Cellular automata (CA) captivate researchers due to teh emergent, complex individualized behavior that simple global rules of interaction enact. Recent advances in the field have combined CA with convolutional neural networks to achieve self-regenerating images. This new branch of CA is called neural cellular automata [1]. The goal of this project is to use the idea of idea of neural cellular automata to grow prediction machines. We place many different convolutional neural networks in a grid. Each conv net cell outputs a prediction of what the next state will be, and minimizes predictive error. Cells received their neighbors' colors and fitnesses as input. Each cell's fitness score described how accurate its predictions were. Cells could also move to explore their environment and some stochasticity was applied to movement.

在过去十年中，我们目睹了深度学习的兴起，以占据人工智能领域。人工神经网络的进步与具有大的内存容量大的硬件加速器的相应进步，以及大型数据集的可用性，使能研究人员和从业者能够培训和部署复杂的神经网络模型，这些模型在几个方面实现了最先进的性能跨越计算机视觉，自然语言处理和加强学习的领域。然而，由于这些神经网络变得更大，更复杂，更广泛地使用，目前深度学习模型的基本问题变得更加明显。已知最先进的深度学习模型遭受稳健性不良，无法适应新的任务设置的问题，以要求刚性和不灵活的配置假设。来自集体智能的想法，特别是来自复杂系统，如自组织，紧急行为，群优化和蜂窝系统的复杂系统的概念倾向于产生鲁棒，适应性，并且对环境配置具有较小的刚性假设的解决方案。因此，很自然地看到这些想法纳入更新的深度学习方法。在这篇综述中，我们将提供神经网络研究的历史背景，即神经网络研究的复杂系统的参与，并突出了现代深度学习研究中的几个活跃区域，这些研究融合了集体智能的原则，以推进其当前能力。为了促进双向思想流动，我们还讨论了利用现代深度学习模型的工作，以帮助推进复杂的系统研究。我们希望这次审查可以作为复杂系统和深度学习社区之间的桥梁，以促进思想的交叉授粉和促进跨学科的新合作。

具有自我分类的能力的材料有可能推进广泛的工程应用和行业。生物系统不仅具有自我调查的能力，而且还具有自我分类以确定一般形状和功能的能力。模块化机器人系统系统的先前工作仅使自我认识和自我授权成为特定的目标形状，缺少自然界中的固有稳健性。因此，在本文中，我们利用了深度学习和神经细胞自动机的最新进展，并提出了一个简单的模块化2D机器人系统，该系统可以通过其组件的局部通信来推断其自己的形状类别。此外，我们证明我们的系统可以成功地转移到硬件上，从而为未来的自我分类机提供了机会。可在https://github.com/kattwalker/projectCube上获得代码。视频可在https://youtu.be/0tcoke4keyc上找到。

为了测试一类深神经网络的泛化能力，我们基于John Conway的生活游戏，我们随机生成了2-D蜂窝自动机（CA）的大量不同规则集。使用这些规则，我们为每个CA实例计算多个轨迹。具有短路和长范围跳过连接的深度卷积编码器 - 解码器网络在各种生成的CA轨迹上培训，以预测给出其先前的州的下一个CA状态。结果表明，该网络能够学习各种，复杂的蜂窝自动机的规则，并概括到看不见的配置。在某种程度上，该网络显示统治集的概括和培训期间没有看到的邻域大小。重现实验的代码是公开可用的：https://github.com/slampai/一一化 - 细胞 - automata

