One relevant area is the motivation of behaviour for artificial agents, both virtual and real. 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?"
Related to an agent's behaviour is also the question on how and where the necessary computation to realise this behaviour is performed. Can information be used to quantify the morphological computation of an embodied agent 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.
|13:00||-||13:05||Christoph Salge||Opening and introduction|
|13:05||-||13:55||Chis Adami||Keynote "Information-theoretic musings concerning the origin and evolution of life"|
|13:55||-||14:20||Simon D. Levy||"Wittgenstein's Robot: Philosophy, Information, and Artificial Life"|
|14:35||-||15:00||Claudius Gros||"The Fisher information as a guiding principle for self-organizing processes"|
|15:00||-||15:20||Georg Martius||"Predictive information as a drive for self-organizing behavior."|
|15:20||-||15:35||Tobias Morville||"The Homeostatic Logic of Reinforcement Learning"|
|15:35||-||16:00||Christoph Salge||"Perspective of information theoretic inceptives"|
The program and all abstract can be downloaded here as [pdf].
If you want to participate in the workshop by giving a talk we would invite you to send us an email with
Specifically for students we also offer the option to submit for a shorter student talk, to present some early results, and discuss their approach to the field. In this case, please submit a 1 - 2 page long extended abstract and indicate that you are interested in a student talk.If there are any questions, or if you just want to indicate interest in submitting or attending, please feel free to mail us at email@example.com .
|Abstract submission deadline||12. May 2014|
|Notification of acceptance||23. May 2014|
|Early bird registration||13. June 2014|
|Workshop date||30. July 2014|
|Special issue deadline (see below)||28 February 2015|
For more information on how to register, please visit the ALife 2014 homepage [here].
The open access journal Entropy sponsors this workshop by an open call, special issue on the topic of "Information Theoretic Incentives for Cognitive Systems"More details will be announced to emails received via firstname.lastname@example.org and over the alife and connectionists mailing lists.