This document discusses morphogenetic engineering, which aims to design decentralized systems capable of developing elaborate morphologies without central planning. It covers three main topics:
1) Engineering and control of self-organization, which involves fostering and guiding complex systems through their elements.
2) Morphogenetic engineering, which explores artificial design of systems that can develop architectures like those seen in biology, with heterogeneous and hierarchical structures emerging from self-organization.
3) Embryomorphic engineering, which takes inspiration from biological morphogenesis and development, aiming to design multi-agent models that can undergo evolution and development like living organisms. The goal is to better understand novelty in evolution by studying emergence at the microscopic, agent level.
This document discusses the concept of morphogenetic engineering, which aims to design artificial self-organized systems capable of developing elaborate architectures without central planning. It begins by looking at natural complex systems like animal flocking and termite mounds that self-organize. The focus is on "architectures without architects" in biological systems. Morphogenetic engineering is proposed as a new type of engineering that designs self-organizing agents, not the architectures directly, taking inspiration from embryogenesis, simulated development and synthetic biology. Several research projects are summarized that aim to model biological development and create modular, programmable artificial self-construction.
Kantian Philosophy of Mathematics and Young Robots: Could a baby robot grow u...Aaron Sloman
This document provides a 3-part summary of a presentation on Kantian philosophy of mathematics and young robots:
[1] It discusses the need for a multi-pronged revolution in intelligence studies, replacing wars between factions with collaboration and focusing more on problems solved by evolution rather than hypothesized solutions.
[2] It outlines some main points of the presentation, including that species have different designs to solve problems, some relying more on genetically determined competences while others can develop new competences through learning.
[3] It notes that the mechanisms for creativity are related to making mathematical discoveries by transforming empirical knowledge into non-empirical knowledge, and gives an example of learning that different counting methods give the same result
This document summarizes several agent-based modeling projects done by students at the University of East London. It describes projects using StarLogo, where students modeled emergent urban forms and traffic patterns. It also discusses modeling the growth of traditional Yemeni cities and experiments deforming NURBS surfaces using agent-based modeling in Microstation. The document provides examples of how agent-based modeling can be used as a design tool to explore emergent patterns and behaviors.
Werfel, j. 2006: extended stigmergy in collective construction.in life intel...ArchiLab 7
This document summarizes research on using extended stigmergy to improve collective construction by swarms of robots. Extended stigmergy augments basic stigmergy by allowing environmental elements like building blocks to store and communicate information. Three variants that use extended stigmergy to different degrees are analyzed and compared through theoretical analysis and simulation. The goal is to start with a desired high-level structure and find low-level rules for robots to build it, addressing the inverse construction problem compared to how social insects build.
The Turing Inspired Meta-Morphogenesis Project -- The self-informing universe...Aaron Sloman
This replaces an earlier version. The latest version with clickable links is available at Versions with clickable links available at http://www.cs.bham.ac.uk/research/projects/cogaff/misc/meta-morphogenesis.html
The document discusses self-replicating modular robots composed of cubes containing electromagnets, computers, and the ability to selectively attach and detach from each other. The cubes are able to replicate by bending over to pick up additional cubes and attaching them, with the ability to continue replicating fully autonomous robots. Potential applications include space exploration, hazardous environments, and studying biological processes.
This document summarizes a presentation on self-adaptation and self-awareness with a focus on reflective Russian dolls. It defines adaptation as the run-time modification of control data. It presents an approach using reflective Russian dolls to support formal techniques for adaptation and self-awareness. This involves using logical reflection and wrapping techniques to represent adaptive systems as towers of reflections. The presentation discusses using Maude to formally model autonomic managers and adaptive systems.
This document discusses the concept of morphogenetic engineering, which aims to design artificial self-organized systems capable of developing elaborate architectures without central planning. It begins by looking at natural complex systems like animal flocking and termite mounds that self-organize. The focus is on "architectures without architects" in biological systems. Morphogenetic engineering is proposed as a new type of engineering that designs self-organizing agents, not the architectures directly, taking inspiration from embryogenesis, simulated development and synthetic biology. Several research projects are summarized that aim to model biological development and create modular, programmable artificial self-construction.
Kantian Philosophy of Mathematics and Young Robots: Could a baby robot grow u...Aaron Sloman
This document provides a 3-part summary of a presentation on Kantian philosophy of mathematics and young robots:
[1] It discusses the need for a multi-pronged revolution in intelligence studies, replacing wars between factions with collaboration and focusing more on problems solved by evolution rather than hypothesized solutions.
[2] It outlines some main points of the presentation, including that species have different designs to solve problems, some relying more on genetically determined competences while others can develop new competences through learning.
[3] It notes that the mechanisms for creativity are related to making mathematical discoveries by transforming empirical knowledge into non-empirical knowledge, and gives an example of learning that different counting methods give the same result
This document summarizes several agent-based modeling projects done by students at the University of East London. It describes projects using StarLogo, where students modeled emergent urban forms and traffic patterns. It also discusses modeling the growth of traditional Yemeni cities and experiments deforming NURBS surfaces using agent-based modeling in Microstation. The document provides examples of how agent-based modeling can be used as a design tool to explore emergent patterns and behaviors.
Werfel, j. 2006: extended stigmergy in collective construction.in life intel...ArchiLab 7
This document summarizes research on using extended stigmergy to improve collective construction by swarms of robots. Extended stigmergy augments basic stigmergy by allowing environmental elements like building blocks to store and communicate information. Three variants that use extended stigmergy to different degrees are analyzed and compared through theoretical analysis and simulation. The goal is to start with a desired high-level structure and find low-level rules for robots to build it, addressing the inverse construction problem compared to how social insects build.
The Turing Inspired Meta-Morphogenesis Project -- The self-informing universe...Aaron Sloman
This replaces an earlier version. The latest version with clickable links is available at Versions with clickable links available at http://www.cs.bham.ac.uk/research/projects/cogaff/misc/meta-morphogenesis.html
The document discusses self-replicating modular robots composed of cubes containing electromagnets, computers, and the ability to selectively attach and detach from each other. The cubes are able to replicate by bending over to pick up additional cubes and attaching them, with the ability to continue replicating fully autonomous robots. Potential applications include space exploration, hazardous environments, and studying biological processes.
This document summarizes a presentation on self-adaptation and self-awareness with a focus on reflective Russian dolls. It defines adaptation as the run-time modification of control data. It presents an approach using reflective Russian dolls to support formal techniques for adaptation and self-awareness. This involves using logical reflection and wrapping techniques to represent adaptive systems as towers of reflections. The presentation discusses using Maude to formally model autonomic managers and adaptive systems.
