One reason, researchers say: there are billions of potential moves available to a Rubik's Cube player, with the puzzle's six sides and nine sections, but only one goal: each of the cube's six sides displaying a solid color. Finding a solution to a puzzle with that degree of complexity, and among billions of potentialities, involves a degree of abstract thinking that, researchers say, begins to approximate human reasoning and decision-making.

The Travelling Salesman Problem (TSP) is one of the most popularCombinatorial Optimization Problem. It is well solicited for the large variety ofapplications that it can solve, but also for its difficulty to find optimal solutions. Oneof the variants of the TSP is the Generalized TSP (GTSP), where the TSP isconsidered as a special case which makes the GTSP harder to solve. We propose inthis paper a new memetic algorithm based on the well-known Breakout Local Search(BLS) metaheuristic to provide good solutions for GTSP instances. Our approach iscompetitive compared to other recent memetic algorithms proposed for the GTSPand gives at the same time some improvements to BLS to reduce its runtime.Keywords: Generalized Travelling Salesman Problem, Breakout Local Search,Memetic Algorithms, Iterated Local Search

Yesterday, artificial intelligence(AI) powerhouse OpenAI astonished the world by unveiling a prototype of a robotic arm that could solve a Rubik's cube with one hand. The prototype didn't only represent a milestone for the robotics ecosystem in solving high complexity tasks that actively require sensorial information but it also resulted on a major achievement for the AI community. The reason is that the OpenAI robot was completely trained using simulations based on the reinforcement learning models that the OpenAI Five system used to beat human players in Dota2. The research was discussed in a paper that accompanied the news. The importance of OpenAI's achievement was not about designing a robot that could solve a Rubik's cube.

--Recently, randomly mapping vectorial data to strings of discrete symbols (i.e., sketches) for fast and space-efficient similarity searches has become popular . Such random mapping is called similarity-preserving hashing and approximates a similarity metric by using the Hamming distance. Although many efficient similarity searches have been proposed, most of them are designed for binary sketches. Similarity searches on integer sketches are in their infancy. In this paper, we present a novel space-efficient trie named b -bit sketch trie on integer sketches for scalable similarity searches by leveraging the idea behind succinct data structures (i.e., space-efficient data structures while supporting various data operations in the compressed format) and a favorable property of integer sketches as fixed-length strings. Our experimental results obtained using real-world datasets show that a trie-based index is built from integer sketches and efficiently performs similarity searches on the index by pruning useless portions of the search space, which greatly improves the search time and space-efficiency of the similarity search. The experimental results show that our similarity search is at most one order of magnitude faster than state-of-the-art similarity searches. Besides, our method needs only 10 GiB of memory on a billion-scale database, while state-of-the-art similarity searches need 29 GiB of memory. I NTRODUCTION The similarity search of vectorial data in databases has been a fundamental task in recent data analysis, and it has various applications such as near duplicate detection in a collection of web pages [1], context-based retrieval in images [2], and functional analysis of molecules [3]. Recently, databases in these applications have become large, and vectorial data in these databases also have been high dimensional, which makes it difficult to apply existing similarity search methods to such large databases. There is thus a strong need to develop much more powerful methods of similarity search for efficiently analyzing databases on a large-scale. A powerful solution to address this need is similarity-preserving hashing, which intends to approximate a similarity measure by randomly mapping vectorial data in a metric space to strings of discrete symbols (i.e., sketches) in the Hamming space. Early methods include Sim-Hash for cosine similarity [4], which intends to build binary sketches from vectorial data for approximating cosine similarity.

Neural Architecture Search (NAS) represents a class of methods to generate the optimal neural network architecture and typically iterate over candidate architectures till convergence over some particular metric like validation loss. They are constrained by the available computation resources, especially in enterprise environments. In this paper, we propose a new approach for NAS, called NASIB, which adapts and attunes to the computation resources (budget) available by varying the exploration vs. exploitation trade-off. We reduce the expert bias by searching over an augmented search space induced by Superkernels. The proposed method can provide the architecture search useful for different computation resources and different domains beyond image classification of natural images where we lack bespoke architecture motifs and domain expertise. We show, on CIFAR10, that itis possible to search over a space that comprises of 12x more candidate operations than the traditional prior art in just 1.5 GPU days, while reaching close to state of the art accuracy. While our method searches over an exponentially larger search space, it could lead to novel architectures that require lesser domain expertise, compared to the majority of the existing methods.

Space-filling designs such as scrambled-Hammersley, Latin Hypercube Sampling and Jittered Sampling have been proposed for fully parallel hyperparameter search, and were shown to be more effective than random or grid search. In this paper, we show that these designs only improve over random search by a constant factor. In contrast, we introduce a new approach based on reshaping the search distribution, which leads to substantial gains over random search, both theoretically and empirically. We propose two flavors of reshaping. First, when the distribution of the optimum is some known $P_0$, we propose Recentering, which uses as search distribution a modified version of $P_0$ tightened closer to the center of the domain, in a dimension-dependent and budget-dependent manner. Second, we show that in a wide range of experiments with $P_0$ unknown, using a proposed Cauchy transformation, which simultaneously has a heavier tail (for unbounded hyperparameters) and is closer to the boundaries (for bounded hyperparameters), leads to improved performances. Besides artificial experiments and simple real world tests on clustering or Salmon mappings, we check our proposed methods on expensive artificial intelligence tasks such as attend/infer/repeat, video next frame segmentation forecasting and progressive generative adversarial networks.

We propose a Reinforcement Learning based approach to approximately solve the Tree Decomposition (TD)problem. TD is a combinatorial problem, which is central to the analysis of graph minor structure and computational complexity, as well as in the algorithms of probabilistic inference, register allocation, and other practical tasks. Recently, it has been shown that combinatorial problems can be successively solved by learned heuristics. However, the majority of existing works do not address the question of the generalization of learning-based solutions. Our model is based on the graph convolution neural network (GCN) for learning graph representations. We show that the agent builton GCN and trained on a single graph using an Actor-Critic method can efficiently generalize to real-world TD problem instances. We establish that our method successfully generalizes from small graphs, where TD can be found by exact algorithms, to large instances of practical interest, while still having very low time-to-solution. On the other hand, the agent-based approach surpasses all greedy heuristics by the quality of the solution.

Artificial intelligence research organization OpenAI has achieved a new milestone in its quest to build general purpose, self-learning robots. The group's robotics division says that Dactyl, its humanoid robotic hand first developed last year, has learned to solve a Rubik's cube one-handed. In a demonstration video showcasing Dactyl's new talent, we can see the robotic hand fumble its way toward a complete cube solve with clumsy yet accurate maneuvers. It takes many minutes, but Dactyl is eventually able to solve the puzzle. It's somewhat unsettling to see in action, if only because the movements look noticeably less fluid than human ones and especially disjointed when compared to the blinding speed and raw dexterity on display when a human speedcuber solves the cube in a matter of seconds.

To avoid this, roboticists use simulation: they build a virtual model of their robot and train it virtually to do the task at hand. The algorithm learns in the safety of the digital space and can be ported into a physical robot afterwards. But that process comes with its own challenges. It's nearly impossible to build a virtual model that exactly replicates all the same laws of physics, material properties, and manipulation behaviors seen in the real world--let alone unexpected circumstances. Thus, the more complex the robot and task, the more difficult it is to apply a virtually trained algorithm in physical reality.