collective behavior

Swarm Robotics represents a dynamic and innovative field at the intersection of robotics, artificial intelligence, and collective behavior. Drawing inspiration from the natural world, particularly from the complex social behaviors exhibited by insects, birds, and fish, this area of study focuses on the development of large numbers of relatively simple robots that operate based on decentralized control mechanisms. The primary goal is to achieve a collective behavior that is robust, scalable, and flexible, enabling the swarm to perform complex tasks that are beyond the capabilities of individual robots.

Principles of Swarm Robotics

Swarm robotics is grounded in the principles of Swarm Intelligence (SI), which emphasizes autonomy, local rules, and the absence of centralized control. The basic premise is that simple agents following simple rules can give rise to complex, intelligent behavior. In swarm robotics, each robot acts based on its local perception and simple interaction rules, without needing a global picture or direct oversight. This approach allows the swarm to adapt dynamically to changing environments and to recover from individual failures effectively.

Applications of Swarm Robotics

Swarm robotics holds promise for a wide range of applications, particularly in areas where tasks are too dangerous, tedious, or complex for humans or individual robotic systems. Some notable applications include:

• Search and Rescue Operations: Swarms can cover large areas quickly, identifying survivors in disaster zones.
• Environmental Monitoring: Autonomous swarms can monitor pollution, wildlife, or agricultural conditions over vast areas.
• Space Exploration: Swarms could be deployed to explore planetary surfaces, gathering data from multiple locations simultaneously.
• Military Reconnaissance: Small, collaborative robots could perform surveillance without putting human lives at risk.

Conclusion: Towards a Collaborative Future

Swarm Robotics is at the forefront of creating collaborative, autonomous systems capable of tackling complex problems through collective effort. By mimicking the natural world's efficiency and adaptability, swarm robotics opens new avenues for exploration, disaster response, environmental monitoring, and beyond. As technology advances, the potential for swarm robotics to transform various sectors becomes increasingly apparent, marking a significant step forward in the evolution of robotic systems and artificial intelligence.

See also: Particle Swarm Optimization (PSO), Ads Shop, D-ID, KI Tools, Prompts ...

Kind regards Schneppat AI & GPT 5

Swarm Intelligence (SI) is a revolutionary concept in artificial intelligence and computational biology, drawing inspiration from the collective behavior of social organisms, such as ants, bees, birds, and fish. It explores how simple agents, following simple rules, can exhibit complex behaviors and solve intricate problems without the need for a central controlling entity. This field has captivated researchers and practitioners alike, offering robust, flexible, and self-organizing systems that can tackle a wide array of challenges across various domains.

Major Algorithms Inspired by Swarm Intelligence

• Particle Swarm Optimization (PSO): Inspired by the social behavior of bird flocking and fish schooling, PSO is used for optimizing a wide range of functions by having a population of candidate solutions, or particles, and moving these particles around in the search-space according to simple mathematical formulae.
• Ant Colony Optimization (ACO): Drawing inspiration from the foraging behavior of ants, ACO is used to find optimal paths through graphs and is applied in routing, scheduling, and assignment problems.

Applications of Swarm Intelligence

SI has been applied in various fields, demonstrating its versatility and efficacy:

• Robotics: For coordinating the behavior of multi-robot systems in exploration, surveillance, and search and rescue operations.
• Optimization Problems: In logistics, manufacturing, and network design, where finding optimal solutions is crucial.
• Artificial Life and Gaming: For creating more realistic behaviors in simulations and video games.

Challenges and Future Directions

While SI offers promising solutions, challenges remain in terms of scalability, the definition of local rules that can lead to desired global behaviors, and the theoretical understanding of the mechanisms behind the emergence of intelligence. Ongoing research is focused on enhancing the scalability of SI algorithms, developing theoretical frameworks to better understand emergent behaviors, and finding new applications in complex, dynamic systems.

Conclusion: A Paradigm of Collective Intelligence

Swarm Intelligence represents a paradigm shift in solving complex problems, emphasizing the power of collective behaviors over individual capabilities. By mimicking the natural world's efficiency, adaptability, and resilience, SI provides a unique lens through which to tackle the multifaceted challenges of today's world, from optimizing networks to designing intelligent, autonomous vehicles. As research progresses, the potential of SI to revolutionize various sectors continues to unfold, making it a vibrant and ever-evolving field of study.

See also: Quantum AI, Particle Swarm Optimization (PSO), Chatbot Development

Kind regards Schneppat AI & GPT 5

Welcome back to "From Our Neurons to Yours," a podcast from the Wu Tsai Neurosciences Institute at Stanford University.

In this episode, we explore the collective intelligence of ant colonies with Deborah Gordon, a professor of biology at Stanford, an expert on ant behavior, and author of a new book, The Ecology of Collective Behavior.

We discuss how ant colonies operate without centralized control, relying on simple local interactions, such as antennal contact, to coordinate their behavior. Gordon explains how studying ant colonies can provide insights into the workings of the human brain, highlighting parallels between different types of collective behavior in ants and the modular functions of the brain.

Listen to the episode to learn more about the intelligence of ant colonies and the implications for neuroscience.

Links
Dr. Gordon's research website
What ants teach us about the brain, cancer and the Internet (TED talk)
An ant colony has memories that its individual members don’t have (Aeon)
The Queen does not rule (Aeon)
Local links run the world (Aeon)
The collective wisdom of ants (Scientific American)
Deborah Gordon: Why Don't Ants Need A Leader? (NPR)
What Do Ants Know That We Don't? (WIRED)

Episode Credits
This episode was produced by Michael Osborne, with production assistance by Morgan Honaker, and hosted by Nicholas Weiler. Cover art by Aimee Garza.