1. The document discusses various examples of complex systems, including physical pattern formation in liquids, biological pattern formation in animal coats, neural networks in the brain, swarm intelligence in insect colonies, and collective motion in herds and schools.
2. Complex systems are characterized by a large number of interacting agents following simple local rules that lead to emergent complex behavior at the global scale without centralized control.
3. The examples presented illustrate how decentralized interactions between many individual parts or agents can self-organize into complex coherent structures and patterns through basic principles like feedback, synchronization, and reinforcement.
1. Engineering aims to improve the physical world for the better according to various definitions of better, such as ethics, technoevolutionary improvement, or minimizing unpleasant surprises.
2. Contemporary systems engineering teaches state-of-the-art (SoTA) practices, including continuous software engineering, cyber-physical systems engineering, and enterprise engineering, generalized for all types of systems.
3. There have been three generations of systems approaches since the 1940s, moving from viewing the system in its environment to recognizing that systems are created by other systems through engineering practices to seeing systems as techno-organisms that evolve through technoevolutionary processes.
This document provides an introduction to complex systems and agent-based modeling. It discusses what complex systems are, including examples ranging from simple systems of a few agents to more sophisticated systems involving many agents. Complex systems are characterized as having emergent behaviors that arise from the interactions of the agents following simple rules, without any centralized control. The document also provides examples of complex systems in nature, such as pattern formation, neural networks, swarm intelligence in insect colonies, collective motion of flocking and schooling, and social biological systems.
2005: Natural Computing - Concepts and ApplicationsLeandro de Castro
The document discusses natural computing, which encompasses computing inspired by nature, simulating natural phenomena using computers, and using natural materials for computing. It surveys ideas from neurocomputing, evolutionary computing, swarm intelligence, immunocomputing, and artificial life. These fields take inspiration from neural networks, evolution, collective animal behavior, the immune system, and the synthesis of life-like behaviors to develop new algorithms and applications. The goal is to develop more robust, adaptive, and fault-tolerant computing approaches.
The document discusses the origins and evolution of project management from a complexity theory perspective. It notes that project management was born out of managing complex systems, like missile development programs in the 1950s. However, over time the fields of general systems theory, cybernetics, and project management diverged, with project management becoming more linear and process-focused. The document argues that reconnecting project management with its roots in managing complex adaptive systems could provide insights into issues like non-linearity, emergence, evolution, and radical uncertainty.
This document summarizes key insights from a presentation on viewing project management through the lens of complexity theory. It discusses how complexity theory originated in the study of natural systems and how its concepts like emergence and non-linearity are relevant to project management. It also notes that while general systems theory promised to connect different fields, project management, cybernetics, and systems thinking ultimately diverged. The document reviews different perspectives on categorizing project complexity and shares insights from interviews where project managers discussed experiencing uncertainty, renegotiating plans, and maintaining progress despite radical uncertainty.
What (Else) Can Agile Learn From ComplexityJurgen Appelo
How can complexity science be applied to software development? This presentation shows you which scientific concepts can be mapped to agile software development.
http://www.noop.nl
http://www.jurgenappelo.com
Swarm intelligence is an artificial intelligence technique inspired by the collective behavior of decentralized and self-organized systems found in nature, such as ant colonies and bird flocks. Two common swarm intelligence algorithms are ant colony optimization and particle swarm optimization. Ant colony optimization is based on the behavior of real ant colonies and can be used to find approximate solutions to difficult optimization problems. Particle swarm optimization is a population-based stochastic optimization technique inspired by swarming behavior in nature, such as bird flocking. It searches for optimal solutions within a problem space by updating the movement of individual particles based on their own experiences and those of neighboring particles.
The document discusses theories of intelligent design and arguments used to detect design in biological systems. It describes how intelligent agents act with purpose and reuse functional components. It also outlines arguments for design based on irreducible complexity and complex specified information in structures like the bacterial flagellum. However, critics argue it is difficult to test claims of intelligent design and that structures said to be irreducibly complex, like the flagellum, may have evolved from pre-existing molecular systems. The theory of intelligent design aims to develop a full model of biological design but has yet to do so.
Cognitive Paradigm in AI - Invited Lecture - Kyiv/Kyev - LietoAntonio Lieto
1) The document discusses the cognitive paradigm in artificial intelligence research and cognitively inspired AI systems.
2) Cognitively inspired AI systems are designed based on insights from human and animal cognition, using structural constraints from cognitive science.
3) Examples of cognitively inspired AI systems discussed include GPS, semantic networks, the RM model of past-tense acquisition, and cognitive architectures like Soar and ACT-R.
A fascinating View of the Artificial Intelligence Journey.
Ramón López de Mántaras, Ph.D.
Technical and Business Perspectives on the Current and Future Impact of Machine Learning - MLVLC
October 20, 2015
The document provides an overview of the speaker's background and research interests in digital ecosystems modelling. It discusses how socio-technical systems can be modeled using a systems approach rather than just mathematics. It also touches on ideas like knowledge ecosystems, complex systems emergence, and the importance of shared vocabularies. The goal is to engage the audience and potentially find opportunities for collaboration.
Chaps29 the entirebookks2017 - The Mind MahineSyedVAhamed
In this chapter, we take bold step and propose the unthinkable: The genesis of a Customizable Mind Machine.
Thought that stems from the mind is deeply seated in a biological framework of neurons. The biological origin lies
in the marvel of evolution over the eons and refined ever so fast, faster than in the prior centuries. Three (a, b and
c), triadic objects are ceaselessly at work. At a personal level (a) Mind, knowledge and machines have been
intertwined like inspiration, words and language since the dawn of the human evolution and more recently (b)
technology, manufacturing and economics have formed a web for (c) wealth, global marketing and insatiable needs
of humans and civilization. These triadic cycles of nine essential objects of human existence are spinning quicker
and quicker every year. The Internet offers the mind no choice but to leap and soar over history and over the globe.
Alternatively, human mind can sink deeper and deeper into ignorance and oblivion. More recently, the Artificial
Intelligence at work in the Internet had challenged the natural intelligence at the cognizance level in the mind to find
its way to breakthroughs and innovations.
We integrate functions of the mind with the processing of knowledge in the hardware of machines by freely
traversing the neural, mental, physical, psychological, social, knowledge, and computational spaces. The laws of
neural biology and mind, laws of knowledge and social sciences and finally the laws of physics and mechanics, in
each of the spaces are unique and executed by distinctive processors for each space. Much as mind rules over
matter, the triad of mind, space and time creates a human-space that rules over the Relativistic-space of matter,
space and time.
