Abstract

Artificial life is becoming increasingly important, not just to scientists, but to the wider global community. The growing challenges we face (energy, the environment, a changing demographic profile, to name but a few) will require inherently inter-disciplinary strategies. Artificial life and unconventional computing technologies will play an important role in addressing these issues.
In this unconventional workshop, we will create a vision for what the world might look like more than fifty years from now, when artificial life is embedded in our everyday existence. Our aim is to spark a wider debate about the applicability and relevance of unconventional computing techniques, and to imagine a long-term picture of how they may come to influence our lives.
We offer a unique opportunity for scientists to collaborate with short story writers, journalists and artists to create a collection of speculative fiction, feature articles and artworks, based on the theme of "Unconventional Computing in 2070". Scientists will work (on the day) with authors to draft short stories with a "ucomp" theme, and with journalists to write accessible, popular science introductions to their field of research. While this is happening, an artist will sketch out initial studies for a number of pieces, each based on a different story. The scientists will also have the opportunity to provide an afterword to each story, once they are written, describing the scientific background to the fictional work.
Scientists will be required to "pitch" their ideas (based on their research) at the beginning of the workshop, and then the authors will each select one as the basis for their story. The remaining ideas will then be used as the basis for a number of feature articles. All stories, afterwords, articles and artworks will be collected together in a published volume.
TRUCE is a Coordination Action, supported by the Future and Emerging Technologies (FET) programme within the ICT theme of the Seventh Framework Programme for Research of the European Commission. The aim of this three-year project is to support and facilitate inter-disciplinary research into the foundations and application of unconventional computing.

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Abstract

Artificial Life was originally conceived as a powerful tool to answer to the question about the nature of life. Many researches in the last decades drew attention on different aspects, including chemistry, biology, evolution and some minimal forms of cognition, so as to study a complex phenomenon at different scale domains. Focusing on cognition, AL techniques have been utilized to uncover minimal mechanism of important cognitive processes such as categorization, language, spatial behavior, motor control and the like. However, following this bottom up AL approach we need to address higher cognitive processes in an integrated manner so as to exploit the full power of AL approach. Hence, the aim of this workshop is to promote and to encourage AL modeling of higher cognitive processes, integrating complex motor systems to complex higher cognition. Topics of interest include, but are not limited to: Spatial Cognition, Language, Mental Imagery, Decision Making, Social Learning, Cultural Transmission, Role Allocation, Active Perception, Awareness and Categorisation.

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Abstract

Computer synthesized world models are now much richer than those 20 years ago. Nowadays, a great deal of data can be acquired from various fields, and the world presented by this data is much richer than artificial ones. Analysis and synthesis of the vast real-time dimension of this data, as well the massive data flow over long time scales has now become possible. This is what we call the era of Massive Data Flow (MDF).
Huge floods of data are often referred to as Big Data, which connotes the era of "business" using big data; from more protable Web advertisements to customized medical services. What we aim to present here, by the name of MDF, is not business applications but the discussion of MDF as a basic science, which we name MDF science. We focus not only on the quantitative, but also the qualitative diversity available today. For example, if you were free to take the spatial and temporal data from all the cells of a cat, anytime and anywhere, what would you do? Would it significantly advance the problem of development? Or, in doing so would you come to understand what is life? We notice a striking absence of an adequate theory or model of MDF and of an epistemological account for MDF science. For example, in the Artical Life community, we have tried to understand the behavior of high-dimensional dynamical systems by projecting them onto a low-dimensional space. However, this is not applicable for MDF science. MDF has revealed that such conventional means do not work well and we are required to develop new means of analysis and concepts.
How to understand a system without reducing its complexity is the slogan of complex systems science. Challenges in a variety of cross-disciplinary sciences under this slogan still need to be encouraged and a new epistemology for analyzing MDF without losing its complexity is still yet to be proposed. We have organized several such sessions in the Japanese Artificial Intelligence community, and it is now time to propose a session in the Artificial life community to widely discuss the potential of artificial life in MDF!
Therefore, we have organized a workshop by unifying two recently developed concepts; one is known as Living Technology and the other is Web science. Any basic science can lead to innovative applications and artificial life studies is no exception. The purpose of living technology is to bring to fruition the concepts developed through the study of artificial life, such as self-reproduction, autonomy, enaction, robustness, open-ended evolution, and evolvability in a real-world context.
The Web is one of the most complex artificial systems that we know. Web science is a new field of computer science which aims to manage new media based on the enormous amount of relevant information collected, analyzed and synthesized, in order to provide better communication and services between people. We are now analyzing Web dynamics as a larger model, when compared to the traditional size of neural nets, to understand a brain dynamics in terms of information flow, the default mode network, and bursting behavior. Creating and studying a minimalistic model of life has been the traditional approach to artificial life. Here we take a maximalistic model for life (creating a large and complex model). Thus, by extending the models of artificial life into the MDF world, we would like to argue what sort of new technology and concepts we can develop in order to understand life in both the artificial and the real world. What living technology can we think of by applying the concept of artificial life to Web science? How do we build a new understanding of the living condition through MDF? We hope that these kinds of questions around MDF will be intensively discussed in this workshop.

