Evolutionary Search for Cellular Automata with Self-Organizing Properties toward Controlling Decentralized Pervasive Systems and Its Applications

2015 ◽  
Vol 3 (1) ◽  
pp. 1-19
Author(s):  
Yusuke Iwase ◽  
Reiji Suzuki ◽  
Takaya Arita
Keyword(s):  
Cellular Automata ◽  
Emergent Behavior ◽  
Emergent Properties ◽  
Evolutionary Search ◽  
Global State ◽  
Interactive Art ◽  
Stable States ◽  
Pervasive Systems ◽  
External Perturbations ◽  
Perturbation Period

Cellular Automata (CAs) have been investigated extensively as abstract models of the decentralized systems composed of autonomous entities characterized by local interactions. However, it is poorly understood how CAs can interact with their external environment, which would be useful for implementing pervasive systems that consist of billions of components (nodes, sensors, etc.). This paper focuses on the emergent properties of CAs induced by external perturbations toward controlling pervasive systems. The authors assumed a minimum task in which a CA has to change its global state drastically after every occurrence of a perturbation period. By conducting evolutionary searches for rules of CAs, they obtained interesting behaviors of CAs in which their global state cyclically transited among different stable states in either ascending or descending order. They analyze the emergent behavior in detail and also introduce applications of the evolved CA for controlling pervasive robots and an interactive art.

Evolutionary Search for Cellular Automata with Self-Organizing Properties toward Controlling Decentralized Pervasive Systems

2010 ◽  
pp. 308-320
Author(s):  
Yusuke Iwase ◽  
Reiji Suzuki ◽  
Takaya Arita
Keyword(s):  
Cellular Automata ◽  
External Environment ◽  
The Self ◽  
Emergent Properties ◽  
Global State ◽  
Decentralized Systems ◽  
Stable States ◽  
Pervasive Systems ◽  
Perturbation Period ◽  
Self Organizing

Cellular Automata (CAs) have been investigated extensively as abstract models of the decentralized systems composed of autonomous entities characterized by local interactions. However, it is poorly understood how CAs can interact with their external environment, which would be useful for implementing decentralized pervasive systems that consist of billions of components (nodes, sensors, etc.) distributed in our everyday environments. This chapter focuses on the emergent properties of CAs induced by external perturbations toward controlling decentralized pervasive systems. We assumed a minimum task in which a CA has to change its global state drastically after every occurrence of a perturbation period. In the perturbation period, each cell state is modified by using an external rule with a small probability. By conducting evolutionary searches for rules of CAs, we obtained interesting behaviors of CAs in which their global state cyclically transited among different stable states in either ascending or descending order. The self-organizing behaviors are due to the clusters of cell states that dynamically grow through occurrences of perturbation periods. These results imply that we can dynamically control the global behaviors of decentralized systems by states of randomly selected components only.

Models of decision support in socio-cyber-physical systems

2019 ◽  
pp. 55-70 ◽  
Author(s):  
A. V. Smirnov ◽  
T. V. Levashova
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Situation Awareness ◽  
Cyber Physical Systems ◽  
Emergent Behavior ◽  
Emergent Properties ◽  
Decision Making Process ◽  
Physical Systems ◽  
Decision Support Models ◽  
Common Goals

Introduction: Socio-cyber-physical systems are complex non-linear systems. Such systems display emergent properties. Involvement of humans, as a part of these systems, in the decision-making process contributes to overcoming the consequences of the emergent system behavior, since people can use their experience and intuition, not just the programmed rules and procedures.Purpose: Development of models for decision support in socio-cyber-physical systems.Results: A scheme of decision making in socio-cyber-physical systems, a conceptual framework of decision support in these systems, and stepwise decision support models have been developed. The decision-making scheme is that cybernetic components make their decisions first, and if they cannot do this, they ask humans for help. The stepwise models support the decisions made by components of socio-cyber-physical systems at the conventional stages of the decision-making process: situation awareness, problem identification, development of alternatives, choice of a preferred alternative, and decision implementation. The application of the developed models is illustrated through a scenario for planning the execution of a common task for robots.Practical relevance: The developed models enable you to design plans on solving tasks common for system components or on achievement of common goals, and to implement these plans. The models contribute to overcoming the consequences of the emergent behavior of socio-cyber-physical systems, and to the research on machine learning and mobile robot control.