Keywords—Mind, Knowledge, Machines, Technology, Human Needs, Knowledge Windows, Perceptual Spaces
Swarm robotics is an approach to controlling large groups of robots inspired by social insects like ants and bees. It emphasizes emergent behaviors from local interactions between relatively simple robots with only local sensing and communication. This allows for properties like robustness, flexibility, and scalability needed for deploying many robots. Swarm robotics systems consist of many homogeneous robots that are not very capable individually but can collectively perform tasks through self-organization.
International journal of engineering issues vol 2015 - no 1 - paper3sophiabelthome
This document summarizes a paper on modeling evolving complex software systems as cyber-physical systems using principles from physics and mathematics. It discusses how software systems can be viewed as complex automatons with mathematical foundations in areas like complex numbers and Fourier transforms. Cybernetics provides tools to model human behaviors and interactions in these systems. The paper also discusses how analog computers were early models of physical phenomena, and how infinitesimals and differentials from calculus can model continuously changing aspects of cyber-physical systems, within the limits imposed by physical reality.
Robots working in swarms need to be self-aware to adapt their behavior based on task performance and collective behavior emerges. Self-aware computing systems could help manage distributed energy production and consumption in smart grids. Data and services could manage themselves in an "ecosystem" through decentralized algorithms. Human cognitive processes like inference could help systems manage internet content by acquiring new content and filtering existing content. Self-aware electric vehicles could communicate to improve reliability, adaptability, and predictability through cooperation. Science clouds use self-aware computing to manage distributed notebooks, servers and virtual machines.
This document discusses the problems of safety and ethics in autonomous systems like robots. Ensuring safe behavior is difficult when robots operate in unpredictable human environments, and they pose ethical challenges if capable of harming humans, inducing emotional responses, appearing intelligent without being so, or causing harm without a responsible party. The author proposes that internal models allowing robots to predict action consequences and check them against safety and ethical rules could enable truly safe and ethical autonomous robots. Self-awareness through internal modeling may be needed to guarantee safety for robots and other autonomous systems working in unknown environments.
More Related Content
Similar to Academic Course: 06 Morphogenetic Engineering
1. The document discusses various examples of complex systems, including physical pattern formation in liquids, biological pattern formation in animal coats, neural networks in the brain, swarm intelligence in insect colonies, and collective motion in herds and schools.
2. Complex systems are characterized by a large number of interacting agents following simple local rules that lead to emergent complex behavior at the global scale without centralized control.
3. The examples presented illustrate how decentralized interactions between many individual parts or agents can self-organize into complex coherent structures and patterns through basic principles like feedback, synchronization, and reinforcement.
1. Engineering aims to improve the physical world for the better according to various definitions of better, such as ethics, technoevolutionary improvement, or minimizing unpleasant surprises.
2. Contemporary systems engineering teaches state-of-the-art (SoTA) practices, including continuous software engineering, cyber-physical systems engineering, and enterprise engineering, generalized for all types of systems.
3. There have been three generations of systems approaches since the 1940s, moving from viewing the system in its environment to recognizing that systems are created by other systems through engineering practices to seeing systems as techno-organisms that evolve through technoevolutionary processes.
This document provides an introduction to complex systems and agent-based modeling. It discusses what complex systems are, including examples ranging from simple systems of a few agents to more sophisticated systems involving many agents. Complex systems are characterized as having emergent behaviors that arise from the interactions of the agents following simple rules, without any centralized control. The document also provides examples of complex systems in nature, such as pattern formation, neural networks, swarm intelligence in insect colonies, collective motion of flocking and schooling, and social biological systems.
2005: Natural Computing - Concepts and ApplicationsLeandro de Castro
The document discusses natural computing, which encompasses computing inspired by nature, simulating natural phenomena using computers, and using natural materials for computing. It surveys ideas from neurocomputing, evolutionary computing, swarm intelligence, immunocomputing, and artificial life. These fields take inspiration from neural networks, evolution, collective animal behavior, the immune system, and the synthesis of life-like behaviors to develop new algorithms and applications. The goal is to develop more robust, adaptive, and fault-tolerant computing approaches.
The document discusses the origins and evolution of project management from a complexity theory perspective. It notes that project management was born out of managing complex systems, like missile development programs in the 1950s. However, over time the fields of general systems theory, cybernetics, and project management diverged, with project management becoming more linear and process-focused. The document argues that reconnecting project management with its roots in managing complex adaptive systems could provide insights into issues like non-linearity, emergence, evolution, and radical uncertainty.
This document summarizes key insights from a presentation on viewing project management through the lens of complexity theory. It discusses how complexity theory originated in the study of natural systems and how its concepts like emergence and non-linearity are relevant to project management. It also notes that while general systems theory promised to connect different fields, project management, cybernetics, and systems thinking ultimately diverged. The document reviews different perspectives on categorizing project complexity and shares insights from interviews where project managers discussed experiencing uncertainty, renegotiating plans, and maintaining progress despite radical uncertainty.
What (Else) Can Agile Learn From ComplexityJurgen Appelo
How can complexity science be applied to software development? This presentation shows you which scientific concepts can be mapped to agile software development.
http://www.noop.nl
http://www.jurgenappelo.com
Swarm intelligence is an artificial intelligence technique inspired by the collective behavior of decentralized and self-organized systems found in nature, such as ant colonies and bird flocks. Two common swarm intelligence algorithms are ant colony optimization and particle swarm optimization. Ant colony optimization is based on the behavior of real ant colonies and can be used to find approximate solutions to difficult optimization problems. Particle swarm optimization is a population-based stochastic optimization technique inspired by swarming behavior in nature, such as bird flocking. It searches for optimal solutions within a problem space by updating the movement of individual particles based on their own experiences and those of neighboring particles.
The document discusses theories of intelligent design and arguments used to detect design in biological systems. It describes how intelligent agents act with purpose and reuse functional components. It also outlines arguments for design based on irreducible complexity and complex specified information in structures like the bacterial flagellum. However, critics argue it is difficult to test claims of intelligent design and that structures said to be irreducibly complex, like the flagellum, may have evolved from pre-existing molecular systems. The theory of intelligent design aims to develop a full model of biological design but has yet to do so.
Cognitive Paradigm in AI - Invited Lecture - Kyiv/Kyev - LietoAntonio Lieto
1) The document discusses the cognitive paradigm in artificial intelligence research and cognitively inspired AI systems.
2) Cognitively inspired AI systems are designed based on insights from human and animal cognition, using structural constraints from cognitive science.
3) Examples of cognitively inspired AI systems discussed include GPS, semantic networks, the RM model of past-tense acquisition, and cognitive architectures like Soar and ACT-R.