Thanks for listening! If you're enjoying our show, please take a moment to give us a review on your podcast app of choice and share this episode with your friends. That's how we grow as a show and bring the stories of the frontiers of neuroscience to a wider audience.

Learn more about the Wu Tsai Neurosciences Institute at Stanford and follow us on Twitter, Facebook, and LinkedIn.

 Collective behavior, competition within large groups, and why a "perfect socialism" would never occur in the adult industry. 

Wildly NSFW.

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“More than the sum of its parts” is practically the slogan of systems thinking. One canonical example is a beehive: individually, a honeybee is not that clever, but together they can function like shapeshifting metamaterials or mesh networks — some of humankind’s most sophisticated innovations. Emergent collective behavior is common in the insect world — and not just among superstar collaborators like bees, ants, and termites. One firefly, alone, blinks randomly; together, fireflies effect an awe-inspiring synchrony in large, coordinated light shows scientists are only starting to explain. It turns out that diversity is key, even in a swarm; variety improves the “computations” that these swarms perform as they adapt to their surroundings. Watch them self-organize for long enough and you might ask, “Is this what people do? What hidden patterns and emergent genius do we all participate in unawares?” If bees and fireflies inspire that kind of question in you, you’ll find yourself at home in this week’s episode…

Welcome to COMPLEXITY, the official podcast of the Santa Fe Institute. I’m your host, Michael Garfield, and every other week we’ll bring you with us for far-ranging conversations with our worldwide network of rigorous researchers developing new frameworks to explain the deepest mysteries of the universe.

In this conversation, we talk to SFI External Professor Orit Peleg (Google Scholar, Twitter) at the University of Colorado Boulder’s BioFrontiers Institute and Computer Science Department about her research into the collective behavior of bees and fireflies. These humble insects can, together, do amazing things — and what science shows about just how they do it points to deeper insights on the nature of noise, creativity, and life in our complex world.

If you value our research and communication efforts, please rate and review us at Apple Podcasts, and/or consider making a donation at santafe.edu/podcastgive. You can find numerous other ways to engage with us at santafe.edu/engage. Thank you for listening!

Join our Facebook discussion group to meet like minds and talk about each episode.

Podcast theme music by Mitch Mignano.

Follow us on social media:
Twitter • YouTube • Facebook • Instagram • LinkedIn
 

Papers Discussed:

Collective mechanical adaptation in honeybee swarms

Collective ventilation in honeybee nests

Flow-mediated olfactory communication in honey bee swarms

Self-organization in natural swarms of Photinus carolinus synchronous fireflies

Spatiotemporal reconstruction of emergent flash synchronization in firefly swarms via stereoscopic 360-degree cameras

Further Listening & Reading:

Episode 29 — David Krakauer on Coronavirus, Crisis, and Creative Opportunity

Episode 56 — J. Doyne Farmer on The Complexity Economics Revolution

Stefani Crabtree — The archaeological record can teach us much about cultural resilience and how to adapt to exogenous threats

Annalee Newitz — Scatter, Adapt, and Remember

Laurence Gonzales on Behind The Shield Podcast

Michael Mauboussin — The Success Equation

Episode 55 — James Evans on Social Computing and Diversity by Design

@sfiscience on Orit Peleg’s research into honeybee olfactory communication

Whether you live in the USA or have just been watching the circus from afar, chances are that you agree: “polarization” dominates descriptions of the social landscape. Judging from the news alone, one might think the States have never been so painfully divided…yet nuanced public polls, and new behavioral models, suggest another narrative: the United States is largely moderate, and people have much more in common with each other than they think. There’s no denying our predicament: cognitive biases lead us to “out-group” one another even when we might be allies, and the game of politics drives a two-party system into ever-more-intense division, until something has to give. But the same evidence from social science offers hope, that we might find a way to harness our collective thinking processes for the sake of everyone and row together toward a future big enough to hold our disagreements.

Welcome to COMPLEXITY, the official podcast of the Santa Fe Institute. I’m your host, Michael Garfield, and every other week we’ll bring you with us for far-ranging conversations with our worldwide network of rigorous researchers developing new frameworks to explain the deepest mysteries of the universe.

In this episode we talk to SFI External Professor Henrik Olsson and SFI Complexity Postdoctoral Fellow, Omidyar Fellow, and Baird Hurst Scholar Vicky C. Yang about their work on social cognition and political identity. In a conversation that couldn’t be more timely, we ask: How can we leverage an understanding of networks for better political polling and prediction? What are the meaningful differences between one’s values and one’s affiliations? And is the American two-party system working for or against a cohesive republic?

If you value our research and communication efforts, please consider making a donation at santafe.edu/give — and/or rating and reviewing us at Apple Podcasts. You can find numerous other ways to engage with us at santafe.edu/engage. Thank you for listening!

Henrik’s Google Scholar Page

Vicky’s Google Scholar Page

Research we discuss in this episode:

Falling Through the Cracks: A Dynamical Model for the Formation of In-Groups and Out-Groups

A Sampling Model of Social Judgment

Harvesting the wisdom of crowds for election predictions using the Bayesian Truth Serum

Why are U.S. Parties So Polarized? A "Satisficing" Dynamical Model

Do two parties represent the US? Clustering analysis of US public ideology survey

Project Page for the SFI-USC Dornslife Polling Research Collaboration

For more on social cognition and collective decision-making, listen to COMPLEXITY episodes 9 with Mirta Galesic and 20 with Albert Kao.

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Join our Facebook discussion group to meet like minds and talk about each episode

Podcast Theme Music by Mitch Mignano