The quality of opponent Artificial Intelligence (AI) in fighting videogames is crucial. Some other game genres can rely on their story or visuals, but fighting games are all about the adversarial experience. In this paper, we will introduce standard behavior algorithms in videogames, such as Finite-State Machines and Behavior Trees, as well as more recent developments, such as Monte-Carlo Tree Search. We will also discuss the existing and potential combinations of these algorithms, and how they might be used in fighting games. Since we are at the financial peak of fighting games, both for casual players and in tournaments, it is important to build and expand on fighting game AI, as it is one of the pillars of this growing market.

Mission schedule of satellites is an important part of space operation nowadays, since the number and types of satellites in orbit are increasing tremendously and their corresponding tasks are also becoming more and more complicated. In this paper, a mission schedule model combined with Proximal Policy Optimization Algorithm(PPO) is proposed. Different from the traditional heuristic planning method, this paper incorporate reinforcement learning algorithms into it and find a new way to describe the problem. Several constraints including data download are considered in this paper.

Block-based visual programming environments play a critical role in introducing computing concepts to K-12 students. One of the key pedagogical challenges in these environments is in designing new practice tasks for a student that match a desired level of difficulty and exercise specific programming concepts. In this paper, we formalize the problem of synthesizing visual programming tasks. In particular, given a reference visual task $\rm T^{in}$ and its solution code $\rm C^{in}$, we propose a novel methodology to automatically generate a set $\{(\rm T^{out}, \rm C^{out})\}$ of new tasks along with solution codes such that tasks $\rm T^{in}$ and $\rm T^{out}$ are conceptually similar but visually dissimilar. Our methodology is based on the realization that the mapping from the space of visual tasks to their solution codes is highly discontinuous; hence, directly mutating reference task $\rm T^{in}$ to generate new tasks is futile. Our task synthesis algorithm operates by first mutating code $\rm C^{in}$ to obtain a set of codes $\{\rm C^{out}\}$. Then, the algorithm performs symbolic execution over a code $\rm C^{out}$ to obtain a visual task $\rm T^{out}$; this step uses the Monte Carlo Tree Search (MCTS) procedure to guide the search in the symbolic tree. We demonstrate the effectiveness of our algorithm through an extensive empirical evaluation and user study on reference tasks taken from the \emph{Hour of the Code: Classic Maze} challenge by \emph{Code.org} and the \emph{Intro to Programming with Karel} course by \emph{CodeHS.com}.

Monte-Carlo planning and Reinforcement Learning (RL) are essential to sequential decision making. The recent AlphaGo and AlphaZero algorithms have shown how to successfully combine these two paradigms in order to solve large scale sequential decision problems. These methodologies exploit a variant of the well-known UCT algorithm to trade off exploitation of good actions and exploration of unvisited states, but their empirical success comes at the cost of poor sample-efficiency and high computation time. In this paper, we overcome these limitations by considering convex regularization in Monte-Carlo Tree Search (MCTS), which has been successfully used in RL to efficiently drive exploration. First, we introduce a unifying theory on the use of generic convex regularizers in MCTS, deriving the regret analysis and providing guarantees of exponential convergence rate. Second, we exploit our theoretical framework to introduce novel regularized backup operators for MCTS, based on the relative entropy of the policy update, and on the Tsallis entropy of the policy. Finally, we empirically evaluate the proposed operators in AlphaGo and AlphaZero on problems of increasing dimensionality and branching factor, from a toy problem to several Atari games, showing their superiority w.r.t. representative baselines.

The modern space race is heating up, and the European Union is acutely aware that it needs to keep pace. Space chief Thierry Breton told Reuters in an interview that the EU is accelerating its plans in light of rapid progress by private companies like SpaceX as well as China's successes. It's moving the deployment of its Galileo navigation satellites ahead by three years, to 2024, and will use its budget for the first time to support reusable rockets and other new launch tech. The EU is also forging a €1 billion deal with Arianespace to spur innovation, and will propose a €1 billion European Space Fund and competitions to foster startups. Breton also hoped to launch a pan-European satellite broadband network as well as a system to avoid collisions with satellites and other items in orbit.

