7 research outputs found
Lenia and Expanded Universe
We report experimental extensions of Lenia, a continuous cellular automata
family capable of producing lifelike self-organizing autonomous patterns. The
rule of Lenia was generalized into higher dimensions, multiple kernels, and
multiple channels. The final architecture approaches what can be seen as a
recurrent convolutional neural network. Using semi-automatic search e.g.
genetic algorithm, we discovered new phenomena like polyhedral symmetries,
individuality, self-replication, emission, growth by ingestion, and saw the
emergence of "virtual eukaryotes" that possess internal division of labor and
type differentiation. We discuss the results in the contexts of biology,
artificial life, and artificial intelligence.Comment: 8 pages, 5 figures, 1 table; submitted to ALIFE 2020 conferenc
Complexity, Development, and Evolution in Morphogenetic Collective Systems
Many living and non-living complex systems can be modeled and understood as
collective systems made of heterogeneous components that self-organize and
generate nontrivial morphological structures and behaviors. This chapter
presents a brief overview of our recent effort that investigated various
aspects of such morphogenetic collective systems. We first propose a
theoretical classification scheme that distinguishes four complexity levels of
morphogenetic collective systems based on the nature of their components and
interactions. We conducted a series of computational experiments using a
self-propelled particle swarm model to investigate the effects of (1)
heterogeneity of components, (2) differentiation/re-differentiation of
components, and (3) local information sharing among components, on the
self-organization of a collective system. Results showed that (a) heterogeneity
of components had a strong impact on the system's structure and behavior, (b)
dynamic differentiation/re-differentiation of components and local information
sharing helped the system maintain spatially adjacent, coherent organization,
(c) dynamic differentiation/re-differentiation contributed to the development
of more diverse structures and behaviors, and (d) stochastic re-differentiation
of components naturally realized a self-repair capability of self-organizing
morphologies. We also explored evolutionary methods to design novel
self-organizing patterns, using interactive evolutionary computation and
spontaneous evolution within an artificial ecosystem. These self-organizing
patterns were found to be remarkably robust against dimensional changes from 2D
to 3D, although evolution worked efficiently only in 2D settings.Comment: 13 pages, 8 figures, 1 table; submitted to "Evolution, Development,
and Complexity: Multiscale Models in Complex Adaptive Systems" (Springer
Proceedings in Complexity Series
Swarm Robotics
Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties
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