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03/08/2021 2:44am
Amazon’s New Patent Wants to Combine #drones with Trucks for Deliveries [LINK]
27/07/2021 3:35am
What is the best #simulation tool for #robotics ? A useful article for those considering different options at the m… [LINK]
14/07/2021 7:59am
RT @hardmaru: Nice article from Unity about multi-agent RL DodgeBall Some strategies that emerged include an agent dedicated to defend the…
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RT @joefresna: Check out the latest *special* issue of Swarm Intelligence @SpringerNature on "Collective decision making in living and arti…
09/07/2021 2:52am
High quality code is actually CHEAPER to produce in the long run - a great article that I absolutely stand behind!… [LINK]

[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.

Impressions from ALIFE 14 New York

 This summer, I attended the Artificial Life conference in New York. There were some interesting and not-so-interesting talks, but generally I am very glad I went. I had a chance to meet some great people and more importantly, to get much needed feedback on my own research. I also got offered to try out real robots in my research.

Stardust Colonies Resurrected

 I've been up to a lot lately, getting back to my Stardust Colonies game project. Some might remember that I released a demo about one and half years ago. I managed to get a lot of useful feedback for the game and I am now ready to take the development to the next level.

Impressions from ECAL 2013

 I recently returned from Taormina, Sicily where I attended the ECAL 2013 conference. It was so amazing that I felt I had to share my experience from it on this blog.

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

 The main purpose of pyCreeper is to wrap tens of lines of python code, required to produce graphs that look good for a publication, into functions. It takes away your need to understand various quirks of matplotlib and gives you back ready-to-use and well-documented code.