Social insects such as ants communicate via pheromones which allows them to coordinate their activity and solve complex tasks as a swarm, e.g. foraging for food. This behaviour was shaped through evolutionary processes. In computational models, self-coordination in swarms has been implemented using probabilistic or action rules to shape the decision of each agent and the collective behaviour. However, manual tuned decision rules may limit the behaviour of the swarm. In this work we investigate the emergence of self-coordination and communication in evolved swarms without defining any rule. We evolve a swarm of agents representing an ant colony. We use a genetic algorithm to optimize a spiking neural network (SNN) which serves as an artificial brain to control the behaviour of each agent. The goal of the colony is to find optimal ways to forage for food in the shortest amount of time. In the evolutionary phase, the ants are able to learn to collaborate by depositing pheromone near food piles and near the nest to guide its cohorts. The pheromone usage is not encoded into the network; instead, this behaviour is established through the optimization procedure. We observe that pheromone-based communication enables the ants to perform better in comparison to colonies where communication did not emerge. We assess the foraging performance by comparing the SNN based model to a rule based system. Our results show that the SNN based model can complete the foraging task more efficiently in a shorter time. Our approach illustrates that even in the absence of pre-defined rules, self coordination via pheromone emerges as a result of the network optimization. This work serves as a proof of concept for the possibility of creating complex applications utilizing SNNs as underlying architectures for multi-agent interactions where communication and self-coordination is desired.

Lenia is a family of cellular automata (CA) generalizing Conway's Game of Life to continuous space, time and states. Lenia has attracted a lot of attention because of the wide diversity of self-organizing patterns it can generate. Among those, some spatially localized patterns (SLPs) resemble life-like artificial creatures. However, those creatures are found in only a small subspace of the Lenia parameter space and are not trivial to discover, necessitating advanced search algorithms. We hypothesize that adding a mass conservation constraint could facilitate the emergence of SLPs. We propose here an extension of the Lenia model, called Flow Lenia, which enables mass conservation. We show a few observations demonstrating its effectiveness in generating SLPs with complex behaviors. Furthermore, we show how Flow Lenia enables the integration of the parameters of the CA update rules within the CA dynamics, making them dynamic and localized. This allows for multi-species simulations, with locally coherent update rules that define properties of the emerging creatures, and that can be mixed with neighbouring rules. We argue that this paves the way for the intrinsic evolution of self-organized artificial life forms within continuous CAs.

生物系统对形态损害非常强大，但人工系统（机器人）目前却不是。在本文中，我们介绍了一个基于神经细胞自动机的系统，其中运动机器人的进化，然后赋予能够通过基于梯度的训练从损害中再生其形态。因此，我们的方法结合了进化的好处，可以发现各种不同的机器人形态，以及通过可区别的更新规则对鲁棒性的监督培训的效率。所得的神经细胞自动机能够生长能够恢复超过80 \％功能的虚拟机器人，即使经过严重的形态损害。

近年来，游戏AI研究取得了巨大的突破，尤其是在增强学习（RL）中。尽管他们成功了，但基础游戏通常是通过自己的预设环境和游戏机制实现的，因此使研究人员难以创建不同的游戏环境。但是，测试RL代理对各种游戏环境的测试对于最近努力研究RL的概括并避免可能发生过度拟合的问题至关重要。在本文中，我们将Gridd呈现为游戏AI研究的新平台，该平台提供了高度可配置的游戏，不同的观察者类型和有效的C ++核心引擎的独特组合。此外，我们提出了一系列基线实验，以研究RL剂的不同观察构构和泛化能力的影响。

在生存的背景下，可以单独繁殖在我们的机器中产生智力吗？在这项工作中，自我复制是在现代学习环境中出现智能行为的一种机制。通过纯粹专注于生存，在进行自然选择的同时，进化的生物被证明会产生有意义的，复杂和聪明的行为，从而在没有任何奖励或目标概念的情况下向挑战性问题展示了创造性的解决方案。Atari和机器人学习环境是根据自然选择重新定义的，在这些实验过程中自我复制生物中出现的行为进行了详细描述。