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Abstract

This workshop is at the intersection of several disciplines, from ethology to swarm intelligence, from collective robotics to evolutionary linguistics. Despite very heterogeneous, these disciplines share a common ground when they refer to the mechanisms and the dynamics of social interaction, both at the ontogenetic and the phylogenetic level (e.g., referring to developmental or evolutionary factors).

Despite the similar scientific questions, the study of collective behaviour and social dynamics is characterised by very different approaches, and few places for discussion and comparison are available. Therefore, the workshop represents a unique occasion in which such a juxtaposition of diverse disciplines can take place. The goal of the workshop is to confront the current trends and advancements in the study of collective behaviour and social dynamics, and to promote cross-fertilisation and contamination between disciplines and approaches that rarely meet together.

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Abstract

Rodney Brooks once famously said that the real world is its own best model. His statement is particularly true in the context of genetic algorithms, where novel solutions to problems are often discovered by exploiting the substrate of evolution. We use the term Evolution of Physical Systems (EPS) to refer to evolutionary algorithms which occur entirely in real-­world physical substrates rather than in simulation. The term encompasses both parallel Embodied Evolution (Watson et al., 2002), in which evolution is distributed across a population of robots, as well as Evolutionary Robotics (Floreano and Mondada, 1994) where evaluation is serialized on a single robot. Notable examples of EPS occur across a wide variety of systems, ranging from Robotics (Zykov et al., 2004)] to FPGAs (Thompson, 1996) to 3D printers (Rieffel and Sayles, 2010). Although EPS comes at a cost (the speed of the real world, unlike CPUs, does not follow Moore’s Law), by definition it avoids the “reality gap” imposed by simulation, and has produced novel and tangeable real­world results. Regardless of application or method, all implementations of EPS are bound by many of the same constraints and technical challenges. The aim of this workshop is to bring together researchers who are currently involved in the Evolution of Physical Systems, as well as those interested in the technique, in order to share ideas and innovations. As the frontiers of artificial life move from the computer to the petri dish, the Evolution of Physical Systems offers to provide inroads into domains which are otherwise impossible to simulate.

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Abstract

Autonomous robot systems tend to have limited learning capabilities. Reinforcement learning systems use a simple evaluative feedback to learn a robot control. This has the advantage of not assuming pre-defined knowledge about ideal robot actions in a particular situation, but has the disadvantage of requiring many robot actions and/or learning cycles before an acceptable mapping from perceptions to actions is found. Evolutionary learning systems manage a population of hypothetical control strategies in parallel. This can lead to a global optimal robot control, but again many learning cycles are needed. Therefore learning is often done in a simulation. How to use simulations well and map results to reality are open issues. This workshop addresses the challenge to develop efficient and versatile learning architectures for autonomous robot systems, with the main focus on adequate evolutionary and reinforcement learning algorithms.

