented algorithms is near-linear, as they scale near-linearly with data size and its dimensions. The results of the experiments (conducted on different machine learning data sets) prove the efficiency of the implemented algorithms and can therefore be recommended for finding the diameter to be used by different machine learning applications when needed.

One of the world's oldest and most popular toys is getting a face-lift. Israel-based startup Particula has unveiled its spin on the Rubik's cube, dubbed the GoCube, that can connect to your phone and enables users to play against other people. It marks a major step up from when the original Rubik's cube, developed by Hungarian sculptor Erno Rubik, was first released in 1974. Since then, over 350 million Rubik's cubes have been sold worldwide. Israel-based startup Particula has unveiled its spin on the Rubik's cube, dubbed the GoCube (pictured), that can connect to your phone and enables users to play against other people The GoCube syncs up with smartphones and tablets using a Bluetooth connection, giving users access to the Battle feature, which lets them'play friends (or enemies) across the world, according to Particula.

There is considerable hype around data science. Websites and social media are flooded with articles on Big Data, Data Science, and Data Analytics. These fields are projected as top fields, while data scientists are considered as saviours of the world and hence are supposed to be highest paid professionals. Most articles project how, using data science, companies have become super-intelligent in understanding their customers and hence are now able to sell products and services in a sophisticated manner. The marketing is now based on customers' differential purchasing interest and buying behaviour.

What if the huge design space for data-driven software could be efficiently mapped and explored in order to have tailor-made, optimized solutions? Researchers from Harvard combine analytical models, benchmarks, and machine learning to make this possible. The Internet of Things is creating serious new security risks. We examine the possibilities and the dangers. Stratos Idreos is an Assistant Professor at Harvard, and leads the Data Systems Laboratory (DASlab).