加强学习的最新进展（RL）已开始生产能够解决复杂环境分布的通常能力的代理。这些试剂通常在固定的，人为实现的环境上进行测试。另一方面，质量多样性（QD）优化已被证明是环境生成算法的有效组成部分，该算法可以产生多种多样的最终代理行为的高质量环境集合。但是，这些算法需要在新生成的环境上对代理的潜在昂贵模拟。我们提出了深层替代辅助生成环境（DSAGE），这是一种样本效率的QD环境生成算法，该算法保持了一个深层的替代模型，用于预测新环境中的试剂行为。结果有两个基准域，表明DSAGE明显优于现有的QD环境生成算法，这些算法在发现了引起最先进的RL代理商和计划代理的各种行为的环境集合中。

尖峰神经网络（SNN）引起了脑启发的人工智能和计算神经科学的广泛关注。它们可用于在多个尺度上模拟大脑中的生物信息处理。更重要的是，SNN是适当的抽象水平，可以将大脑和认知的灵感带入人工智能。在本文中，我们介绍了脑启发的认知智力引擎（Braincog），用于创建脑启发的AI和脑模拟模型。 Braincog将不同类型的尖峰神经元模型，学习规则，大脑区域等作为平台提供的重要模块。基于这些易于使用的模块，BrainCog支持各种受脑启发的认知功能，包括感知和学习，决策，知识表示和推理，运动控制和社会认知。这些受脑启发的AI模型已在各种受监督，无监督和强化学习任务上有效验证，并且可以用来使AI模型具有多种受脑启发的认知功能。为了进行大脑模拟，Braincog实现了决策，工作记忆，神经回路的结构模拟以及小鼠大脑，猕猴大脑和人脑的整个大脑结构模拟的功能模拟。一个名为BORN的AI引擎是基于Braincog开发的，它演示了如何将Braincog的组件集成并用于构建AI模型和应用。为了使科学追求解码生物智能的性质并创建AI，Braincog旨在提供必要且易于使用的构件，并提供基础设施支持，以开发基于脑部的尖峰神经网络AI，并模拟认知大脑在多个尺度上。可以在https://github.com/braincog-x上找到Braincog的在线存储库。

为了在专门的神经形态硬件中进行节能计算，我们提出了尖峰神经编码，这是基于预测性编码理论的人工神经模型家族的实例化。该模型是同类模型，它是通过在“猜测和检查”的永无止境过程中运行的，神经元可以预测彼此的活动值，然后调整自己的活动以做出更好的未来预测。我们系统的互动性，迭代性质非常适合感官流预测的连续时间表述，并且如我们所示，模型的结构产生了局部突触更新规则，可以用来补充或作为在线峰值定位的替代方案依赖的可塑性。在本文中，我们对模型的实例化进行了实例化，该模型包括泄漏的集成和火灾单元。但是，我们系统所在的框架自然可以结合更复杂的神经元，例如Hodgkin-Huxley模型。我们在模式识别方面的实验结果证明了当二进制尖峰列车是通信间通信的主要范式时，模型的潜力。值得注意的是，尖峰神经编码在分类绩效方面具有竞争力，并且在从任务序列中学习时会降低遗忘，从而提供了更经济的，具有生物学上的替代品，可用于流行的人工神经网络。

Agent-based modeling (ABM) is a well-established paradigm for simulating complex systems via interactions between constituent entities. Machine learning (ML) refers to approaches whereby statistical algorithms 'learn' from data on their own, without imposing a priori theories of system behavior. Biological systems -- from molecules, to cells, to entire organisms -- consist of vast numbers of entities, governed by complex webs of interactions that span many spatiotemporal scales and exhibit nonlinearity, stochasticity and intricate coupling between entities. The macroscopic properties and collective dynamics of such systems are difficult to capture via continuum modelling and mean-field formalisms. ABM takes a 'bottom-up' approach that obviates these difficulties by enabling one to easily propose and test a set of well-defined 'rules' to be applied to the individual entities (agents) in a system. Evaluating a system and propagating its state over discrete time-steps effectively simulates the system, allowing observables to be computed and system properties to be analyzed. Because the rules that govern an ABM can be difficult to abstract and formulate from experimental data, there is an opportunity to use ML to help infer optimal, system-specific ABM rules. Once such rule-sets are devised, ABM calculations can generate a wealth of data, and ML can be applied there too -- e.g., to probe statistical measures that meaningfully describe a system's stochastic properties. As an example of synergy in the other direction (from ABM to ML), ABM simulations can generate realistic datasets for training ML algorithms (e.g., for regularization, to mitigate overfitting). In these ways, one can envision various synergistic ABM$\rightleftharpoons$ML loops. This review summarizes how ABM and ML have been integrated in contexts that span spatiotemporal scales, from cellular to population-level epidemiology.

