LIFELIKE Computing Systems

Workshop program 

Session 1 from 11:30 to 13:00 CEST
Chair: Anthony Stein

11:30 Welcome note by the Lifelike Organizers

11:40 LIFELIKE Keynote: Lukas Esterle
Collaborative Systems: Learning and Working Together 

12:20 Chloe M. Barnes, Anikó Ekárt, Kai Olav Ellefsen, Kyrre Glette, Peter R. Lewis and Jim Tørrresen
Overcoming Dynamicity with Plasticity: Neuromodulation for Lifelike Systems 

12:40 Carolina Padilla, Marco A Martínez, Rupendra Raavi and Patrick Hung
Development of Brando: a robotic dog capable of expressing emotions

+++ Lunch break 13:00 to 14:00 +++

Session 2 from 14:00 to 15:30 CEST
Chair: Aishwaryaprajna 

14:00 Sooraj K Babu and Sebastian von Mammen
Learning Classifier Systems as Recommender Systems

14:20 Markus Görlich-Bucher and Jörg Hähner
Towards XCSF-based Identification of Physical Disturbances

14:40 Aleksey Koschowoj, Mathias Pacher and Uwe Brinkschulte
The Next Step in the Evolution of Artificial DNA: the Abstract ADNA

15:00 Juniper Lovato, Laurent Hebert-Dufresne, Jonathan St-Onge, Gabriela Salazar Lopez, Sean P. Rogers, Randall Harp, Anne Marie Stupinski, Ijaz Ul Haq,
and Jeremiah Onaolapo
Diverse Misinformation: impacts of human biases on detection of deepfakes on online social networks

15:20 Interactive Wrap-up  

15:30 End of workshop

Instructions for Presenters

• The presenting authors of contributed papers are asked to prepare a max. 12 minute oral presentation.

• Each presentation is followed by a 5 minute discussion.

• The sequence and time slots of the presentations is given in the final program (to be published very soon).

• Please make sure to get familiar with the general guidelines for attending ALife virtual conference sessions you get provided after registration. 

LIFELIKE 2022 Keynote 

Collaborative Systems: Learning and Working Together 

by Associate Professor Dr. Lukas Esterle, Aarhus University, Denmark

Abstract: Nature, biology, and society have been a great inspiration for computing systems over the past decades. From ants inspiring routing techniques for networks to market-based approaches to allocate resources. In this keynote, we will specifically look at autonomous systems utilising lifelike approaches to facilitate collaboration among them. To begin with, the individual systems require an awareness of the other systems, their capabilities, needs, and goals. Having this understand allows for the individual systems to engage in active collaboration. Afterwards, we will discuss challenges and approaches for collaborative learning and will specifically look at federated learning as a mechanism to bundle knowledge from multiple learners. Finally, we will explore nature inspired approaches to tackle collaborative task assignments for autonomous systems with common goals.

Short Bio: Lukas Esterle is an Associate Professor at Aarhus University, Denmark, where he leads the Autonomous Intelligent Systems group. He received his PhD from the University of Klagenfurt, Austria and was a post-doctoral researcher at the Vienna University of Technology. Afterwards, he was a Marie Skłodowska-Curie fellow at Aston University and the University of Birmingham, UK. His research is on autonomous systems and specifically on the interaction among them with shared as well competing goals.

Aims and Scope of LIFELIKE

Complexity in Information and Communication Technology (ICT) is still increasing, driven by the growing number of devices with vast amounts of computational resources. These systems are also increasingly interwoven into the very fabric of society, playing a role in how we connect together and socialize, how we move, work, and do business, and considering the role of technology in the spread of information, even what we know. As a result, the control of these systems is polycentric and necessarily complex and adaptive. Approaches for control and governance go way beyond traditional notions of central administration, which we note is often simply impossible for human operators.

Based on these insights, a growing movement considers the necessity of capabilities that allow these systems to successfully act and survive in such complex, real environments -- they are supposed to be `lifelike'. 

Inspired by organic systems, our future socio-technical and cyber-physical systems also need to exhibit such characterizing self-x properties. Research initiatives such as Organic Computing, Autonomic Computing as well as Self-aware Computing all share the common goal of understanding and engineering technical systems capable of dealing with uncertainty due to continually changing and highly dynamic environments.

In order to achieve the desired degree of system robustness and flexibility, the envisaged computing systems are:

• Increasingly decentralized into large self-organizing collectives of (semi-)autonomous agents.

• Equipped with sensors and actuators to perceive and modify their productive environment.

• Deployed with machine learning, planning and optimization algorithms from the broad domain of Artificial Intelligence (AI) to render these (sub-)systems autonomously self-learning.

In light of this year’s ALIFE theme, we are also strongly convinced that beyond making systems `intelligent’ through AI technology, the Artificial Life community can deeply contribute to further advance the field of building viable future computing systems — which we call Lifelike Computing Systems.

