Modern machine learning tasks often require considering not just one but multiple objectives. For example, besides the prediction quality, this could be the efficiency, robustness or fairness of the learned models, or any of their combinations. Multi-objective learning offers a natural framework for handling such problems without having to commit to early trade-offs. Surprisingly, statistical learning theory so far offers almost no insight into the generalization properties of multi-objective learning. In this work, we make first steps to fill this gap: we establish foundational generalization bounds for the multi-objective setting as well as generalization and excess bounds for learning with scalarizations. We also provide the first theoretical analysis of the relation between the Pareto-optimal sets of the true objectives and the Pareto-optimal sets of their empirical approximations from training data. In particular, we show a surprising asymmetry: all Pareto-optimal solutions can be approximated by empirically Pareto-optimal ones, but not vice versa.

Some of the computation paradigm is based on the ability to use physical proposed advantages of QML include access to larger feature properties such as entanglement or superposition, which allow spaces, more general and expressive models and algorithmic quantum bits or qubits, which are basic information units in improvements in model optimization. Our proposed quantum quantum computing, to be in more than one state at the same image classification methods utilize these advantages. We time (see Section II-A), allowing access to Hilbert spaces and optimize the models by using metaheuristic techniques to thus to spaces that may be infinite-dimensional H. Similar to automatically obtain the best predictions based on test data, classical computing, in which information is calculated based ensuring model robustness, and we automatically generate on electrical circuits and logic gates that operate on bits, in simple quantum-inspired machine learning classifiers that can quantum computing, quantum circuits composed of sequences easily be implemented on classical computers. of quantum gates are used to modify quantum states (see In addition to algorithms that are useful for future scalable Section II-B).

Unmanned aerial vehicles (UAVs) are being employed in many areas such as photography, emergency, entertainment, defence, agriculture, forestry, mining and construction. Over the last decade, UAV technology has found applications in numerous construction project phases, ranging from site mapping, progress monitoring, building inspection, damage assessments, and material delivery. While extensive studies have been conducted on the advantages of UAVs for various construction-related processes, studies on UAV collaboration to improve the task capacity and efficiency are still scarce. This paper proposes a new cooperative path planning algorithm for multiple UAVs based on the stag hunt game and particle swarm optimization (PSO). First, a cost function for each UAV is defined, incorporating multiple objectives and constraints. The UAV game framework is then developed to formulate the multi-UAV path planning into the problem of finding payoff-dominant equilibrium. Next, a PSO-based algorithm is proposed to obtain optimal paths for the UAVs. Simulation results for a large construction site inspected by three UAVs indicate the effectiveness of the proposed algorithm in generating feasible and efficient flight paths for UAV formation during the inspection task.

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision and policy making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This second paper presents a literature review of key enabling technologies of digital twins, with an emphasis on uncertainty quantification, optimization methods, open source datasets and tools, major findings, challenges, and future directions. Discussions focus on current methods of uncertainty quantification and optimization and how they are applied in different dimensions of a digital twin. Additionally, this paper presents a case study where a battery digital twin is constructed and tested to illustrate some of the modeling and twinning methods reviewed in this two-part review. Code and preprocessed data for generating all the results and figures presented in the case study are available on GitHub.

The field of evolutionary robotics uses principles of natural evolution to design robots. In this paper, we study the effect of adding a new module inspired by the skeletal muscle to the existing RoboGen framework: the linear actuator. Additionally, we investigate how robots evolved in a plain environment differ from robots evolved in a rough environment. We consider the task of directed locomotion for comparing evolved robot morphologies. The results show that the addition of the linear actuator does not have a significant impact on the performance and morphologies of robots evolved in a plain environment. However, we find significant differences in the morphologies of robots evolved in a plain environment and robots evolved in a rough environment. We find that more complex behavior and morphologies emerge when we change the terrain of the environment.