List of Topics:

• Mobile robot navigation by means of reinforcement learning
• Combining offline- and online learning methods for robot control
• Reinforcement learning by evolutionary algorithms of neural network-based and other robot controllers
• Developmental and epigenetic robotics
• Combining modelling and parameter estimation by reinforcement learning
• Combining evolutionary and reinforcement learning (Hybrid approaches)
• Cascaded and hierarchical learning architectures
• Accelerating learning through imitation and transfer learning
• Knowledge-based reinforcement learning
• Balancing exploration and exploitation of acquired knowledge
• Simulated environments for autonomous robot learning scenarios
• Minimizing the simulation reality gap

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Abstract

Collective Adaptive Systems (CAS) is a broad term that describes large scale system that comprise of many units/nodes, each of which may have their own individual properties, objectives and actions. Decision-making in such a system is distributed and possibly highly dispersed, and interaction between the units may lead to the emergence of unexpected phenomena. CAS are open, in that nodes may enter or leave the collective at any time, and boundaries between CASs are fluid. The units can be highly heterogeneous (computers, robots, agents, devices, biological entities, etc.), each operating at different temporal and spatial scales, and having different (potentially conflicting) objectives and goals.
Our society increasingly depends on such systems, in which collections of heterogeneous 'technological' nodes are tightly entangled with human and social structures to form 'artificial societies'. Yet, to properly exploit them, we need to develop a deeper scientific understanding of the principles by which they operate, in order to better design them.
This workshop solicits conceptual papers that address new methodologies, theories and principles that can be used in order to develop a better understanding of the fundamental factors underpinning the operation of such systems, so that we can better design, build, analyse such systems.
We expect that such a research effort will require significant inter-disciplinary working, and that ideas will come from communities such as ALife, Biology, Games Theory, Evolutionary Computing, Network Science, etc.

Suggested Topics (but not limited to):

• Novel theories relating to operating principles of CAS
• Novel design principles for building CAS systems
• Insights into the Evolutionary Properties
• Insights into Emergent Properties
• Insights into general properties of large scale, distributed CAS
• Methodologies for studying, analysing and building CAS
• Frameworks for analysing or developing CAS
• Case-studies/Scenarios that can be used to investigate CAS properties

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Abstract

Systems biology aims at providing a system-level understanding of biological systems by unveiling their structure, dynamics and control methods. The intrinsic multi-scale nature of these systems, both in space, in organization levels, and in time, makes extremely difficult to model them in a uniform way, e.g., by means of differential equations or discrete stochastic processes. Furthermore, such models are often not easily amenable to formal analysis and their simulations at the organ or even the cell level are frequently impractical. Indeed, an important open problem is finding appropriate computational models that scale well for both simulation and formal analysis of biological processes. Hybrid modeling techniques, combining discrete and continuous processes, are gaining more and more attention in systems biology, and they have been applied to successfully capture the behavior of several biological complex systems, ranging from genetic networks, biochemical reactions, signaling pathways and cardiac tissues electrophysiology. This workshop aims at collecting scientists working in the area of hybrid modeling applied to systems biology, in order to discuss about current achieved goals, current challenges and future possible developments.

Topics of interest include, but are not limited to:

• Hybrid models of biological systems (case studies of genetic, cellular networks, models of tissues, etc.)
• Models of biological systems with hybrid behavior
• Computational and mathematical analysis techniques for hybrid systems (i.e. reachability, model checking, abstract interpretation, bifurcation theory for hybrid dynamical systems, stability analysis, etc.), with applications in Systems Biology
• Hybrid system identification techniques (learning the model from the experimental data)
• Efficient simulation techniques for hybrid systems Hybrid modeling languages for biological systems
• Hybrid systems coping with incomplete and uncertain information
• Hierarchical Hybrid Systems for multi scale analysis
• Stochastic Hybrid Systems
• Hybrid simulation (stochastic/deterministic) methods for biological systems
• Abstraction and Approximation techniques
• Hybrid Systems modeling for Synthetic Biology
• Hybrid Control of biological systems
• Sensitivity analysis for hybrid systems
• Behaviour-driven parameters identification for hybrid systems
• Analysis and simulation tools.

