Lifelike Computing Systems
8th Edition in the Evolution of the Series of Autonomously Learning and Optimizing Systems (SAOS)
++ Submission deadline extended to June 15 (AoE)! ++
++ Reduced registration rates still available until June 8! ++
"Designing Robot Swarms and Bio-hybrid Systems for Robustness and Adaptivity"
by Prof. Dr. Heiko Hamann, University of Lübeck, Germany
Decentralized self-organizing large-scale systems, such as robot swarms, can be designed to be adaptive, robust, and scalable. However, developing robot and system behaviors that are robust and adaptive to dynamic environments, dynamic system size, and faults is still challenging. We go through a number of examples from swarm robotics, such as a swarm showing robust scalability and a morphogenesis behavior that is robust to damages. In a second part of the talk, we discuss bio-hybrid systems of natural plants interacting with robotic nodes. We use methods of machine learning to model natural plants and to guide their growth and motion with an autonomous system. In bio-hybrid systems we can exploit natural adaptive behaviors to build, for example, systems that self-repair.
Since 2017 Heiko Hamann is professor for service robotics at the University of Lübeck, Germany. He coordinated the EU-funded project flora robotica that develops and investigates closely linked symbiotic relationships between robots and natural plants to explore the potentials of a plant-robot society able to produce architectural artifacts and living spaces. His main research interests are swarm robotics and evolutionary robotics, especially collective decision-making and the emergence of swarm behaviors triggered by intrinsic motivation. He was assistant professor at the University of Paderborn, Germany and did his postdoctoral training at the zoology department of the University of Graz, Austria.
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. As a result, the central administration of present and future systems becomes impossible for human operators. Based on that insight and in order to tackle this development, modern systems necessitate capabilities allowing them to successfully act and survive in such complex real-world environments — they are supposed to be `life-like’.
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 Life-like Computing Systems.
Characterized by life-like, or self-x properties such as being highly distributed and thereby acting self-motivated, self-organizing, self-adaptive, self-improving, self-healing, etc., Life-like 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 Life-like 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 life-like 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 Life-like 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.
Submission deadline: May 31, 2020
- Extended Submission deadline: June 15, 2020
- Decision notification: July 1, 2020
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.
Anthony SteinUniversity of Hohenheim (DE)
Sven TomfordeKiel University (DE)
Jean BotevUniversity of Luxembourg (LU)
Peter LewisAston University (UK)
Program Committee (to be completed)
- Jacob Beal, BBN Technologies
- Kirstie Bellman, Topcy House Consulting
- Jean Botev, University of Luxembourg
- Uwe Brinkschulte, University of Frankfurt
- Ada Diaconescu, Telecom ParisTech
- Lukas Esterle, Aston University
- Jörg Hähner, University of Augsburg
- Heiko Hamann, University of Lübeck
- Christian Krupitzer, University of Würzburg
- Chris Landauer, Topcy House Consulting
- Peter Lewis, Aston University
- Erik Maehle, University of Lübeck
- Sanaz Mostaghim, Otto von Guericke University Magdeburg
- Gero Mühl, University of Rostock
- Christian Müller-Schloer, Leibniz University Hannover
- Christian Renner, University of Lübeck
- Wolfgang Reif, University of Augsburg
- Stefan Rudolph, University of Augsburg
- Bernhard Sick, University of Kassel
- Anthony Stein, University of Hohenheim
- Claudio Juan Tessone, University of Zurich
- Sven Tomforde, Kiel University
- Sebastian von Mammen, University of Würzburg
- Torben Weis, University of Duisburg-Essen
Previous Editions of LIFELIKE/SAOS
- SAOS Workshop 2019: https://www.organic-computing.de/saos19
- SAOS Workshop 2018: https://www.informatik.uni-augsburg.de/de/lehrstuehle/oc/Veranstaltungen/oc-ws-arcs18/
- SAOS Workshop 2017: https://www.informatik.uni-augsburg.de/de/lehrstuehle/oc/Veranstaltungen/oc-ws-arcs17/
- SAOS Workshop 2016: https://www.informatik.uni-augsburg.de/de/lehrstuehle/oc/Veranstaltungen/oc-ws-arcs16/
- SAOS Workshop 2015: https://www.informatik.uni-augsburg.de/de/lehrstuehle/oc/Veranstaltungen/oc-ws-arcs15/
- SAOS Workshop 2014: https://www.informatik.uni-augsburg.de/de/lehrstuehle/oc/Veranstaltungen/oc-ws-arcs14/