Characterized by lifelike, or self-x properties such as being highly distributed and thereby acting self-motivated, self-organizing, self-adaptive, self-improving, self-healing, etc., Lifelike Computing systems foster a paradigm shift regarding their design and deployment. As a result, the vision of Organic and Autonomic Computing manifests itself — traditional design-time decisions are moved to the productive runtime and, thus, the systems themselves take over control. Although this would dramatically increase the degree of system autonomy, it also satisfies the conditions for emergence occurring. This aspect, however, should be envisioned as a double-edged sword, since emergent effects can be beneficial but also detrimental; at least for our technical computing systems. In any case, technical systems must comply with necessary safety boundaries apparent in nearly any real-world application with humans involved. Therefore, we deem Lifelike Computing Systems as urgently required to be self-explanatory!

This workshop is intended to provide a forum for discussing the implications and new insights from adopting Artificial Life principles to technical computing systems acting in real-world environments. Additionally, we explicitly emphasize the aspects of interpretability and explainability of the involved algorithms in order to provide a basis for system transparency already at the core of its mechanisms. Besides this self-explanatory property, further key ingredients to reach a specific level of intelligence are self-awareness and the resulting ongoing pursuit for continual self-improvement by means of learning and optimization. The resulting, particular tension between increasing system viability through adopting lifelike characteristics, while at the same time ensuring an appropriate degree of system explainability, validation and compliance to exploration boundaries, constitutes the main motivation and unique topic of this workshop.

Therefore, we solicit research and position papers which are expected to set their focus on at least one or else multiple self-x properties for realizing Lifelike Computing Systems, among others:

Self-organization, i.e., adoption of organic system principles concerning bottom-up evolution of system structures, holarchies, trusted communities or socio-technical design concepts.

Self-explainability, i.e., deriving new metrics for quantification, system validation, guaranteeing, understanding \& trust as well as proper ways for visualization via context-aware and transient interfaces.

Self-improvement, i.e., continual behavior optimization@runtime through mechanisms such as automated algorithm configuration \& selection or evolutionary intelligence as a mechanism to change.

Self-awareness, i.e., establishing autonomous learning behaviour in technical systems by means of techniques such as active learning, transfer learning, online concept drift and novelty detection, efficient reinforcement learning from feedback, or model self-reflection.

Important Dates

• Submission deadline: May 15, 2022

• Extended submission deadline: May 29, 2022

• Decision notification: June 12, 2022

• New decision notification: June 26, 2022 (due to the submission extension)

Submission Information

Papers must be written in English and are expected to report on innovative ideas and novel research results around the topic of LIFELIKE. Reported results and findings have to be integrated with the current state of the art and should provide details and metrics allowing for an assessment of practical as well as statistical significance. Contributions bringing in novel ideas and concepts from related fields such as Organic Computing, Autonomic Computing, Self-aware Computing, etc. are explicitly solicited, but authors are at the same time strongly encouraged to clearly state the relevance and relation to LIFELIKE's as well as ALIFE's main theme.

Submissions must

• conform to the ALIFE submission instructions.

• not exceed 

  • 2 pages for extended abstracts on new ideas, notes, discussion points, or reporting on relevant work recently published elsewhere (e.g. in journals) 

  • 4 pages for position papers, raising intriguing standpoints/hypotheses or summarizing pursued / proposed research agendas  

  • 6 pages  for research papers reporting on original results and novel insights underpinned by experimental or theoretical evidence.   

• be submitted via EasyChair 

Program Committee 

• Jacob Beal, BBN Technologies

• Kirstie Bellman, Topcy House Consulting

• Jean Botev, University of Luxembourg

• Uwe Brinkschulte, University of Frankfurt

• Ada Diaconescu, Telecom ParisTech

• Frank Dürr, University of Stuttgart

• Lukas Esterle, Aston University

• Jörg Hähner, University of Augsburg

• Heiko Hamann, University of Lübeck

• Martin Hoffmann, Bielefeld University of Applied Sciences

• Christian Krupitzer, University of Hohenheim

• Chris Landauer, Topcy House Consulting

• Peter Lewis, Ontario Tech University 

• Erik Maehle, University of Lübeck

• Hella Ponsar, University of Augsburg

• Wolfgang Reif, University of Augsburg

• Gregor Schiele, University Duisburg-Essen

• Bernhard Sick, University of Kassel

• Anthony Stein, University of Hohenheim

• Matthias Tichy, Ulm University 

• Sven Tomforde, Kiel University

• Sebastian von Mammen, University of Würzburg

• Stefan Wildermann, Friedrich-Alexander-University Erlangen-Nuremberg

Contact

• Email:  lifelikecs[at]protonmail[dot]com 

• Twitter:  @lifelikecs