在移动机器人学中，区域勘探和覆盖率是关键能力。在大多数可用研究中，共同的假设是全球性，远程通信和集中合作。本文提出了一种新的基于群的覆盖控制算法，可以放松这些假设。该算法组合了两个元素：Swarm规则和前沿搜索算法。受到大量简单代理（例如，教育鱼，植绒鸟类，蜂拥昆虫）的自然系统的启发，第一元素使用三个简单的规则来以分布式方式维持群体形成。第二元素提供了选择有希望区域以使用涉及代理的相对位置的成本函数的最小化来探索（和覆盖）的装置。我们在不同环境中测试了我们的方法对异质和同质移动机器人的性能。我们衡量覆盖性能和允许本集团维持沟通的覆盖性能和群体形成统计数据。通过一系列比较实验，我们展示了拟议的策略在最近提出的地图覆盖方法和传统的人工潜在领域基于细胞覆盖，转变和安全路径的百分比，同时保持允许短程的形成沟通。

多目标自组织追求（SOP）问题已广泛应用，并被认为是一个充满挑战的分布式系统的自组织游戏，在该系统中，智能代理在其中合作追求具有部分观察的多个动态目标。这项工作为分散的多机构系统提出了一个框架，以提高智能代理的搜索和追求能力。我们将一个自组织的系统建模为可观察到的马尔可夫游戏（POMG），具有权力下放，部分观察和非通信的特征。然后将拟议的分布式算法：模糊自组织合作协同进化（FSC2）杠杆化，以解决多目标SOP中的三个挑战：分布式自组织搜索（SOS），分布式任务分配和分布式单目标追踪。 FSC2包括一种协调的多代理深钢筋学习方法，该方法使均匀的代理能够学习天然SOS模式。此外，我们提出了一种基于模糊的分布式任务分配方法，该方法将多目标SOP分解为几个单目标追求问题。合作进化原则用于协调每个单一目标问题的分布式追随者。因此，可以缓解POMG中固有的部分观察和分布式决策的不确定性。实验结果表明，在所有三个子任务中，分布式不传动的多机构协调都具有部分观察结果，而2048 FSC2代理可以执行有效的多目标SOP，其捕获率几乎为100％。

Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Accordingly, techniques that enable efficient processing of DNNs to improve energy efficiency and throughput without sacrificing application accuracy or increasing hardware cost are critical to the wide deployment of DNNs in AI systems.This article aims to provide a comprehensive tutorial and survey about the recent advances towards the goal of enabling efficient processing of DNNs. Specifically, it will provide an overview of DNNs, discuss various hardware platforms and architectures that support DNNs, and highlight key trends in reducing the computation cost of DNNs either solely via hardware design changes or via joint hardware design and DNN algorithm changes. It will also summarize various development resources that enable researchers and practitioners to quickly get started in this field, and highlight important benchmarking metrics and design considerations that should be used for evaluating the rapidly growing number of DNN hardware designs, optionally including algorithmic co-designs, being proposed in academia and industry.The reader will take away the following concepts from this article: understand the key design considerations for DNNs; be able to evaluate different DNN hardware implementations with benchmarks and comparison metrics; understand the trade-offs between various hardware architectures and platforms; be able to evaluate the utility of various DNN design techniques for efficient processing; and understand recent implementation trends and opportunities.

