In evolutionary computation, the fitness function normally measures progress toward an objective in the search space, effectively acting as an objective function. Through deception, such objective functions may actually prevent the objective from being reached. While methods exist to mitigate deception, they leave the underlying pathology untreated: Objective functions themselves may actively misdirect search toward dead ends. This paper proposes an approach to circumventing deception that also yields a new perspective on open-ended evolution. Instead of either explicitly seeking an objective or modeling natural evolution to capture open-endedness, the idea is to simply search for behavioral novelty. Even in an objective-based problem, such novelty search ignores the objective. Because many points in the search space collapse to a single behavior, the search for novelty is often feasible. Furthermore, because there are only so many simple behaviors, the search for novelty leads to increasing complexity. By decoupling open-ended search from artificial life worlds, the search for novelty is applicable to real world problems. Counterintuitively, in the maze navigation and biped walking tasks in this paper, novelty search significantly outperforms objective-based search, suggesting the strange conclusion that some problems are best solved by methods that ignore the objective. The main lesson is the inherent limitation of the objective-based paradigm and the unexploited opportunity to guide search through other means.
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In most modern video games, character behavior is scripted; no matter how many times the player exploits a weakness, that weakness is never repaired. Yet, if game characters could learn through interacting with the player, behavior could improve as the game is played, keeping it interesting. This paper introduces the real-time Neuroevolution of Augmenting Topologies (rtNEAT) method for evolving increasingly complex artificial neural networks in real time, as a game is being played. The rtNEAT method allows agents to change and improve during the game. In fact, rtNEAT makes possible an entirely new genre of video games in which the player trains a team of agents through a series of customized exercises. To demonstrate this concept, the Neuroevolving Robotic Operatives (NERO) game was built based on rtNEAT. In NERO, the player trains a team of virtual robots for combat against other players' teams. This paper describes results from this novel application of machine learning, and demonstrates that rtNEAT makes possible video games like NERO where agents evolve and adapt in real time. In the future, rtNEAT may allow new kinds of educational and training applications through interactive and adapting games.
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生物塑料神经网络是由进化,发展和终身学习塑造的非凡计算能力的系统。这些元素的相互作用导致适应性行为和智能的出现。受这种错综复杂的自然现象的启发,演化塑料人工神经网络(EPANNs)使用模拟的计算机进化来培育具有多种动力学,结构和塑性规则的塑性神经网络:这些人工系统由输入,输出和塑料成分组成,响应环境中的经验。这些系统可以自主发现新的自适应算法,并导致对生物适应的出现的假设。 EPANN在过去二十年中取得了令人瞩目的进展。目前人工神经网络的科学和技术进步正在为全新的方法和结果设定条件。特别是,手工设计网络的限制可以通过更灵活和创新的解决方案来克服。本文汇集了各种鼓舞人心的理念,定义了EPANN领域。回顾了主要方法和结果。最后,提出了新的机遇和发展。
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A major challenge for evolutionary computation is to evolve phenotypes such as neural networks, sensory systems, or motor controllers at the same level of complexity as found in biological organisms. In order to meet this challenge, many researchers are proposing indirect encodings, that is, evolutionary mechanisms where the same genes are used multiple times in the process of building a phenotype. Such gene reuse allows compact representations of very complex phenotypes. Development is a natural choice for implementing indirect encodings, if only because nature itself uses this very process. Motivated by the development of embryos in nature, we define artificial embryogeny (AE) as the subdiscipline of evolutionary computation (EC) in which phenotypes undergo a developmental phase. An increasing number of AE systems are currently being developed, and a need has arisen for a principled approach to comparing and contrasting, and ultimately building, such systems. Thus, in this paper, we develop a principled taxonomy for AE. This taxonomy provides a unified context for long-term research in AE, so that implementation decisions can be compared and contrasted along known dimensions in the design space of embryogenic systems. It also allows predicting how the settings of various AE parameters affect the capacity to efficiently evolve complex phenotypes.
