Designing artificial intelligence for games (Game AI) has been long recognized as a notoriously challenging task in game industry, as it mainly relies on manual design, requiring plenty of domain knowledge. More frustratingly, even spending a lot of efforts, a satisfying Game AI is still hard to achieve by manual design due to the almost infinite search space. The recent success of deep reinforcement learning (DRL) sheds light on advancing automated game designing, significantly relaxing human competitive intelligent supp ort. However, existing DRL algorithms mostly focus on training a Game AI to win the game rather that the way it wins (style). To bridge the gap, we introduce EMO-DRL, an end-to-end game design framework, leveraging evolutionary algorithm, DRL and multi-objective optimization (MOO) to perform intelligent and automatic game design. Firstly, EMO-DRL proposes the style-oriented learning to bypass manual reward shaping in DRL and directly learns a Game AI with an expected style in an end-to-end fashion. On this basis, the prioritized multi-objective optimization is introduced to achieve more diverse, nature and humanlike Game AI. Large-scale evaluations on a Atari game and a commercial massively mul-tiplayer online game are conducted. The results demonstrat es that EMO-DRL, compared to existing algorithms, achieve better game designs in an intelligent and automatic way.

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

The artificial skin developed by Prof. Gordon Cheng and his team consists of hexagonal cells about the size of a two-euro coin (i.e. about one inch in diameter). Each is equipped with a microprocessor and sensors to detect contact, acceleration, proximity and temperature. Such artificial skin enables robots to perceive their surroundings in much greater detail and with more sensitivity. This not only helps them to move safely. It also makes them safer when operating near people and gives them the ability to anticipate and actively avoid accidents.

Before moving on, let's take some time to have a closer look at a single-objective problem. This will give us some perspective. In single-objective problems, the objective is to find a single solution which represents the global optimum in the entire search space. Determining which solutions outperforms others is a simple task when only considering a single-objective, because the best solution is simply the one with the highest (for maximisation problems) or lowest (for minimisation problems) objective value. Let's take the Sphere function as an example.

From Gary Kasparov to Elon Musk, the list of those who say AI needs to be applied such that it augments us, not compete with us, is long. Yet the supply of reports warning that AI threatens jobs doesn't seem to have an end. On the other hand, a new report looking at a technology called Swarm AI may provide a much more benign fix. Speaking at a recent conference, chess legend, Gary Kasparov, said that the public perception of AI has been overly influenced by Hollywood: the reality is far more positive -- Kasparov's take on AI is a reason for optimism Swarms can be intelligent-- there is no great insight here. Those who study Emergence understand this, from ant colonies to cities, great things can be achieved from simpler entities working together.

Parallel exploration is a key to a successful search. The recently proposed Negatively Correlated Search (NCS) achieved this ability by constructing a set of negatively correlated search processes and has been applied to many real-world problems. In NCS, the key technique is to explicitly model and maximize the diversity among search processes in parallel. However, the original diversity model was mostly devised by intuition, which introduced several drawbacks to NCS. In this paper, a mathematically principled diversity model is proposed to solve the existing drawbacks of NCS, resulting a new NCS framework. A new instantiation of NCS is also derived and its effectiveness is verified on a set of multi-modal continuous optimization problems.

Reparameterization (RP) and likelihood ratio (LR) gradient estimators are used throughout machine and reinforcement learning; however, they are usually explained as simple mathematical tricks without providing any insight into their nature. We use a first principles approach to explain LR and RP, and show a connection between the two via the divergence theorem. The theory motivated us to derive optimal importance sampling schemes to reduce LR gradient variance. Our newly derived distributions have analytic probability densities and can be directly sampled from. The improvement for Gaussian target distributions was modest, but for other distributions such as a Beta distribution, our method could lead to arbitrarily large improvements, and was crucial to obtain competitive performance in evolution strategies experiments.

Choosing the most adequate kernel is crucial in many Machine Learning applications. Gaussian Process is a state-of-the-art technique for regression and classification that heavily relies on a kernel function. However, in the Gaussian Process literature, kernels have usually been either ad hoc designed, selected from a predefined set, or searched for in a space of compositions of kernels which have been defined a priori. In this paper, we propose a Genetic-Programming algorithm that represents a kernel function as a tree of elementary mathematical expressions. By means of this representation, a wider set of kernels can be modeled, where potentially better solutions can be found, although new challenges also arise. The proposed algorithm is able to overcome these difficulties and find kernels that accurately model the characteristics of the data. This method has been tested in several real-world time-series extrapolation problems, improving the state-of-the-art results while reducing the complexity of the kernels.

Sensitive synthetic skin enables robots to sense their own bodies and surroundings--a crucial capability if they are to be in close contact with people. Inspired by human skin, a team at the Technical University of Munich (TUM) has developed a system combining artificial skin with control algorithms and used it to create the first autonomous humanoid robot with full-body artificial skin. The artificial skin developed by Prof. Gordon Cheng and his team consists of hexagonal cells about the size of a two-euro coin (i.e. about one inch in diameter). Each is equipped with a microprocessor and sensors to detect contact, acceleration, proximity and temperature. Such artificial skin enables robots to perceive their surroundings in much greater detail and with more sensitivity.

Technical University of Munich researchers designed a system integrating artificial skin with control algorithms, which they used to create the first autonomous humanoid robot with full-body synthetic skin. Researchers at the Technical University of Munich in Germany have designed a system integrating artificial skin with control algorithms, which they used to create the first autonomous humanoid robot with full-body synthetic skin. The skin is composed of hexagonal cells about an inch in diameter, each with a microprocessor and sensors to measure pressure, acceleration, proximity, and temperature. The researchers use an event-based system to track the cells instead of continuous monitoring, with individual cells only sending data when values change; this cuts the processing load by up to 90%. Said the university's Gordon Cheng, "Our system is designed to work trouble-free and quickly with all kinds of robots. Now we're working to create smaller skin cells with the potential to be produced in larger numbers."