The 2017 EvoMUSART conference took place in Amsterdam as part of the EvoStar collection of conferences and workshops. I presented a new paper on “Niche Constructing Drawing Robots”. Based on previous work with software agents, in this work I implemented parts of the density preference niche construction algorithm in a Pololu M3Pi robot. The conference took place from the 19th – 21st of April.
This paper describes a series of experiments in creating autonomous drawing robots that generate aesthetically interesting and engaging drawings. Based on a previous method for multiple software agents that mimic the biological process of niche construction, the challenge in this project was to re-interpret the implementation of a set of evolving software agents into a physical robotic system. In this new robotic system, individual robots try to reinforce a particular niche defined by the density of the lines drawn underneath them. The paper also outlines the role of environmental interactions in determining the style of drawing produced.
I also have a paper with my PhD student, the amazing Patrick Hutchings (who did all the work of course) on “Using Autonomous Agents to Improvise Music Compositions in Real-Time”. Here’s the abstract for that paper:
This paper outlines an approach to real-time music generation using melody and harmony focused agents in a process inspired by jazz improvisation. A harmony agent employs a Long Short-Term Memory (LSTM) artificial neural network trained on the chord progressions of 2986 jazz ‘standard’ compositions using a network structure novel to chord sequence analysis. The melody agent uses a rule-based system of manipulating provided, pre-composed melodies to improvise new themes and variations. The agents take turns in leading the direction of the composition based on a rating system that rewards harmonic consistency and melodic flow. In developing the multi-agent system it was found that implementing embedded spaces in the LSTM encoding process resulted in significant improvements to chord sequence learning.
References and Links to Papers
McCormack J. (2017) Niche Constructing Drawing Robots. In: Correia J., Ciesielski V., Liapis A. (eds) Computational Intelligence in Music, Sound, Art and Design. EvoMUSART 2017. Lecture Notes in Computer Science, vol 10198. Springer, Cham [pdf of the paper] Selected for Best Paper Award
Hutchings P., McCormack J. (2017) Using Autonomous Agents to Improvise Music Compositions in Real-Time. In: Correia J., Ciesielski V., Liapis A. (eds) Computational Intelligence in Music, Sound, Art and Design. EvoMUSART 2017. Lecture Notes in Computer Science, vol 10198. Springer, Cham [pdf of the paper]