LIFELIKE Computing Systems

9th Edition in the Evolution of the Workshop Series of Autonomously Learning and Optimizing Systems (SAOS)

Details regarding the exact date and time tba

hosted as satellite event of ALIFE 2021 on July 19th 2021

++ Workshop Proceedings out now! ++

Agenda of the workshop day July 19, 2021 (all times are CEST)


chaired by Anthony Stein, University of Hohenheim, DE

10:00 - 10:05 Welcome Note of the LIFELIKE organizers 

10:05 - 10:30   "Lifelike Computing Systems"
presented by Jean Botev, University of Luxembourg,  LU

10:30 - 11:30 LIFELIKE Keynote "Sharks, Zombies and Volleyball - (Super)Natural Computation Gone Wild"
by Dr. Felipe Campelo, Aston University, UK

Contributed Papers I

chaired by Anthony Stein, University of Hohenheim, DE

11.30 to 11.55   "Analysing Metaheuristic Components"
presented by Helena Stegherr, University of Augsburg, DE

++ 12.00 to 12.30   Lunch break ++ 

Contributed Papers II

chaired by Sven Tomforde, Kiel University, DE

12.30 to 12.55   "What I Want in a (Computational) Partner"
presented by Christopher Landauer, Topcy House Consulting, CA

12.55 to 13.20   "Learning Classifier Systems for Self-Explaining Socio-Technical-Systems"
presented by Michael Heider, University of Augsburg, DE

13.20 to 13.45   "A Concept for Self-Explanation of Macro-Level Behaviour in Lifelike Computing Systems"
presented by Sven Tomforde, Kiel University, DE


chaired by Peter Lewis, Ontario Tech University, CA

13.45 to 14.25   Discussion

14.25 to 14.30   Summary and Good bye 


"Sharks, Zombies and Volleyball - (Super)Natural Computation Gone Wild"

by Dr. Felipe Campelo, Aston University, UK

Abstract: The field of meta-heuristic search algorithms has a long history of finding inspiration in natural systems. Starting from classics such as Genetic Algorithms and Ant Colony Optimization, we have recently witnessed an explosion of natural (and sometimes supernatural) methods in the literature - from Birds and Barnacles to Zombies and Reincarnation. While metaphors can often be powerful inspiration tools, it can be argued that the emergence of hundreds of barely discernible algorithmic variants under different labels and nomenclatures has done little to advance science, be it in terms of understanding and simulating biological systems, or of contributing generalisable knowledge or design principles for global optimisation approaches. In this talk we'll visit the Evolutionary Computation Bestiary and use it to discuss some of the possible causes and consequences of this trend, as well as some efforts aimed at moving the area of metaheuristics towards a better balance between inspiration and scientific soundness. 

Short Bio: Felipe Campelo is a Senior Lecturer in Computer Science at Aston University. He received his BSc in Electrical Engineering from UFMG/Brazil in 2003, and his MSc (Information Science and Technology) and PhD (Systems Science and Informatics) from Hokkaido University/Japan in 2006 and 2009, respectively. His research focuses on the development of integrated solution pipelines for prescriptive data analytics, seamlessly connecting data mining, multi-objective optimisation and decision support systems for the solution of a variety of problems in science and engineering, from electromagnetic devices and power distribution systems to bioinformatics. He is also involved with the development of methodologically and statistically sound protocols for the experimental comparison of optimisation algorithms. Felipe is a member of the IEEE Computational Intelligence Society, the ACM Special Interest Group on Genetic and Evolutionary Computation, and the Foundation for Open Access Statistics. 

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:

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 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

Organizing Committee

Anthony Stein

University of Hohenheim (DE)

Sven Tomforde

Kiel University (DE)

Jean Botev

University of Luxembourg (LU)

Peter Lewis

Ontario Tech University (CA) 

Program Committee 

Previous Editions of LIFELIKE/SAOS