[How will artificial life impact the future?]

Multi-agent systems Robots Neural networks AI Added on 26/10/2014

Beta-Life book coverIn 2013, I was a part of the TRUCE workshop at the Alife conference. The workshop brought together scientists and creative writers in order to create a cool book full of stories about A-life (artificial life) and artificial intelligence and about how it will impact our society in the future.

As I am very interested in swarm robotics, sci-fi games and movies and generally how the future will look like when robots run around and are part of our everyday lives, I jumped at the opportunity to cooperate on the project. My idea for a story, in which government uses insect-size robots to spy on and silently kill people, was wonderfully expanded on and crafted into a short story by Robin Yassin-Kassab.

The book is called Beta-Life and there are many other stories inside, with topics like how data that we leave behind on the Internet and in the physical world will be (ab)used in the future, or how we will able to use our insights from biology to grow skyscrapers. It should be great fun to read for any sci-fi and science enthusiasts. What’s great about this book is that each story is accompanied by an afterword from a scientist who created the story idea, so the book not only feeds the imagination but also offers a great insight into current trends in the A-life and AI research.



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Designing robot swarms

 In software engineering, a design pattern associates a particular class of known problem with a particular class of effective solution. Analogously, swarm robot engineers would benefit from design patterns that each associate specific robot control schemes with desired collective performance. In this project, we characterise such design patterns for robot swarms in the context of collective foraging and task allocation.

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Foraging Strategies in Nature and Their Application to Swarm Robotics

 While foraging is a task often experimented with in swarm robotics, it is often the case that foraging strategies inspired by nature are chosen without careful consideration. Foraging strategies including solitary foraging, behavioural matching, stigmergy, signaling to guide others and coordinated and cooperative hunting are identified and their implementation costs in robots, as well as their suitability for different scenarios is discussed.

pyCreeper

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