NACO special issue editorial
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The 13th European Conference on Artificial Life (ECAL 2015) was held in York, United Kingdom, 20–24 July 2015, hosted by the York Centre for Complex Systems Analysis at the The University of York. ECAL 2015 showcased a wide range of topics in Artificial Life, bringing together world-leading researchers to discuss the latest advance. Artificial Life is an interdisciplinary field, and as such submissions from across the spectrum of scientific and humanities disciplines were presented, that considered the main conference themes of Embodiment, Interaction, Conversation.
The full conference proceedings of the contributed peer reviewed papers Andrews et al. (2015) were published by MIT Press. This issue of Natural Computing brings together five of the best papers from the conference, as determined by the original review process. The authors were invited to revise and extend their original contributions for this special issue.
2 The papers
2.1 Mayne and Adamatzky
The slime mould’s actin signalling network forms the basis of its natural computational properties, but the way these networks carry out their computations is not fully understood. One way to approach this problem is via a model of signal transmission through cell arrays of similar topology. Mayne and Adamatsky find evidence for Boolean logic operations within signal–signal interactions, which may form the basis of the remarkable computational behaviour of the slime moulds.
Neuroscience research underpins the work of Edvarsen, who uses an artificial neural network model of grid cells for spatial reasoning. Grid cells hold a multi-scale representation of spatial information which can be used to encode information regarding position of both the self and of other objects, such as the location of the destination. This model of the way grid cells encode position allows a simulated agent to calculate a path towards the goal via the process of decoding the grid cell representation.
2.3 Gomes, Mariano and Christensen
Simulation of the evolution of co-operation at a different level is presented in the work by Gomes, Mariano and Christensen, where it is used to solve a simulated task in a heterogeneous robot team. There are two classes of robot here with different capabilities: a relatively unsophisticated land-based robot that can merely navigate its local environment co-operates with an aerial robot that can detect target items and relay pertinent information to the ground robot. Thus the two robots have to learn different skill sets whilst also learning what information to communicate and how to interpret it.
2.4 Sulyok, McPherson and Harte
The studies above describe artificial life problems where the goal can be defined precisely, but there are many problems in which the quality of the solution is less tangible. Our final paper in this special issue, by Sulyok, McPherson and Harte, shows that artificial life can find effective representations in music composition. Here, the goal is to evolve musical pieces that emulate the qualities of the Bach keyboard exercises. This is achieved by evolving the characteristics of a composition automaton, which holds a representation of the corpus that is used to generate new pieces of work with similar characteristics of repetition and variation.
Our thanks go to all the contributors for their hard work in getting their papers prepared and revised. All submissions received multiple reviews from external reviewers, and we thank them for their timely and in-depth reviews.
- Andrews P, Caves L, Doursat R, Hickinbotham S, Polack F, Stepney S, Taylor T, Timmis J (eds) (2015) In: Proceedings of the European Conference on Artificial Life 2015. MIT Press, CambridgeGoogle Scholar