Search-based procedural content generation (PCG) is a well-known method for level generation in games. Its key advantage is that it is generic and able to satisfy functional constraints. However, due to the heavy computational costs to run these algorithms online, search-based PCG is rarely utilized for real-time generation. In this paper, we introduce mutation models, a new type of iterative level generator based on machine learning. We train a model to imitate the evolutionary process and use the trained model to generate levels. This trained model is able to modify noisy levels sequentially to create better levels without the need for a fitness function during inference. We evaluate our trained models on a 2D maze generation task. We compare several different versions of the method: training the models either at the end of evolution (normal evolution) or every 100 generations (assisted evolution) and using the model as a mutation function during evolution. Using the assisted evolution process, the final trained models are able to generate mazes with a success rate of 99% and high diversity of 86%. The trained model is many times faster than the evolutionary process it was trained on. This work opens the door to a new way of learning level generators guided by an evolutionary process, meaning automatic creation of generators with specifiable constraints and objectives that are fast enough for runtime deployment in games.

Nature is an inhabitant for enormous number of species. All the species do perform complex activities with simple and elegant rules for their survival. The property of emergence of collective behavior is remarkably supporting their activities. One form of the collective behaviour is the swarm intelligence -- all agents poses same rules and capabilities. This equality along with local cooperation in the agents tremendously leads to achieving global results. Some of the swarm behaviours in the nature includes birds formations , fish school maneuverings, ants movement. Recently, one school of research has studied these behaviours and proposed artificial paradigms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Glowworm Swarm Optimization (GSO) etc. Another school of research used these models and designed robotic platforms to detect (locate) multiple signal sources such as light, fire, plume, odour etc. Kinbots platform is one such recent experiment. In the same line of thought, this extended abstract presents the recently proposed butterfly inspired metaphor and corresponding simulations, ongoing experiments with outcomes.

Formation control problem is one of the most concerned topics within the realm of swarm intelligence, which is usually solved by conventional mathematical approaches. In this paper, however, we presents a metaheuristic approach that leverages a natural co-evolutionary strategy to solve the formation control problem for a swarm of missiles. The missile swarm is modeled by a second-order system with heterogeneous reference target, and exponential error function is made to be the objective function such that the swarm converge to optimal equilibrium states satisfying certain formation requirements. Focusing on the issue of local optimum and unstable evolution, we incorporate a novel model-based policy constraint and a population adaptation strategies that greatly alleviates the performance degradation. With application of the Molloy-Reed criterion in the field of network communication, we developed an adaptive topology method that assure the connectivity under node failure and its effectiveness are validated both theoretically and experimentally. Experimental results valid the effectiveness of the proposed formation control approach. More significantly, we showed that it is feasible to treat generic formation control problem as Markov Decision Process(MDP) and solve it through iterative learning.

Systematic development of accurate density functionals has been a decades-long challenge for scientists. Despite the emerging application of machine learning (ML) in approximating functionals, the resulting ML functionals usually contain more than tens of thousands parameters, which makes a huge gap in the formulation with the conventional human-designed symbolic functionals. We propose a new framework, Symbolic Functional Evolutionary Search (SyFES), that automatically constructs accurate functionals in the symbolic form, which is more explainable to humans, cheaper to evaluate, and easier to integrate to existing density functional theory codes than other ML functionals. We first show that without prior knowledge, SyFES reconstructed a known functional from scratch. We then demonstrate that evolving from an existing functional $\omega$B97M-V, SyFES found a new functional, GAS22 (Google Accelerated Science 22), that performs better for the majority of molecular types in the test set of Main Group Chemistry Database (MGCDB84). Our framework opens a new direction in leveraging computing power for the systematic development of symbolic density functionals.

In 1959, Arthur Samuel, a pioneer in the field of Artificial Intelligence defined the term Machine Learning [1] as the "field of study that gives computers the ability to learn without being explicitly programmed". In the field of Machine Learning, an important technique called Deep Learning allows "computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction" [2]. In recent years, many accurate decision support systems based on Deep Learning have been constructed as black boxes [3], that is as systems that hide their internal logic to the user. Thus, the purpose of an Explainable Artificial Intelligence [4-7] system is to make its behavior more intelligible to humans by providing explanations [8]. A popular approach to addressing the problem of opacity of black-box machine learning models is the use of post-hoc explainability methods: these methods approximate the logic of underlying machine learning models with the aim of explaining their internal workings, so that the user can understand them [9]. Unfortunately, these methods provide explanations that are not faithful to what the black-box model computes and can be misleading [10]. A recent and highly cited perspective [10] highlighted the need for white box models (i.e.