A fascinating View of the Artificial Intelligence Journey.
Ramón López de Mántaras, Ph.D.
Technical and Business Perspectives on the Current and Future Impact of Machine Learning - MLVLC
October 20, 2015
The document provides an overview of the speaker's background and research interests in digital ecosystems modelling. It discusses how socio-technical systems can be modeled using a systems approach rather than just mathematics. It also touches on ideas like knowledge ecosystems, complex systems emergence, and the importance of shared vocabularies. The goal is to engage the audience and potentially find opportunities for collaboration.
Chaps29 the entirebookks2017 - The Mind MahineSyedVAhamed
In this chapter, we take bold step and propose the unthinkable: The genesis of a Customizable Mind Machine.
Thought that stems from the mind is deeply seated in a biological framework of neurons. The biological origin lies
in the marvel of evolution over the eons and refined ever so fast, faster than in the prior centuries. Three (a, b and
c), triadic objects are ceaselessly at work. At a personal level (a) Mind, knowledge and machines have been
intertwined like inspiration, words and language since the dawn of the human evolution and more recently (b)
technology, manufacturing and economics have formed a web for (c) wealth, global marketing and insatiable needs
of humans and civilization. These triadic cycles of nine essential objects of human existence are spinning quicker
and quicker every year. The Internet offers the mind no choice but to leap and soar over history and over the globe.
Alternatively, human mind can sink deeper and deeper into ignorance and oblivion. More recently, the Artificial
Intelligence at work in the Internet had challenged the natural intelligence at the cognizance level in the mind to find
its way to breakthroughs and innovations.
We integrate functions of the mind with the processing of knowledge in the hardware of machines by freely
traversing the neural, mental, physical, psychological, social, knowledge, and computational spaces. The laws of
neural biology and mind, laws of knowledge and social sciences and finally the laws of physics and mechanics, in
each of the spaces are unique and executed by distinctive processors for each space. Much as mind rules over
matter, the triad of mind, space and time creates a human-space that rules over the Relativistic-space of matter,
space and time.
Keywords—Mind, Knowledge, Machines, Technology, Human Needs, Knowledge Windows, Perceptual Spaces
Swarm robotics is an approach to controlling large groups of robots inspired by social insects like ants and bees. It emphasizes emergent behaviors from local interactions between relatively simple robots with only local sensing and communication. This allows for properties like robustness, flexibility, and scalability needed for deploying many robots. Swarm robotics systems consist of many homogeneous robots that are not very capable individually but can collectively perform tasks through self-organization.
International journal of engineering issues vol 2015 - no 1 - paper3sophiabelthome
This document summarizes a paper on modeling evolving complex software systems as cyber-physical systems using principles from physics and mathematics. It discusses how software systems can be viewed as complex automatons with mathematical foundations in areas like complex numbers and Fourier transforms. Cybernetics provides tools to model human behaviors and interactions in these systems. The paper also discusses how analog computers were early models of physical phenomena, and how infinitesimals and differentials from calculus can model continuously changing aspects of cyber-physical systems, within the limits imposed by physical reality.
Similar to Academic Course: 06 Morphogenetic Engineering (20)
Robots working in swarms need to be self-aware to adapt their behavior based on task performance and collective behavior emerges. Self-aware computing systems could help manage distributed energy production and consumption in smart grids. Data and services could manage themselves in an "ecosystem" through decentralized algorithms. Human cognitive processes like inference could help systems manage internet content by acquiring new content and filtering existing content. Self-aware electric vehicles could communicate to improve reliability, adaptability, and predictability through cooperation. Science clouds use self-aware computing to manage distributed notebooks, servers and virtual machines.
This document discusses the problems of safety and ethics in autonomous systems like robots. Ensuring safe behavior is difficult when robots operate in unpredictable human environments, and they pose ethical challenges if capable of harming humans, inducing emotional responses, appearing intelligent without being so, or causing harm without a responsible party. The author proposes that internal models allowing robots to predict action consequences and check them against safety and ethical rules could enable truly safe and ethical autonomous robots. Self-awareness through internal modeling may be needed to guarantee safety for robots and other autonomous systems working in unknown environments.
This document discusses design patterns for autonomic systems. It begins by explaining what design patterns are and how they allow common solutions to recurring problems to be reused, saving time. It then discusses how patterns are described and can be composed to solve different problems. The document outlines several bio-inspired design patterns for autonomic computing systems, including spreading, aggregation, evaporation, and repulsion. It concludes by discussing a taxonomy for classifying patterns according to the component and ensemble levels in an autonomic system.
This document provides an introduction to modeling and analyzing autonomic systems. It discusses modeling autonomic systems using the SOTA/GEM framework for requirements specification and the SCEL modeling language. It then presents a case study of modeling a swarm of garbage collecting robots. Key steps include modeling goals and requirements, selecting adaptation patterns, modeling the robot behavior and interactions in SCEL, and validating requirements through quantitative analysis using techniques like CTMC and ODE models. The document outlines the iterative design time and runtime engineering process for autonomic systems using these techniques.
The document discusses autonomic multi-agent systems and self-awareness. It covers:
1) The objectives of understanding fundamental properties of autonomic systems and how agents can use environmental awareness for self-organization.
2) An overview of multi-agent systems, autonomic systems, and representative approaches like dynamic norm-governed systems.
3) How awareness can enable self-healing through maintaining congruence between rules and system state.
This document discusses common features of complex systems and networks. It notes that complex systems generally have a large number of elements that follow individual behavior rules and interact locally. The systems exhibit node and link diversity and dynamics. They can display hierarchy across different levels and heterogeneity. Complex networks form the backbone of complex systems. Network structure influences function and vice versa. Three key metrics to characterize networks are described - average path length, degree distribution, and clustering coefficient. Different types of networks, including random, regular, small-world and scale-free are also discussed.
This document discusses self-awareness in psychology and proposes a framework for computational self-awareness. It defines different types of self-awareness, such as implicit/explicit and private/public. It also outlines levels of self-awareness ranging from stimulus awareness to meta-self-awareness. Finally, it proposes applying these concepts to computing by defining private and public computational self-awareness and levels that could emerge from interactions between components.
The document provides an outline for a presentation on self-awareness in autonomic systems. It discusses introductory examples of robot swarms, science clouds, and cooperative electric vehicles. It then motivates the need for awareness in complex distributed systems like communication and power networks. Existing research projects exploring self-awareness concepts are summarized, including ASCENS, CoCoRo, EPiCS, RECOGNITION, SAPERE, and SYMBRION. Nature-inspired examples of self-aware behaviors in flocking, ant foraging, quorum sensing, chemotaxis, morphogenesis, and gossiping are presented. Finally, awareness properties in biological systems like the immune system are discussed.