Retrosynthetic planning is a critical task in organic chemistry which identifies a series of reactions that can lead to the synthesis of a target product. The vast number of possible chemical transformations makes the size of the search space very big, and retrosynthetic planning is challenging even for experienced chemists. However, existing methods either require expensive return estimation by rollout with high variance, or optimize for search speed rather than the quality. In this paper, we propose Retro*, a neural-based A*-like algorithm that finds high-quality synthetic routes efficiently. It maintains the search as an AND-OR tree, and learns a neural search bias with off-policy data. Then guided by this neural network, it performs best-first search efficiently during new planning episodes. Experiments on benchmark USPTO datasets show that, our proposed method outperforms existing state-of-the-art with respect to both the success rate and solution quality, while being more efficient at the same time.

When it comes to artificial intelligence (AI), countless conference sessions and seminars have dedicated inconceivable amount of hours asking what-if questions, with terrifying examples from across science fiction acting as the bleak backgrounds. Terminator's Skynet, Agents in The Matrix, and Ava in Ex Machina are just some of the fictional antagonists which have stemmed from humanity's own creations. But one franchise has spent over 50 years diving deeper than its contemporaries to depict scenarios of AI enhancing life, and in some cases not so – and that is Star Trek. Gene Roddenberry's utopic vision of the future has led to some of the most thought-provoking media to come to life. Topics of race and discrimination, death, and morality are some of the cornerstone topics that kept it relevant across multiple iterations for so long.

Housing Secretary Robert Jenrick still has questions to answer over his role in a planning case involving a Tory donor, Sir Keir Starmer has said. The Labour leader told the BBC the matter was "far from closed" but stopped short of calling for the minister's resignation. Mr Jenrick is under fire after granting permission for a luxury housing development to donor Richard Desmond. Downing Street said the PM had full confidence in the minister. Mr Jenrick says he was motivated by a desire to see more homes built when he overruled government inspectors to give the green light to Mr Desmond's plans for a 1,500 home development at the former Westferry printing works, in London's Isle of Dogs.

In this paper we address the mine countermeasures (MCM) search problem for an autonomous underwater vehicle (AUV) surveying the seabed using a side-looking sonar. We propose a coverage path planning method that adapts the AUV track spacing with the objective of collecting better data. We achieve this by shifting the coverage overlap at the tail of the sensor range where the lowest data quality is expected. To assess the algorithm, we collected data from three at-sea experiments. The adaptive survey allowed the AUV to recover from a situation where the sensor range was overestimated and resulted in reducing area coverage gaps. In another experiment,the adaptive survey showed a 4.2% improvement in data quality for nearly 30% of the 'worst' data.

In this work, the online printing shop scheduling problem introduced in (Lunardi et al., Mixed Integer Linear Programming and Constraint Programming Models for the Online Printing Shop Scheduling Problem, Computers & Operations Research, to appear) is considered. This challenging real scheduling problem, that emerged in the nowadays printing industry, corresponds to a flexible job shop scheduling problem with sequencing flexibility; and it presents several complicating specificities such as resumable operations, periods of unavailability of the machines, sequence-dependent setup times, partial overlapping between operations with precedence constraints, and fixed operations, among others. A local search strategy and metaheuristic approaches for the problem are proposed and evaluated. Based on a common representation scheme, trajectory and populational metaheuristics are considered. Extensive numerical experiments with large-sized instances show that the proposed methods are suitable for solving practical instances of the problem; and that they outperform a half-heuristic-half-exact off-the-shelf solver by a large extent. Numerical experiments with classical instances of the flexible job shop scheduling problem show that the introduced methods are also competitive when applied to this particular case.