Fusing novelty and surprise for evolving robot morphologies

Traditional evolutionary algorithms tend to converge to a single good solution, which can limit their chance of discovering more diverse and creative outcomes. Divergent search, on the other hand, aims to counter convergence to local optima by avoiding selection pressure towards the objective. Forms of divergent search such as novelty or surprise search have proven to be beneficial for both the efficiency and the variety of the solutions obtained in deceptive tasks. Importantly for this paper, early results in maze navigation have shown that combining novelty and surprise search yields an even more effective search strategy due to their orthogonal nature. Motivated by the largely unexplored potential of coupling novelty and surprise as a search strategy, in this paper we investigate how fusing the two can affect the evolution of soft robot morphologies. We test the capacity of the combined search strategy against objective, novelty, and surprise search, by comparing their efficiency and robustness, and the variety of robots they evolve. Our key results demonstrate that novelty-surprise search is generally more efficient and robust across eight different resolutions. Further, surprise search explores the space of robot morphologies more broadly than any other algorithm examined.peer-reviewe

상상 모델: 구성 패턴 생성 네트워크의 다양성 탐색을 통한 이미지 제작

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2019. 2. 문병로.Divergent Search methods are devised to resolve the problem falling into a trap of local optima, an arch-enemy of stochastic optimization algorithms. Novelty Search and Surprise Search, inter alia, use the concept of {\it behavior} and explore behavior space defined by it, maintaining evolutionary divergence and they have shown great performance in this respect. Moreover, coupling novelty and surprise concept was designed based on ideas that those two algorithms search behavioral space in a different way. The combination of two algorithms can be viewed as multiobjective optimization algorithm, and this approach enhanced the performance than using one divergent search method only. Since several divergent search methods have outperformed existing stochastic optimization algorithms in recent studies of robotics, it has been applied to many other domains, such as robot morphology, artificial life and generating images. Particularly, the Innovation Engines applied Novelty Search to image generating method so as to create novel and interesting images. In this paper, we propose Imagination Model that adopts Novelty-Surprise Search which is the combination of Novelty and Surprise Search instead of pure Novelty Search, as an extension of Innovation Engine. Evolutionary algorithms using Novelty Search, Surprise Search, Novelty-Surprise Search are compared via well-trained deep neural networks defining the behaviors of individuals in terms of creating interesting images. Results of experiments indicate that Novelty-Surprise Search outperforms Novelty Search and Surprise Search even in image domainit searches and explores vast behavioral space more extensively than each search algorithm on its own.다양성 검색 방법은 확률적 최적화 알고리즘의 주적인 지역 최적해의 함정에 빠지는 문제를 해결하기 위해 고안되었다. 그중에서도 참신함 탐색과 놀라움 탐색은 {\it 행동}이라는 개념과 그 개념이 정의하는 행동 공간을 탐색하며 진화적 다양성을 유지했고 이 점에 있어서 훌륭한 성능을 보여주었다. 그뿐만 아니라 두 다양성 탐색이 서로 다른 방식으로 행동 공간을 탐색하는 데에서 착안하여, 참신함과 놀라움을 결합하는 알고리즘이 설계되었다. 두 알고리즘의 조합은 다목적 최적화 알고리즘으로 간주할 수 있는데, 이 접근 방식은 둘 중 하나만의 다양성 탐색 방법을 사용할 때보다 성능이 개선됨을 다양한 연구에서 보여주었다. 이처럼 여러 다양성 탐색이 기존의 확률적 최적화 알고리즘을 뛰어 넘는 성능을 보였기 때문에, 로봇 형태학, 인공생명, 이미지 생성처럼 다양한 분야에 응용되어왔다. 특히, 혁신 엔진은 새로우면서도 흥미로운 이미지를 창조하기 위해 이미지 생성 방법에 참신함 탐색을 적용했다. 이에 더해 우리는 이 논문에서 상상 모델을 제안한다. 이 상상 모델은 혁신 엔진의 확장으로서 순수한 참신함 탐색 대신 참신함 탐색과 놀라움 탐색을 결합한 참신함-놀라움 탐색을 도입한다. 참신함 탐색, 놀라움 탐색 그리고 참신함-놀라움 탐색을 사용한 진화 연산을 이미지 생성에 관한 측면에서 비교하는 실험을 진행하며, 이들은 모두 심층 인공신경망을 통해 그들이 사용하는 행동이라는 개념이 정의된다. 실험 결과를 살펴보면, 참신함-놀라움 탐색은 단순히 참신함 탐색이나 놀라움 탐색 각각을 따로따로 사용하는 것보다 더 넓은 행동 공간을 더 광범위하게 탐색하는 모습을 보여주었다. 이로부터, 다른 분야뿐 아니라 이미지 생성 영역에서도 참신함-놀라움 탐색이 참신함 탐색과 놀라움 탐색 각각을 뛰어넘는 성능을 보인다는 것을 확인하였다.Abstract i Contents iii List of Figures v List of Tables vi Chapter 1 Introduction 1 Chapter 2 Background 4 2.1 CPPN-NEAT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Novelty Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3 Surprise Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.4 Combining Novelty and Surprise Score . . . . . . . . . . . . . . . . . . . 7 2.5 Innovation Engines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Chapter 3 Methods 9 3.1 Image Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2 Behavioral Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.3 Imagination Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Chapter 4 Experiments 13 4.1 Fitness Measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.2 Deep Neural Networks and Dataset . . . . . . . . . . . . . . . . . . . . . . 14 Chapter 5 Results 16 Chapter 6 Discussion 25 Chapter 7 Conclusion 27 Bibliography 29 요약 33Maste