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人工智能(AI)的最新进展使人们重新建立了像人一样学习和思考的系统。许多进步来自于在对象识别,视频游戏和棋盘游戏等任务中使用端到端训练的深度神经网络,在某些方面实现了与人类相当的性能。尽管他们的生物灵感和性能成就,这些系统不同于人类智能的不规则方式。我们回顾了认知科学的进展,表明真正的人类学习和思维机器将不得不超越当前的工程学习趋势,以及他们如何学习它。具体而言,我们认为这些机器应该(a)构建世界的因果模型支持解释和理解,而不仅仅是解决模式识别问题; (b)在物理学和心理学的直觉理论中进行基础学习,以支持和丰富所学知识;以及(c)利用组合性和学习 - 学习快速获取知识并将其推广到新的任务和情境。我们建议针对这些目标的具体挑战和有希望的途径,这些目标可以将最近神经网络进步的强度与更结构化的认知模型结合起来。
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在本文中,我们回顾了进化计算中最先进的结果,并观察到我们不会在没有人为干预的情况下从刮擦中发展出非平凡的软件。考虑了许多可能的解释,但我们得出结论,问题的计算复杂性使其无法像当前尝试那样被解决。我们提供了对必要和可用计算资源的详细分析,以支持我们的发现。
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Composable Controllers for Physics-Based Character Animation An ambitious goal in the area of physics-based computer animation is the creation of virtual actors that autonomously synthesize realistic human motions and possess a broad repertoire of lifelike motor skills. To this end, the control of dynamic, anthropomorphic figures subject to gravity and contact forces remains a difficult open problem. We propose a framework for composing controllers in order to enhance the motor abilities of such figures. A key contribution of our composition framework is an explicit model of the "pre-conditions" under which motor controllers are expected to function properly. We demonstrate controller composition with preconditions determined not only manually, but also automatically based on Support Vector Machine (SVM) learning theory. We evaluate our composition framework using a family of controllers capable of synthesizing basic actions such as balance, protective stepping when balance is disturbed, protective arm reactions when falling, and multiple ways of standing up after a fall. We furthermore demonstrate these basic controllers working in conjunction with more dynamic motor skills within a two-dimensional and a three-dimensional prototype virtual stuntperson. Our composition framework promises to enable the community of physics-based animation practitioners to more easily exchange motor controllers and integrate them into dynamic characters. ii Dedication To my father, Nikolaos Faloutsos, my mother, Sofia Faloutsou, and my wife, Florine Tseu. iii Acknowledgements I am done! Phew! It feels great. I have to do one more thing and that is to write the acknowledgements, one of the most important parts of a PhD thesis. The educational process of working towards a PhD degree teaches you, among other things, how important the interaction and contributions of the other people are to your career and personal development. First, I would like to thank my supervisors, Michiel van de Panne and Demetri Terzopoulos, for everything they did for me. And it was a lot. You have been the perfect supervisors. THANK YOU! However, I will never forgive Michiel for beating me at a stair-climbing race during a charity event that required running up the CN Tower stairs. Michiel, you may have forgotten, but I haven't! I am grateful to my external appraiser, Jessica Hodgins, and the members of my supervisory committee, Ken Jackson, Alejo Hausner and James Stewart, for their contribution to the successful completion of my degree. I would like to thank my close collaborator, Victor Ng-Thow-Hing, for being the richest source of knowledge on graphics research, graphics technology, investing and martial arts movies. Too bad you do not like Jackie Chan, Victor. A great THANKS is due to Joe Laszlo, the heart and soul of our lab's community spirit. Joe practically ran our lab during some difficult times. He has spent hours of his time to ensure the smooth operation of the lab and its equip
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This paper investigates how an evolutionary algorithm with an indirect encoding exploits the property of phenotypic regularity, an important design principle found in natural organisms and engineered designs. We present the first comprehensive study showing that such phenotypic regularity enables an indirect encoding to outperform direct encoding controls as problem regularity increases. Such an ability to produce regular solutions that can exploit the regularity of problems is an important prerequisite if evolutionary algorithms are to scale to high-dimensional real-world problems, which typically contain many regularities, both known and unrecognized. The indirect encoding in this case study is HyperNEAT, which evolves artificial neural networks (ANNs) in a manner inspired by concepts from biological development. We demonstrate that, in contrast to two direct encoding controls, HyperNEAT produces both regular behaviors and regular ANNs, which enables HyperNEAT to significantly outperform the direct encodings as regularity increases in three problem domains. We also show that the types of regularities HyperNEAT produces can be biased, allowing domain knowledge and preferences to be injected into the search. Finally, we examine the downside of a bias toward regularity. Even when a solution is mainly regular, some irregularity may be needed to perfect its functionality. This insight is illustrated by a new algorithm called HybrID that hybridizes indirect and direct encodings, which matched HyperNEAT's performance on regular problems yet outperformed it on problems with some irregularity. HybrID's ability to improve upon the performance of HyperNEAT raises the question of whether indirect encodings may ultimately excel not as stand-alone algorithms, but by being hybridized with a further process of refinement, wherein the indirect encoding produces patterns that exploit problem regularity and the refining process modifies that pattern to capture irregularities. This paper thus paints a more complete picture of indirect encodings than prior studies because it analyzes the impact of the continuum between irregularity and regularity on the performance of such encodings, and ultimately suggests a path forward that combines indirect encodings with a separate process of refinement.
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This paper describes a system for the evolution and co-evolution of virtual creatures that compete in physically simulated three-dimensional worlds. Pairs of individuals enter one-on-one contests in which they contend to gain control of a common resource. The winners receive higher relative fitness scores allowing them to survive and reproduce. Realistic dynamics simulation including gravity, collisions, and friction, restricts the actions to physically plausible behaviors. The morphology of these creatures and the neural systems for controlling their muscle forces are both genetically determined, and the morphology and behavior can adapt to each other as they evolve simultaneously. The genotypes are structured as directed graphs of nodes and connections, and they can efficiently but flexibly describe instructions for the development of creatures' bodies and control systems with repeating or recursive components. When simulated evolutions are performed with populations of competing creatures, interesting and diverse strategies and counter-strategies emerge.