This document summarizes several research projects related to autonomic and self-aware systems. It discusses proprioceptive systems like EPiCS which aim to develop self-aware and self-expressive computing systems. It also discusses swarm robotics projects like SYMBRION that develop robotic swarms capable of self-organization. Data management projects like SAPERE and RECOGNITION seek to develop self-aware techniques for acquiring and managing large amounts of data and content.
Simulation tools can help understand natural systems and develop self-aware systems. Existing simulators like Repast and The ONE have advantages but lack certain features. The CoSMoS method structures simulation development through domain, platform, and results models to help ensure simulations accurately represent domains. Simulations aid controller design for systems like underwater robots, though the "reality gap" between simulation and reality requires attention.
The document discusses awareness in autonomous systems. It covers general properties of self-awareness like perception and collectivity. It also discusses the short-term impacts of self-awareness like safety and sustainability and long-term open issues. Key aspects of self-awareness are levels ranging from ecological to conceptual awareness. Distributed emergence of self-awareness is possible through collective systems though parts exhibit less awareness. Internal models are important for self-aware systems to represent themselves and environments to test possibilities.
This document discusses self-awareness in autonomous systems and provides examples. It defines autonomic systems as self-governing systems that can operate without external direction in complex environments. Examples discussed include robot swarms, science clouds, and cooperative electric vehicles. The motivation for self-awareness in information and communication technology systems is that as systems become more distributed and complex, they require mechanisms to manage and organize themselves. Existing self-aware systems in nature that provide inspiration include flocking behavior in animals and ant foraging behavior through decentralized coordination.
This document discusses robot swarms and swarm robotics. It introduces marXbot, a miniature mobile robot with various sensors that can dock with other robots. It discusses problems with swarm robotics like noise and uncertainty. It then covers using action logics and Markov decision processes to model probabilistic behavior in robot swarms. Finally, it discusses reinforcement learning techniques like hierarchical reinforcement learning and decomposition that can help address challenges of modeling large state spaces.
This document discusses engineering autonomic ensembles through model-based development. It describes modeling autonomic systems using Agamemnon and implementing components using Poem. Reinforcement learning is used to find good completions for partial programs that maximize reward. The Service Component Ensemble Language (SCEL) provides an abstract framework for ensemble programming. A case study of a robot ensemble is used to illustrate modeling the domain and requirements, selecting adaptation patterns, modeling behavior, and analyzing requirements through simulation and sensitivity analysis.
The document discusses using swarms of underwater robots to perform search and rescue tasks. It describes the CoCoRo project which uses collective cognition and swarm intelligence to coordinate groups of simple robots. This allows them to display complex emergent behaviors. Specific challenges of operating underwater like communication and localization are addressed. The document proposes using a relay chain to connect an exploratory swarm of robots to a base station. It provides resources to start simulating and developing algorithms for the swarm and relay chain behaviors.
This document discusses a case study on computational self-awareness in smart camera networks. It provides an overview of the EPiCS project, which aims to develop self-aware and self-expressive systems. Surveillance camera networks are presented as an application domain, along with challenges in distributed multi-camera object tracking. The case study then introduces the concept of self-awareness in smart camera networks and provides prerequisites and objectives for participants to develop new strategies for distributed tracking using a simulation environment over the course of a week.
The document discusses how robots may need to be self-aware to be trusted, especially in unpredictable environments. It argues that safety cannot be achieved without self-awareness when a robot's environment is unknown. An internal model allows a robot to simulate possible future actions and outcomes without committing to them. This can provide a minimal level of functional self-awareness for safety. A generic internal modeling architecture is proposed where an internal model evaluates consequences of actions to moderate action selection for safety. Examples of robots using internal models for functions like planning, learning control, and distributed coordination are also provided.
The document discusses ensemble-oriented programming and self-adaptive systems. It provides an overview of the E-Vehicle case study that will be used to demonstrate a Service Component Ensemble Language (SCEL) and its runtime framework in Java (jRESP). The case study involves coordination between users, vehicles, and parking lots to satisfy transportation needs and optimize resource allocation.
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
2. Designed by René Doursat
(ECSO)1. Engineering & Control of Self-Organization
(ME)2. Morphogenetic Engineering
i.e. “Architectures without Architects”
(EE)3. Example: Embryomorphic Engineering
i.e. “Artificial Life Evo-Devo”
3.a. MapDevo
Modular Architecture by Programmable Development
3.b. ProgLim
Program-Limited Aggregation
MORPHOGENETIC ENGINEERING
3. Designed by René Doursat
MORPHOGENETIC ENGINEERING
(ECSO)1. Engineering & Control of Self-Organization
4. Designed by René Doursat
Between natural and engineered emergence
CS (ICT) Engineering: creating and programming
a new, artificial self-organization / emergence
(Complex) Multi-Agent Systems (MAS)
CS Science: observing and understanding "natural",
spontaneous emergence (including human-caused)
Agent-Based Modeling (ABM)
Engineering & Control of Self-Organization:
fostering and guiding complex systems
at the level of their elements
1. Engineering & Control of Self-Organization
5. Designed by René Doursat
Exporting models of natural CS to ICT: “(bio-)inspiration”
already a tradition...
1. Engineering & Control of Self-Organization
ex: genes & evolution
laws of genetics
genetic program,
binary code, mutation
genetic algorithms (GAs),
& evolutionary computation
for search & optimization
ex: neurons & brain
biological neural models
binary neuron,
linear synapse
artificial neural networks
(ANNs) applied to machine
learning & classification
ex: ant colonies / bird flocks
trails, swarms / collective motion
move, deposit, follow “pheromone” /
separation, alignment, cohesion (“boids”)
ant colony optimization (ACO)
graph theoretic & networking problems /
particle swarm optimization (PSO)
“flying over” solutions in high-D spaces
TODAY: simulated in a Turing machine / von Neumann architecture
6. Designed by René Doursat
Exporting models of natural CS to ICT: “(bio-)inspiration”
... and looping back onto an unconventional physical implementation
to fully exploit the "in materio" computational efficiency
DNA computing
synthetic biology
chemical, wave-based
computing
TOMORROW: running in truly parallel roboware, bioware, nanoware, etc.
swarm robotics
genetic algorithms (GAs),
& evolutionary computation
for search & optimization
artificial neural networks
(ANNs) applied to machine
learning & classification
ant colony optimization (ACO)
graph theoretic & networking problems /
particle swarm optimization (PSO)
“flying over” solutions in high-D spaces
1. Engineering & Control of Self-Organization
7. Designed by René Doursat
specific natural or societal
complex system
model simulating this system
generic principles and mechanisms
(schematization, caricature)
new computational discipline
exploiting these principles
to solve ICT problems
1. Engineering & Control of Self-Organization
Exporting models of natural CS to ICT: “(bio-)inspiration”
common shortcoming: the classical "(over-
)engineering" attitude
reintroducing too much exogenous, top-
down design/control
exploit and only “influence” the natural
endogenous properties!
breaking things apart too much
keep the system internally complex
and self-organized!
the stepwise fallacy: start with simple
tasks (ex: XOR) and build up from there
go for the collective attractors right
away! (Kauffman’s “order for free”)
keeping self-organization, but forcing it
into rigid design (logic gate flow RBN)
clinging to computing universality
specialization is more promising!