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Abstract

Cells are the basic entities upon which all life forms known today on Earth are based. Such cells are the result of billions of years of Darwinian evolution, hence even the simplest bacterium has developed a very complex network of chemical pathways allowing it to adapt to the changing environments and thus to survive.
However it is generally believed that this was not the case once the first life forms appeared on Earth. Starting from this hypothesis, strong efforts have been done by researchers, to unravel the mechanisms at work in the simplest primitive life forms, the protocells. Because data from experiments are very scarce and difficult to obtain, this is a field where computational and analytical models are very useful to explore all the possible behaviors and possibly provide guidelines to new experiments.
In understanding the step from “inanimate” chemical reactions networks to “live ones”, the “compartmentalization”, namely the fact that such pathways could develop on the surface, or in the interior, of vesicles or micelles, has played a major role. A protocell life cycle must exploit a synergetic interaction between these two elements. Models that have this character, chemical reaction networks coupled to containers, we consider to be protocell models.
After several years of researches and efforts, we believe it’s time to propose a workshop to look to the past (recent) history with a glimpse to the future: Protocells: back to the future.
The proposed workshop has multiple goals:

• To draw collectively the “state of the art” of the currently available main families of hypotheses (protocell models), the corresponding set of emergent dynamics (results) and the different analytical tools used.
• To foster the research by exchanging knowledge concerning methods for abstract protocell models, with the goal of understanding emergent phenomena displayed by the models.
• To discuss the main challenges to address by the field in the years to come.
• To consider current protocell experimental results and suggestions they give for modeling efforts.
• To find a common framework to design new experimental techniques that could be used to validate protocell models.

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Abstract

Traditionally Artificial Intelligence (AI) research, broadly conceived as the study of intelligence through the construction of artificial models of natural cognitive systems, has been developed in the context of computer science and robotics. Today the scientific and technical advancements of biological sciences, leading to the emergence of Synthetic Biology (SB) conceived as the chemical synthesis of biological parts/systems/processes, allow the scientific community to extend AI research within the field of experimental biology.
The workshop aims at offering an interdisciplinary forum in which nascent programs involving cooperation between SB and AI in the exploration of biological and cognitive processes can be discussed in their groundings, their procedures, their possibilities and their limits, as well as enriched through scientific exchange of ideas.
The main focus will be on current and possible applications in AI research of the emerging bio-chemical based Information and Communication Technologies (ICT), founded on the convergence of biological, chemical, physical approaches, often in combination with progresses in miniaturization like micro-fluidic devices and Micro Electro-Mechanical Systems (MEMS). But the workshop is interested also in introducing and discussing other actual and possible approaches and research programs which involve SB in AI research.
Most of the participants will have a SB, AI, and/or bio-chem-ICT background, or come from scientific disciplines dealing with theoretical, epistemological and/or experimental issues related to the synthetic study of life and cognition. Our goal is to stimulate the interaction between applied research and theoretical/epistemological reflections, and to promote a front line in SB and AI that focuses on (some of) these questions:

• Can intelligence be studied through the construction and exploration of synthetic biological systems and processes? In which conditions? More specifically: What SB, and in particular its bio-chem-ICT tools and issues, can offer to AI?
• Which are the groundings, procedures, possibilities, limits, expected results, and impacts of current and possible research programs involving SB in AI research? How AI will advance by encompassing SB and bio-chem-ICT approaches?
• Can we nowadays plan concrete collaborations between computer science, robotics and SB in the scientific study natural forms of intelligence? How?
• Are the emerging directions of research in AI (such as embodied AI, enactive AI, soft robotics, …) good candidate to cooperate with SB in the exploration of natural forms of cognition? Can SB contribute to the development of artificial forms of cognition (artificial cognitive systems which do not model natural cognitive systems)?

The workshop intends to bring together researchers interested in investigating one or more of these aspects of the (possible/actual) relationships between SB and AI. The aim is developing an interdisciplinary dialogue able to promote the reflected involvement of SB in AI, and to create a interdisciplinary community concretely developing research programs based on the cooperation of SB and AI.

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