Open-ended Search through Minimal Criterion Coevolution

Search processes guided by objectives are ubiquitous in machine learning. They iteratively reward artifacts based on their proximity to an optimization target, and terminate upon solution space convergence. Some recent studies take a different approach, capitalizing on the disconnect between mainstream methods in artificial intelligence and the field\u27s biological inspirations. Natural evolution has an unparalleled propensity for generating well-adapted artifacts, but these artifacts are decidedly non-convergent. This new class of non-objective algorithms induce a divergent search by rewarding solutions according to their novelty with respect to prior discoveries. While the diversity of resulting innovations exhibit marked parallels to natural evolution, the methods by which search is driven remain unnatural. In particular, nature has no need to characterize and enforce novelty; rather, it is guided by a single, simple constraint: survive long enough to reproduce. The key insight is that such a constraint, called the minimal criterion, can be harnessed in a coevolutionary context where two populations interact, finding novel ways to satisfy their reproductive constraint with respect to each other. Among the contributions of this dissertation, this approach, called minimal criterion coevolution (MCC), is the primary (1). MCC is initially demonstrated in a maze domain (2) where it evolves increasingly complex mazes and solutions. An enhancement to the initial domain (3) is then introduced, allowing mazes to expand unboundedly and validating MCC\u27s propensity for open-ended discovery. A more natural method of diversity preservation through resource limitation (4) is introduced and shown to maintain population diversity without comparing genetic distance. Finally, MCC is demonstrated in an evolutionary robotics domain (5) where it coevolves increasingly complex bodies with brain controllers to achieve principled locomotion. The overall benefit of these contributions is a novel, general, algorithmic framework for the continual production of open-ended dynamics without the need for a characterization of behavioral novelty

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Constructing Participatory Environments: a Behavioural Model for Design