8. Designed by René Doursat
John Holland, founder of GAs and major
promoter of Complex (Adaptive) Systems: his
titles ~100% CAS
• Hidden order: How adaptation builds complexity
• Emergence: From chaos to order
• Artificial adaptive agents in economic theory
• Outline for a logical theory of adaptive systems
• Studying complex adaptive systems
• Exploring the evolution of complexity in signaling
networks
• Can there be a unified theory of CAS?
• etc.
GECCO, among the biggest (and most
selective) EC conferences:
its tracks in 2009 ~15% CAS?
(... individuals internally complex?)
• ACO and Swarm Intelligence
• Artificial Life, Evolutionary Robotics, Adaptive
Behavior, Evolvable Hardware
• Bioinformatics and Computational Biology
• Combinatorial Optimization and Metaheuristics
• Estimation of Distribution Algorithms
• Evolution Strategies and Evolutionary Programming
• Evolutionary Multiobjective Optimization
• Generative and Developmental Systems
• Genetic Algorithms
• Genetic Programming
• Genetics-Based Machine Learning
• Parallel Evolutionary Systems
• Real World Application
• Search Based Software Engineering
1. Engineering & Control of Self-Organization
Ex. of “over-engineered” bio-inspired domain: evo computation
9. Designed by René Doursat
1. Engineering & Control of Self-Organization
Other examples of “over-engineered” bio-inspired domains:
artificial neural networks (IJCNN, etc.)
multi-agent systems (AAMAS, etc.)
... even artificial life! (Alife, ECAL) is playing down the self-
organization and complex systems properties of living matter
Conversely: “natural” complex systems conferences don’t show
much concern for design, engineering or control issues
overcrowded with (statistical) physicists (ICCS, ECCS, etc.)
or overcrowded with biologists
10. Designed by René Doursat http://iscpif.fr/ecso2014
... and that’s why we need
11. Designed by René Doursat
MORPHOGENETIC ENGINEERING
(ECSO)1. Engineering & Control of Self-Organization
2. Morphogenetic Engineering
i.e. “Architectures without Architects”
(ME)
12. Designed by René Doursat
the brain organisms ant trails
termite
mounds
animal
flocks
cities,
populations
social networks
markets,
economy
Internet,
Web
physical
patterns
living cell
biological
patterns
animals
humans
& tech
molecules
cells
All agent types: molecules, cells, animals, humans & tech
2. Architectures Without Architects
13. Designed by René Doursat
... yet, even human-caused systems are
"natural" in the sense of their
unplanned, spontaneous emergence
the brain organisms ant trails
termite
mounds
animal
flocks
physical
patterns
living cell
biological
patterns
biology strikingly demonstrates
the possibility of combining
pure self-organization and
elaborate architecture, i.e.:
a non-trivial, sophisticated morphology
hierarchical (multi-scale): regions, parts, details
modular: reuse of parts, quasi-repetition
heterogeneous: differentiation, division of labor
random at agent level, reproducible at system level
"Simple"/random vs. architectured complex systems
2. Architectures Without Architects
14. Designed by René Doursat
2. Morphogenetic Engineering
Doursat, Sayama & Michel (2012)
15. Designed by René Doursat
2. Morphogenetic Engineering
Doursat, Sayama & Michel (2012)
16. Designed by René Doursat
2. Morphogenetic Engineering
Doursat, Sayama & Michel (2012)
17. Designed by René Doursat
2. Morphogenetic Engineering
Doursat, Sayama & Michel (2012)
18. Designed by René Doursat
ME brings a new focus inside the ECSO family
exploring the artificial design and implementation of decentralized
systems capable of developing elaborate, heterogeneous
morphologies without central planning or external lead
Morphogenetic Engineering (ME) is about designing the
agents of self-organized architectures... not the architectures directly
swarm robotics,
modular/reconfigurable robotics
mobile ad hoc networks,
sensor-actuator networks
synthetic biology, etc.
Related emerging ICT disciplines and application domains
amorphous/spatial computing (MIT, Fr.)
organic computing (DFG, Germany)
pervasive adaptation (FET, EU)
ubiquitous computing (PARC)
programmable matter (CMU)
2. Morphogenetic Engineering
19. Designed by René Doursat
http://iscpif.fr/MEW2009
1st Morphogenetic Engineering Workshop, ISC, Paris 2009
http://iridia.ulb.ac.be/ants2010
2nd Morphogenetic Engineering Session, ANTS 2010, Brussels
Morphogenetic Engineering:
Toward Programmable Complex Systems
Fall 2012, SpringerR. Doursat, H. Sayama & O. Michel, eds.
http://ecal11.org/workshops#mew
3rd Morphogenetic Engineering Workshop, ECAL 2011, Paris
2. Morphogenetic Engineering
20. Designed by René Doursat
1) O'Grady, Christensen & Dorigo
2) Jin & Meng
3) Liu & Winfield
4) Werfel
5) Arbuckle & Requicha
6) Bhalla & Bentley
7) Sayama
8) Bai & Breen
9) Nembrini & Winfield
10) Doursat, Sanchez, Dordea, Fourquet &
Kowaliw
11) Beal
12) Kowaliw & Banzhaf
13) Cussat-Blanc, Pascalie, Mazac, Luga &
Duthen
14) Montagna & Viroli
15) Michel, Spicher & Giavitto
16) Lobo, Fernandez & Vico
17) von Mammen, Phillips, Davison,
Jamniczky, Hallgrimsson & Jacob
18) Verdenal, Combes & Escobar-Gutierrez
Doursat, Sayama & Michel (2012)
21. Designed by René Doursat
Part I. Constructing
Relatively few robots or components
(possibly originating from a larger,
ambient pool) attach to each other or
assemble blocks together, creating
relatively precise formations. The built
structures are ``coarse-grained'', and
most of them stick figures, i.e., made
of 1-unit wide segments. The space is
the 2D plane, with occasional vertical
elevation into 3D by stacking or
folding.
