Artificial Life aims to understand the basic and generic
principles of life, and demonstrate this understanding by producing
life-like systems. In recent years, many research fields have gained
interest in Information Theory due to its domain independent
quantitative and qualitative descriptive power.
In Artificial Life research Information Theory offers a language to express principles for the generation, motivation, understanding, and quantification of behaviour and other processes in artificial agents. Instead of learning to perform a specific task, informational measures can be used to define concepts such as boredom, empowerment or the ability to predict one's own future. Intrinsic motivations derived from these concepts allow us to generate behaviour, ideally from an embodied and enactive perspective, which are based on basic but generic principles. The key questions here are: What are the important intrinsic motivations a living agent has, and what behaviour can be produced by them? Similarly, behaviour can be analysed with information theoretic measures to study its complexity or understand information flows. This may also be useful in answering the question on how and where computation is realised. Can the morphological computation of an agent's embodiment be quantified and to what degree are the computational limitations of an agent influencing its behaviour?
Another area of interest is the guidance of artificial evolution or adaptation. Assuming it is true that an agent wants to optimise its information processing, possibly obtain as much relevant information as possible for the cheapest computational cost, then what behaviour would naturally follow from that? Can the development of social interaction or collective phenomena be motivated by an informational gradient? Furthermore, evolution itself can be seen as a process in which an agent population obtains information from the environment, which begs the question of how this can be quantified, and how systems would adapt to maximise this information?
The common theme in those different scenarios is the identification and quantification of driving forces behind evolution, learning, behaviour and other crucial processes of life, in the hope that the implementation or optimisation of these measurements will allow us to construct life-like systems.
| 10:00 | - | 10:05 | Georg Martius | Opening and introduction |
| 10:05 | - | 10:50 | Fumiya Iida | Keynote "Adaptation of locomotion behaviors in model-free physical robot evolution" |
| 10:50 | - | 11:20 | Coffee break | |
| 11:20 | - | 11:45 | Joseph T. Lizier | "What information dynamics can tell us about artificial life systems" |
| 11:45 | - | 12:10 | Christoph Salge | "Swarm Behaviour Motivated by Information" |
| 12:10 | - | 12:35 | Andrei Robu | "Time-keeping with Limited Clocks" |
| 12:35 | - | 13:00 | J. Michael Herrmann | "Evaluation of Model Learning in Autonomous Robots" |
If you want to participate in the workshop by giving a talk we would
invite you to send us an email
with
We also offer presentation slots for students with partial travel funding. Please apply as usual with an extended abstract, but indicate that you are interested in student funding.
If there are any questions, or if you just want to indicate interest in submitting or attending, please feel free to mail us at itialife@gmail.com.
| Abstract submission deadline | 8. May, 2015 |
| Notification of acceptance | 15. May, 2015 |
| Workshop date | 20. July, 2015 |
For more information please visit the ECAL 2015 homepage [here]