Phosphofructokinase controls the acetaldehyde-induced phase shift in isolated yeast glycolytic oscillators

2019 ◽  
Vol 476 (2) ◽  
pp. 353-363
Author(s):  
David D. van Niekerk ◽  
Anna-Karin Gustavsson ◽  
Martin Mojica-Benavides ◽  
Caroline B. Adiels ◽  
Mattias Goksör ◽  
...  
Keyword(s):  
Phase Shift ◽  
Phase Response Curve ◽  
Mechanistic Model ◽  
Phase Response ◽  
Yeast Cells ◽  
Emergent Properties ◽  
Functional Coupling ◽  
External Signal ◽  
Oscillatory Systems ◽  
External Perturbations

Abstract The response of oscillatory systems to external perturbations is crucial for emergent properties such as synchronisation and phase locking and can be quantified in a phase response curve (PRC). In individual, oscillating yeast cells, we characterised experimentally the phase response of glycolytic oscillations for external acetaldehyde pulses and followed the transduction of the perturbation through the system. Subsequently, we analysed the control of the relevant system components in a detailed mechanistic model. The observed responses are interpreted in terms of the functional coupling and regulation in the reaction network. We find that our model quantitatively predicts the phase-dependent phase shift observed in the experimental data. The phase shift is in agreement with an adaptation leading to synchronisation with an external signal. Our model analysis establishes that phosphofructokinase plays a key role in the phase shift dynamics as shown in the PRC and adaptation time to external perturbations. Specific mechanism-based interventions, made possible through such analyses of detailed models, can improve upon standard trial and error methods, e.g. melatonin supplementation to overcome jet-lag, which are error-prone, specifically, since the effects are phase dependent and dose dependent. The models by Gustavsson and Goldbeter discussed in the text can be obtained from the JWS Online simulation database: (https://jjj.bio.vu.nl/models/gustavsson5 and https://jjj.bio.vu.nl/models/goldbeter1)

scholarly journals Dynamic Graphical Models of Molecular Kinetics

2018 ◽  
Author(s):  
Simon Olsson ◽  
Frank Noé
Keyword(s):  
Graphical Models ◽  
Molecular System ◽  
System Size ◽  
Ising Models ◽  
Metastable States ◽  
Dynamics Simulation ◽  
Global State ◽  
Stable States ◽  
Markov State ◽  
State Models

AbstractMost current molecular dynamics simulation and analysis methods rely on the idea that the molecular system can be characterized by a single global state, e.g., a Markov State in a Markov State Model (MSM). In this approach, molecules can be extensively sampled and analyzed when they only possess a few metastable states, such as small to medium-sized proteins. However this approach breaks down in frustrated systems and in large protein assemblies, where the number of global meta-stable states may grow exponentially with the system size. Here, we introduce Dynamic Graphical Models (DGMs), which build upon the idea of Ising models, and describe molecules as assemblies of coupled subsystems. The switching of each sub-system state is only governed by the states of itself and its neighbors. DGMs need many fewer parameters than MSMs or other global-state models, in particular we do not need to observe all global system configurations to estimate them. Therefore, DGMs can predict new, previously unobserved, molecular configurations. Here, we demonstrate that DGMs can faithfully describe molecular thermodynamics and kinetics and predict previously unobserved metastable states for Ising models and protein simulations.

scholarly journals Biogenic gradients in algal density affect the emergent properties of spatially self-organized mussel beds

2014 ◽  
Vol 11 (96) ◽  
pp. 20140089 ◽  
Author(s):  
Quan-Xing Liu ◽  
Ellen J. Weerman ◽  
Rohit Gupta ◽  
Peter M. J. Herman ◽  
Han Olff ◽  
...  
Keyword(s):  
Large Scale ◽  
Scale Effects ◽  
Water Layer ◽  
Theoretical Models ◽  
Emergent Properties ◽  
Stable States ◽  
Mussel Beds ◽  
Self Organized ◽  
Proposed Model ◽  
Secondary Productivity

Theoretical models highlight that spatially self-organized patterns can have important emergent effects on the functioning of ecosystems, for instance by increasing productivity and affecting the vulnerability to catastrophic shifts. However, most theoretical studies presume idealized homogeneous conditions, which are rarely met in real ecosystems. Using self-organized mussel beds as a case study, we reveal that spatial heterogeneity, resulting from the large-scale effects of mussel beds on their environment, significantly alters the emergent properties predicted by idealized self-organization models that assume homogeneous conditions. The proposed model explicitly considers that the suspended algae, the prime food for the mussels, are supplied by water flow from the seaward boundary of the bed, which causes in combination with consumption a gradual depletion of algae over the simulated domain. Predictions of the model are consistent with properties of natural mussel patterns observed in the field, featuring a decline in mussel biomass and a change in patterning. Model analyses reveal a fundamental change in ecosystem functioning when this self-induced algal depletion gradient is included in the model. First, no enhancement of secondary productivity of the mussels comparing with non-patterns states is predicted, irrespective of parameter setting; the equilibrium amount of mussels is entirely set by the input of algae. Second, alternate stable states, potentially present in the original (no algal gradient) model, are absent when gradual depletion of algae in the overflowing water layer is allowed. Our findings stress the importance of including sufficiently realistic environmental conditions when assessing the emergent properties of self-organized ecosystems.

Emergent behavior in two complex cellular automata rule sets

Complexity ◽  
2005 ◽  
Vol 10 (5) ◽  
pp. 45-55 ◽  
Author(s):  
Christopher J. Hazard ◽  
Kyle R. Kimport ◽  
David H. Johnson
Keyword(s):  
Cellular Automata ◽  
Emergent Behavior ◽  
Rule Sets
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