Part II. Coalescing
A great number of mobile agents
exhibit heterogeneous flocking
behavior. Without literally attaching,
they aggregate and stay near each
other while moving to maintain
neighbor-to-neighbor communication
(e.g., of visual, chemical, or wireless
type). Together, they tend to form
dense, fine-grained clusters that
assume certain ``fluid'' yet stable
shapes. Most contributions focus on
simulated systems, as large-scale
robotic swarms are still too costly and
programmable flocking nano-particles
unheard of.
Doursat, Sayama & Michel (2012)
22. Designed by René Doursat
Part III. Developing
Closer to cell-based models of biological
morphogenesis. Starting from a single
agent and growing to a large size by
repeated, yet differential, division or
aggregation. Mechanisms underlying this
development involve one or several
biological features such as gene
regulation, molecular signalling and
chemotaxis. As a consequence, the
resulting structures exhibit properties of
biotic patterns or tissues, such as
vascularization and segmentation, or
entire organisms, such as arthropods or
branched creatures. Potential
applications range from synthetic biology
and collective robotics to computer
networks
Part IV. Generating
Morphogenetic systems are generated by
successive transformations of
components in 3D space, based on
``rewrite'' rules. These rules are formally
expressed as ``grammars'', which can be
designed by hand or evolved. The
resulting architectures have potential
applications as diverse as natural
computing, robotics, computer graphics
or plant biology.
Doursat, Sayama & Michel (2012)
23. Designed by René Doursat
Other, less distinguishing
features:
Robotic models or applications,
virtual or physical.
Actual physical implementations
with robots or mechanical
components.
Emphasis on various patterns,
textures, or symmetrical shapes,
rather than complicated
morphologies.
Biological models based on real
data, such as fruit fly and rye
grass.
Doursat, Sayama & Michel (2012)
24. Designed by René Doursat
MORPHOGENETIC ENGINEERING
(ECSO)1. Engineering & Control of Self-Organization
(ME)2. Morphogenetic Engineering
i.e. “Architectures without Architects”
(EE)3. Example: Embryomorphic Engineering
i.e. “An Artificial Life Evo-Devo”
25. Designed by René Doursat
Exporting models of natural CS to ICT: “(bio-)inspiration”
already a tradition (although too re-engineered and "decomplexified")
ex: genes & evolution
laws of genetics
genetic program,
binary code, mutation
genetic algorithms (GAs),
& evolutionary computation
for search & optimization
ex: neurons & brain
biological neural models
binary neuron,
linear synapse
artificial neural networks
(ANNs) applied to machine
learning & classification
ex: ant colonies / bird flocks
trails, swarms / collective motion
move, deposit, follow “pheromone” /
separation, alignment, cohesion (“boids”)
ant colony optimization (ACO)
graph theoretic & networking problems /
particle swarm optimization (PSO)
“flying over” solutions in high-D spaces
... or embedded in bioware, nanoware...whether simulated in a Turing machine...
3. Embryomorphic Engineering: Alife Evo-Devo
26. Designed by René Doursat
A new line of bio-inspiration: biological morphogenesis
designing multi-agent models for decentralized systems engineering
Doursat (2006) Doursat, Sanchez, Fernandez
Kowaliw & Vico (2013)
Doursat & Ulieru (2009) Doursat, Fourquet,
Dordea & Kowaliw (2013)
Doursat (2008, 2009)
Embryomorphic Engineering
... or embedded in bioware, nanoware...whether simulated in a Turing machine...
3. Embryomorphic Engineering: Alife Evo-Devo
27. Designed by René Doursat
evolution(My)
development(m/y)
Doursat (2008, 2009)
one individual is
internally complex
learning
3. Embryomorphic Engineering: Alife Evo-Devo
28. Designed by René Doursat
Nothing in biology makes sense
except in the light of evolution
—Dobzhansky, 1973
Nothing in development makes sense
except in the light of complex systems
Not much in evolution makes sense except
in the light of multicellular development
(or molecular self-assembly for unicellular organisms)
Evo-Devo 3. Embryomorphic Engineering: Alife Evo-Devo
29. Designed by René Doursat
Development: the missing link of the Modern Synthesis...
Purves et al., Life: The Science of Biology
evolutionmutation
“When Charles Darwin proposed his theory of evolution by variation and
selection, explaining selection was his great achievement. He could not
explain variation. That was Darwin’s dilemma.”
—Marc W. Kirschner and John C. Gerhart (2005)
The Plausibility of Life, p. ix
“To understand novelty in evolution, we need to understand
organisms down to their individual building blocks, down to their
deepest components, for these are what undergo change.”
?? ??
3. Embryomorphic Engineering: Alife Evo-Devo
30. Designed by René Doursat
NathanSawaya
www.brickartist.com
Development: the missing link of the Modern Synthesis...
“To understand novelty in evolution, we need to
understand organisms down to their individual building
blocks, down to their deepest components, for these are
what undergo change.”
AmyL.Rawson
www.thirdroar.com
3. Embryomorphic Engineering: Alife Evo-Devo
macroscopic,
emergent level
microscopic,
componential
level
emergence
31. Designed by René Doursat
NathanSawaya
www.brickartist.com
Development: the missing link of the Modern Synthesis...
AmyL.Rawson
www.thirdroar.com
generic elementary
rules of self-assembly
macroscopic,
emergent level
microscopic,
componential
level
Genotype Phenotype“Transformation”?
more or less direct
representation
( )
3. Embryomorphic Engineering: Alife Evo-Devo
emergence
32. Designed by René Doursat
MORPHOGENETIC ENGINEERING
(ECSO)1. Engineering & Control of Self-Organization
(ME)2. Morphogenetic Engineering
i.e. “Architectures without Architects”
(EE)3. Example: Embryomorphic Engineering
i.e. “Artificial Life Evo-Devo”
3.a. MapDevo
Modular Architecture by Programmable Development
33. Designed by René Doursat
Doursat, Sanchez, Fernandez, Kowaliw & Vico (2013)
3D Development
3.a. MapDevo – Modular Architecture
34. Designed by René Doursat
3.a. MapDevo – Modular Architecture
Body
Doursat, Sanchez, Fernandez, Kowaliw & Vico (2013)
35. Designed by René Doursat
3.a. MapDevo – Modular Architecture
Limbs
Doursat, Sanchez, Fernandez, Kowaliw & Vico (2013)
36. Designed by René Doursat
Capturing the essence of morphogenesis in an Artificial Life agent model
3.a. MapDevo – Modular Architecture
grad1
div1
patt1
div2
grad2
patt2
div3
grad3
Alternation of self-
positioning (div)
and self-
identifying
(grad/patt)
genotype
each agent
follows the same set
of self-architecting rules (the "genotype")
but reacts differently depending on its neighbors
patt
3
...