This thesis proposes the design of cybernetic frameworks that attempt to explore architecture as ecology of interacting systems that move beyond the fixed and finite tendencies of the past towards spatial environments that are adaptive, emotive and behavioural. Environments within this framework are attempts to construct interaction scenarios that enable agency, curiosity and play, forging intimate exchanges that are participatory and evolving over time. Interaction understood as the evolving relationships between things allows a generative and time-based framework to explore space as a model of interfacing that shifts the tendencies of passive occupancy towards an active ecology of interacting agents. The work argued here moves away from known models that reinforce habitual responses within architecture, towards an understanding of adaptive systems that are active agents for communication and exploration. Architecture within the context of this thesis is explored as a medium for spatial interfacing. Design is thus considered as durational, realtime and anticipatory exploring human human, human machine, and machine machine communication. The challenge posed is how designers can construct environments that are shared, enable curiosity, evolve and allow for complex interactions to arise through human and non-human agency. Attention thus is placed on behavioural features that afford conversational rich exchanges between participants and system, participants with other participants and or systems with other systems. This evolving framework demands that design systems have the capacity to participate and enable new forms of communication. Beyond conventional models that are reactive in their definition of interaction, architecture here moves towards features that are life-like, machine learned, and emotively communicated. The thesis demonstrates and articulates concepts of participation and behaviour through authored prototypes and real-time experiments. Behaviour is not relegated to a generative process in the design phase; rather it is time-based and conversational constantly constructing models of and for communication

Design Transactions

Design Transactions presents the outcome of new research to emerge from ‘Innochain’, a consortium of six leading European architectural and engineering-focused institutions and their industry partners. The book presents new advances in digital design tooling that challenge established building cultures and systems. It offers new sustainable and materially smart design solutions with a strong focus on changing the way the industry thinks, designs, and builds our physical environment. Divided into sections exploring communication, simulation and materialisation, Design Transactions explores digital and physical prototyping and testing that challenges the traditional linear construction methods of incremental refinement. This novel research investigates ‘the digital chain’ between phases as an opportunity for extended interdisciplinary design collaboration. The highly illustrated book features work from 15 early-stage researchers alongside chapters from world-leading industry collaborators and academics

Integrated material practice in free-form timber structures

Integrated material practice in free-form timber structures is a practice-led research project at CITA (Centre for IT and Architecture) that develops a digitally-augmented material practice around glue-laminated timber. The project is part of the InnoChain ETN and has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642877. The advent of digital tools and computation has shifted the focus of many material practices from the shaping of material to the shaping of information. The ability to process large amounts of data quickly has made computation commonplace in the design and manufacture of buildings, especially in iterative digital design workflows. The simulation of material performance and the shift from models as representational tools to functional ones has opened up new methods of working between digital model and physical material. Wood has gained a new relevance in contemporary construction because it is sustainable, renewable, and stores carbon. In light of the climate crisis and concerns about overpopulation, and coupled with developments in adhesives and process technology, it is returning to the forefront of construction. However, as a grown and heterogeneous material, its properties and behaviours nevertheless present barriers to its utilization in architecturally demanding areas. Similarly, the integration of the properties, material behaviours, and production constraints of glue-laminated timber (glulam) assemblies into early-stage architectural design workflows remains a challenging specialist and inter-disciplinary affair. Drawing on a partnership with Dsearch – the digital research network at White Arkitekter in Sweden – and Blumer Lehmann AG – a leading Swiss timber contractor – this research examines the design and fabrication of glue-laminated timber structures and seeks a means to link industrial timber fabrication with early-stage architectural design through the application of computational modelling, design, and an interrogation of established timber production processes. A particular focus is placed on large-scale free-form glulam structures due to their high performance demands and the challenge of exploiting the bending properties of timber. By proposing a computationally-augmented material practice in which design intent is informed by material and fabrication constraints, the research aims to discover new potentials in timber architecture. The central figure in the research is the glulam blank - the glue-laminated near-net shape of large-scale timber components. The design space that the blank occupies - between sawn, graded lumber and the finished architectural component - holds the potential to yield new types of timber components and new structural morphologies. Engaging with this space therefore requires new interfaces for design modelling and production that take into account the affordances of timber and timber processing. The contribution of this research is a framework for a material practice that integrates processes of computational modelling, architectural design, and timber fabrication and acts as a broker between domains of architectural design and industrial timber production. The research identifies four different notions of feedback that allow this material practice to form