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自然进化给人的印象是导致增加多样性和复杂性的开放式过程。如果我们的目标是人为地产生这样的开放性,那么这表明了一种由进化隐喻驱动的方法。另一方面,机器学习和人工智能技术通常被认为过于狭窄,无法提供与进化相关的探索性动力学。在本文中,我们希望通过回顾在演化启发的方法中开放式的常见障碍以及它们如何在进化案例中处理 - 大学崩溃,复杂性的饱和以及未能形成新的个体性来弥合这一差距。然后,我们展示了这些问题如何映射到机器学习方法中的类似问题,并讨论如何可以移除缓解进化方法中的这些障碍的相同见解和解决方案。同时,这些问题在机器学习制定中所采用的形式表明新的分析和解决开放性障碍的方法。最终,我们希望激励研究人员能够以可变的方式使用基于进化和梯度下降的机器学习方法来设计和创建开放式系统。
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This paper contains a modern vision of the paral-lelization techniques used for evolutionary algorithms (EAs). The work is motivated by two fundamental facts: first, the different families of EAs have naturally converged in the last decade while parallel EAs (PEAs) seem still to lack unified studies, and second, there is a large number of improvements in these algorithms and in their parallelization that raise the need for a comprehensive survey. We stress the differences between the EA model and its parallel implementation throughout the paper. We discuss the advantages and drawbacks of PEAs. Also, successful applications are mentioned and open problems are identified. We propose potential solutions to these problems and classify the different ways in which recent results in theory and practice are helping to solve them. Finally, we provide a highly structured background relating PEAs in order to make researchers aware of the benefits of decentralizing and par-allelizing an EA.
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Developmental robotics is an emerging field located at the intersection of robotics, cognitive science and developmental sciences. This paper elucidates the main reasons and key motivations behind the convergence of fields with seemingly disparate interests, and shows why developmental robotics might prove to be beneficial for all fields involved. The methodology advocated is synthetic and two-pronged: on the one hand, it employs robots to instantiate models originating from developmental sciences; on the other hand, it aims to develop better robotic systems by exploiting insights gained from studies on ontogenetic development. This paper gives a survey of the relevant research issues and points to some future research directions.
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A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: We take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. We believe that this equation formally captures the concept of machine intelligence in the broadest reasonable sense. We then show how this formal definition is related to the theory of universal optimal learning agents. Finally, we survey the many other tests and definitions of intelligence that have been proposed for machines.
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This paper describes a novel system for creating virtual creatures that move and behave in simulated three-dimensional physical worlds. The morphologies of creatures and the neural systems for controlling their muscle forces are both generated automatically using genetic algorithms. Different fitness evaluation functions are used to direct simulated evolutions towards specific behaviors such as swimming, walking, jumping, and following. A genetic language is presented that uses nodes and connections as its primitive elements to represent directed graphs, which are used to describe both the morphology and the neural circuitry of these creatures. This genetic language defines a hyperspace containing an indefinite number of possible creatures with behaviors, and when it is searched using optimization techniques, a variety of successful and interesting locomotion strategies emerge, some of which would be difficult to invent or build by design.
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More than a decade after the first extensive overview on parameter control, we revisit the field and present a survey of the state of the art. We briefly summarise the development of the field and discuss existing work related to each major parameter or component of an evolutionary algorithm. Based on this overview we observe trends in the area, identify some (methodological) shortcomings, and give recommendations for future research.
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Cooperative multi-agent systems (MAS) are ones in which several agents attempt, through their interaction, to jointly solve tasks or to maximize utility. Due to the interactions among the agents, multi-agent problem complexity can rise rapidly with the number of agents or their behavioral sophistication. The challenge this presents to the task of programming solutions to MAS problems has spawned increasing interest in machine learning techniques to automate the search and optimization process. We provide a broad survey of the cooperative multi-agent learning literature. Previous surveys of this area have largely focused on issues common to specific subareas (for example, reinforcement learning, RL or robotics). In this survey we attempt to draw from multi-agent learning work in a spectrum of areas, including RL, evolutionary computation, game theory, complex systems, agent modeling, and robotics. We find that this broad view leads to a division of the work into two categories, each with its own special issues: applying a single learner to discover joint solutions to multi-agent problems (team learning), or using multiple simultaneous learners, often one per agent (concurrent learning). Additionally, we discuss direct and indirect communication in connection with learning, plus open issues in task decomposition, scalability, and adaptive dynamics. We conclude with a presentation of multi-agent learning problem domains, and a list of multi-agent learning resources.
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Monte Carlo Tree Search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarise the results from the key game and non-game domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work.
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Algorithmic composition is the partial or total automation of the process of music composition by using computers. Since the 1950s, different computational techniques related to Artificial Intelligence have been used for algorithmic composition, including grammatical representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint programming and evolutionary algorithms. This survey aims to be a comprehensive account of research on algorithmic composition, presenting a thorough view of the field for researchers in Artificial Intelligence.
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