Doursat (2008, 2009)
37. Designed by René Doursat
3.a. MapDevo – Modular Architecture
pA
B
V
rr0rerc
GSA: rc < re = 1 << r0
p = 0.05
div
38. Designed by René Doursat
38
grad
EW
S
N
EW
WE WE
NS
3.a. MapDevo – Modular Architecture
39. Designed by René Doursat
39
I4 I6
B4
B3
patt
X Y
. . . I3 I4 I5 . . .
B1 B2 B4B3
wix,iy
GPF : {w }
wki
WE NS
Bi = (Li(X, Y)) = (wix X + wiy Y i) Ik = i |w'ki|(w'kiBi + (1w'ki)/2)
3.a. MapDevo – Modular Architecture
40. Designed by René Doursat
40
I9
I1
(a) (b)
(c)
. . . . . .
WE = X NS = Y
B1 B2 B3 B4
I3 I4 I5
X Y
. . . I3 I4 I5 . . .
B1 B2 B4B3
wiX,Y
GPF
wki
Programmed patterning (patt): the hidden embryo atlas
a) same swarm in different colormaps to visualize the agents’ internal
patterning variables X, Y, Bi and Ik (virtual in situ hybridization)
b) consolidated view of all identity regions Ik for k = 1...9
c) gene regulatory network used by each agent to calculate its expression
levels, here: B1 = (1/3 X), B3 = (2/3 Y), I4 = B1B3(1 B4), etc.
3.a. MapDevo – Modular Architecture
41. Designed by René Doursat
rc = .8, re = 1, r0 =
r'e= r'0=1, p =.01
GSA
I4 I6
E(4)
W(6)
I5I4
I1
N(4)
S(4)
W(4) E(4)
SA
PF
SA4
PF4
SA6
PF6
SA
PF
SA4
PF4
SA6
PF6
all cells have same GRN, but execute different
expression paths determination /
differentiation
microscopic (cell) randomness, but
mesoscopic (region) predictability
Doursat
(2008, 2009)
3.a. MapDevo – Modular Architecture
42. Designed by René Doursat
Bones & muscles: structural differentiation and properties
(a) (b) (c)
3.a. MapDevo – Modular Architecture
(c)
43. Designed by René Doursat
Bones & muscles: structural differentiation and properties
(a) (b) (c)
3.a. MapDevo – Modular Architecture
(c)
44. Designed by René Doursat
Bones & muscles: structural differentiation and properties
(a) (b) (c)
Doursat, Sanchez, Fernandez, Kowaliw & Vico (2013)
3.a. MapDevo – Modular Architecture
(c)
45. Designed by René Doursat
Locomotion and behavior by muscle contraction
3.a. MapDevo – Modular Architecture
46. Designed by René Doursat
Locomotion and behavior by muscle contraction
3.a. MapDevo – Modular Architecture
47. Designed by René Doursat
Locomotion and behavior by muscle contraction
Doursat, Sanchez, Fernandez, Kowaliw & Vico (2013)
3.a. MapDevo – Modular Architecture
48. Designed by René Doursat
Quantitative mutations: limb thickness
GPF
GSA
33
1, 1
p = .05
g = 15
4 6
disc
GPF
GSA
11
tip p’= .05
g’= 15
GPF
GSA
11
tip p’= .05
g’= 15
GPF
GSA
33
2, 1 4 6
disc
p = .05
g = 15
GPF
GSA
11
tip p’= .05
g’= 15
GPF
GSA
33
0.5, 1 4 6
disc
p = .05
g = 15
(a) (b) (c)
wild type thin-limb thick-limb
body plan
module
limb
module
4 6
Doursat (2009)
3.a. MapDevo – Modular Architecture
49. Designed by René Doursat
(a) (b) (c)
antennapedia duplication
(three-limb)
divergence
(short & long-limb)
PF
SA
11
tip p’= .05
GPF
GSA
33
p = .05
4 2
disc
6
PF
SA
11
tip p’= .1
PF
SA
11
tip p’= .03
GPF
GSA
33
p = .05
4 2
disc
6
GPF
GSA
11
p’= .05tip
GPF
GSA
33
p = .05
4 2
disc
GPF
GSA
11
p’= .05tip
4
2
6
Qualitative mutations: limb position and differentiation
antennapedia homology by duplication divergence of the homology
Doursat (2009)
3.a. MapDevo – Modular Architecture
50. Designed by René Doursat
production
of structural
innovation
Changing the agents’ self-architecting rules through evolution
by tinkering with the genotype, new architectures (phenotypes) can be obtained Doursat (2009)
3.a. MapDevo – Modular Architecture
51. Designed by René Doursat
A simple challenge: path-based fitness
f = | end – start | / path length < 1
3.a. MapDevo – Modular Architecture
52. Designed by René Doursat
3.a. MapDevo – Modular Architecture
optimal body size? optimal limb size?
53. Designed by René Doursat
MORPHOGENETIC ENGINEERING
(ECSO)1. Engineering & Control of Self-Organization
(ME)2. Morphogenetic Engineering
i.e. “Architectures without Architects”
(EE)3. Example: Embryomorphic Engineering
i.e. “Artificial Life Evo-Devo”
3.a. MapDevo
Modular Architecture by Programmable Development
3.b. ProgLim
Program-Limited Aggregation
54. Designed by René Doursat
Doursat, Fourquet, Dordea & Kowaliw (2013)
3.b. ProgLim – Program-Limited Aggregation
55. Designed by René Doursat
3.b. ProgLim – Program-Limited Aggregation
56. Designed by René Doursat
Preferential (“scale-free”)
Programmed
Attachment
Networking
3.b. ProgLim – Program-Limited Aggregation
57. Designed by René Doursat
Preferential
Programmed
Attachment
Networking
(ProgNet)
Diffusion-
Program-
Limited
Aggregation
(ProgLim)
Doursat, Fourquet, Dordea & Kowaliw (2013)
3.b. ProgLim – Program-Limited Aggregation
58. Designed by René Doursat
Stereotyped
development
Environment-
Induced
Polyphenism
Doursat, Fourquet, Dordea & Kowaliw (2013)
evol evol
3.b. ProgLim – Program-Limited Aggregation