How did organisms go beyond specific, hardwired responses to more general and flexible forms of adaptive behaviour?
The last decades have witnessed an explosive development in artificial intelligence built upon over a century of scientific advances and millennia of human development. One can only imagine the small brachiopod Lingula experienced similar events some 500 million years ago in the Cambrian explosion. Both explosions involved innovations in learning — sudden transitions and slow antecedents — giving rise to new modes of life and restructuring the world.
The shared connection is a puzzle that Bedau and colleagues (2000) include among their open questions in artificial life: to "determine minimal conditions for evolutionary transitions from specific to generic response systems." This workshop invites experimentalists and theorists to revisit this enduring challenge for learning, specifically.
Our inquiry concerns three questions: How should learning be identified in the context of this transition? What mechanisms — molecular, neural, and otherwise — are necessary and sufficient for its development? And what conditions — ecological, environmental — are necessary and sufficient for its emergence?
We welcome contributions from the full range of modelling traditions in artificial life — whether completed, ongoing, or proposed — as well as more theoretical reflections on prior work and future directions of inquiry.
This workshop intends to coalesce fragmented paradigms, analytic frameworks, and established results across disciplines, backgrounds, and perspectives. The ultimate aim is to familiarise ourselves with the breadth of current knowledge — both empirical and methodological — and work towards a common understanding of the most promising means for addressing this challenge.
Associate Professor of Computer Science and Software Engineering. His research interests span artificial life, bio-inspired AI, developmental neural networks, evolutionary development (Evo-Devo), and evolvable hardware, exploring how biological principles inform intelligence, learning, and system adaptability. He has ongoing industry collaborations in developing an open-source evolvable hardware platform, and a passion for mentoring undergraduate researchers.
PhD candidate working on minimal cognition and the evolution of mind. Driven by the Spinozan conviction that mind, as something that has come into being, must be understood through the processes that produce it — which has led him in two directions: to biology, to trace the mind through its ontogeny and phylogeny; and to artificial life, to explore how these principles might be recapitulated.
Principal Scientist at Autogenetics, pursuing biologically inspired AI. His research includes evolving digital models of cells to study how adaptive behaviour and autonomy emerge from regulatory and sensing networks, and applying these to industrial automation. He is currently collaborating with JPL/NASA on the application of genetic algorithms to generative design of space-based hardware. Prior to his academic work, he founded and led an applied research company in Brazil developing adaptive control systems for underwater robots and vessel dynamic positioning.
Assistant Professor of Computer Science. His research leverages computational modelling to empirically investigate evolutionary theory via experiments that are impossible or intractable in traditional wet-lab systems. His work has focused on evolvability and historical contingency in evolution, while prior work centred on the evolution of learning. He is focused on bridging the divide between ALife and traditional experimental evolution researchers.