这本数字本书包含在物理模拟的背景下与深度学习相关的一切实际和全面的一切。尽可能多，所有主题都带有Jupyter笔记本的形式的动手代码示例，以便快速入门。除了标准的受监督学习的数据中，我们将看看物理丢失约束，更紧密耦合的学习算法，具有可微分的模拟，以及加强学习和不确定性建模。我们生活在令人兴奋的时期：这些方法具有从根本上改变计算机模拟可以实现的巨大潜力。

Imitation learning techniques aim to mimic human behavior in a given task. An agent (a learning machine) is trained to perform a task from demonstrations by learning a mapping between observations and actions. The idea of teaching by imitation has been around for many years, however, the field is gaining attention recently due to advances in computing and sensing as well as rising demand for intelligent applications. The paradigm of learning by imitation is gaining popularity because it facilitates teaching complex tasks with minimal expert knowledge of the tasks. Generic imitation learning methods could potentially reduce the problem of teaching a task to that of providing demonstrations; without the need for explicit programming or designing reward functions specific to the task. Modern sensors are able to collect and transmit high volumes of data rapidly, and processors with high computational power allow fast processing that maps the sensory data to actions in a timely manner. This opens the door for many potential AI applications that require real-time perception and reaction such as humanoid robots, self-driving vehicles, human computer interaction and computer games to name a few. However, specialized algorithms are needed to effectively and robustly learn models as learning by imitation poses its own set of challenges. In this paper, we survey imitation learning methods and present design options in different steps of the learning process. We introduce a background and motivation for the field as well as highlight challenges specific to the imitation problem. Methods for designing and evaluating imitation learning tasks are categorized and reviewed. Special attention is given to learning methods in robotics and games as these domains are the most popular in the literature and provide a wide array of problems and methodologies. We extensively discuss combining imitation learning approaches using different sources and methods, as well as incorporating other motion learning methods to enhance imitation. We also discuss the potential impact on industry, present major applications and highlight current and future research directions.

在这项工作中，我们认为寻找人工通用智能（AGI）应该从比人类水平的智能低得多的水平开始。自然界中智能行为的环境是由于有机体与周围环境相互作用的情况，这种环境可能会随着时间的流逝而改变，并对有机体施加压力，以便学习新的行为或环境模型。我们的假设是，学习是通过解释代理在环境中作用时的感觉反馈而发生的。为此，需要一个身体和反应性环境。我们评估了一种进化生物学启发的人工神经网络的方法，该神经网络从名为“人工通用智能的神经进化”（Nagi）的环境反应中学习，这是一个低水平AGI的框架。该方法允许使用自适应突触的随机启用尖峰神经网络的进化络合，该神经网络控制在可变环境中实例化的代理。这种配置使我们能够基准基准控制器的适应性和通用性。可变环境中所选的任务是食品觅食，逻辑门的仿真和卡特杆平衡。这三个任务通过相当小的网络拓扑成功解决，因此，它打开了实验更复杂的任务和方案的可能性，其中课程学习是有益的。

Artificial life is a research field studying what processes and properties define life, based on a multidisciplinary approach spanning the physical, natural and computational sciences. Artificial life aims to foster a comprehensive study of life beyond "life as we know it" and towards "life as it could be", with theoretical, synthetic and empirical models of the fundamental properties of living systems. While still a relatively young field, artificial life has flourished as an environment for researchers with different backgrounds, welcoming ideas and contributions from a wide range of subjects. Hybrid Life is an attempt to bring attention to some of the most recent developments within the artificial life community, rooted in more traditional artificial life studies but looking at new challenges emerging from interactions with other fields. In particular, Hybrid Life focuses on three complementary themes: 1) theories of systems and agents, 2) hybrid augmentation, with augmented architectures combining living and artificial systems, and 3) hybrid interactions among artificial and biological systems. After discussing some of the major sources of inspiration for these themes, we will focus on an overview of the works that appeared in Hybrid Life special sessions, hosted by the annual Artificial Life Conference between 2018 and 2022.