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Nathaniel Virgo
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Proceedings Papers
. isal2023, ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference96, (July 24–28, 2023) 10.1162/isal_a_00607
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We informally summarize our recent work on Bayesian reasoners and agents. We also briefly sketch its relation to an existing enactive definition of agents.
Proceedings Papers
. isal2019, ALIFE 2019: The 2019 Conference on Artificial Life255-262, (July 29–August 2, 2019) 10.1162/isal_a_00171
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In the 1950s, the famous cyberneticists Gordon Pask and Stafford Beer conducted a series of remarkable electrochemical deposition experiments. By applying an electric potential across electrodes submerged in an acidic solution of ferrous sulfate, they could bias the growth of electrochemical deposition so as to form functional structures including sensory structures capable of distinguishing between different sounds. Unfortunately, the details of their apparatus and methods are unavailable. As a consequence, their experiment has not been replicated, and the precise mechanisms underlying their results remain unknown. As preliminary steps toward recreating their remarkable results, this paper presents a new computational model that simulates the growth and decay of dendritic structures similar to those investigated by Beer & Pask. We use this model to demonstrate a plausible mechanism through which an electrochemical system of this kind could respond to a reinforcement signal. More specifically, we investigate three strategies for varying the applied electrical current so as to guide the formation of structures into target forms. Each presented strategy succeeds at influencing the growth of the structure, with the most successful strategy involving a ‘constant-current’ feedback mechanism combined with an externally driven oscillation. In the discussion, we compare the adaptation of these structures with various biological adaptive processes, including evolution and metabolism-based adaptive behaviour.
Proceedings Papers
. alife2018, ALIFE 2018: The 2018 Conference on Artificial Life137-144, (July 23–27, 2018) 10.1162/isal_a_00033
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Artificial life has been developing a behavior-based perspective on the origins of life, which emphasizes the adaptive potential of agent-environment interaction even at that initial stage. So far this perspective has been closely aligned to metabolism-first theories, while most researchers who study life’s origins tend to assign an essential role to RNA. An outstanding challenge is to show that a behavior-based perspective can also address open questions related to the genetic system. Accordingly, we have recently applied this perspective to one of science’s most fascinating mysteries: the origins of the standard genetic code. We modeled horizontal transfer of cellular components in a population of protocells using an iterated learning approach and found that it can account for the emergence of several key properties of the standard code. Here we further investigated the diachronic emergence of artificial codes and discovered that the model’s most frequent sequence of amino acid assignments overlaps significantly with the predictions in the literature. Our explorations of the factors that favor early incorporation into an emerging artificial code revealed two aspects: an amino acid’s relative probability of horizontal transfer, and its relative ease of discriminability in chemical space.
Proceedings Papers
. alife2018, ALIFE 2018: The 2018 Conference on Artificial Life1-4, (July 23–27, 2018) 10.1162/isal_e_00002
Proceedings Papers
. alife2018, ALIFE 2018: The 2018 Conference on Artificial Lifeix-xvii, (July 23–27, 2018) 10.1162/isal_e_00001
Proceedings Papers
. alife2018, ALIFE 2018: The 2018 Conference on Artificial Lifei-672, (July 23–27, 2018) 10.1162/isal_a_00122
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The complete Proceedings of the The 2018 Conference on Artificial Life: A Hybrid of the European Conference on Artificial Life (ECAL) and the International Conference on the Synthesis and Simulation of Living Systems (ALIFE)
Proceedings Papers
. alife2018, ALIFE 2018: The 2018 Conference on Artificial Life518-525, (July 23–27, 2018) 10.1162/isal_a_00095
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We investigate the use of attentional neural network layers in order to learn a ‘behavior characterization’ which can be used to drive novelty search and curiosity-based policies. The space is structured towards answering a particular distribution of questions, which are used in a supervised way to train the attentional neural network. We find that in a 2d exploration task, the structure of the space successfully encodes local sensory-motor contingencies such that even a greedy local ‘do the most novel action’ policy with no reinforcement learning or evolution can explore the space quickly. We also apply this to a high/low number guessing game task, and find that guessing according to the learned attention profile performs active inference and can discover the correct number more quickly than an exact but passive approach.
Proceedings Papers
. ecal2017, ECAL 2017, the Fourteenth European Conference on Artificial Life420-427, (September 4–8, 2017) 10.1162/isal_a_070
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The biological machinery of evolution can itself be subject to natural selection. Several mechanisms have been proposed through which this can happen. Here we argue that one of these—lineage selection—becomes a strong selective force when the time scale of fixation in the population is comparable to the time scale of adaptation. This implies that lineage selection will be enhanced by anything that slows down fixation; in particular, we expect its effects to be strong when populations are very large and when spatial diffusion is limited. To demonstrate this we construct a simple model of a spatially structured population evolving on a fixed, but infinite, fitness landscape. This landscape consists of a smooth, evolvable path surrounded by rugged local peaks. Our model exhibits an extremely strong dependence on population size: as its size is increased the population evolves along the smooth path, avoiding local peaks, for exponentially longer times. These results suggest that selection for evolvability might become an increasingly important force as we consider larger spatiotemporal scales, and in particular that it might help to explain the evolution of the modern cellular architecture from some previous, less evolvable state.
Proceedings Papers
. alif2016, ALIFE 2016, the Fifteenth International Conference on the Synthesis and Simulation of Living Systems598-599, (July 4–6, 2016) 10.1162/978-0-262-33936-0-ch095
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Many of the chemistries studied by chemists as being relevant to the origins of life produce a combinatorial explosion of products. Researchers in the origins of life have traditionally regarded such combinatorial explosions as a problem that needs to be overcome, but due to recent technological advances there is an increasing interest in understanding these combinatorially complex chemistries as complex dynamical systems in their own right. An important question is whether a sufficiently large and complex reaction network can, in some quantitative sense, behave fundamentally differently from what is possible in the clean chemistries more usually studied by chemists. Here I argue for an affirmative answer to this question, with the aid of a simple toy example. The example shows the existence of a phase transition, leading to a threshold effect of a kind that cannot occur in a small reaction network. This threshold is closely re- lated to Eigens error threshold. However, while the error threshold occurs in a very structured chemistry (template replication), this transition occurs in a very unstructured chemistry more closely resembling Kauffmans autocatalytic set model. The significance of this result lies not in the scenario presented, but in showing that such threshold phenomena can exist in messy chemistries at all.
Proceedings Papers
. ecal2015, ECAL 2015: the 13th European Conference on Artificial Life175-182, (July 20–24, 2015) 10.1162/978-0-262-33027-5-ch037
Proceedings Papers
. ecal2015, ECAL 2015: the 13th European Conference on Artificial Life325-332, (July 20–24, 2015) 10.1162/978-0-262-33027-5-ch061
Proceedings Papers
. alife2014, ALIFE 14: The Fourteenth International Conference on the Synthesis and Simulation of Living Systems530-531, (July 30–August 2, 2014) 10.1162/978-0-262-32621-6-ch084
Proceedings Papers
. alife2014, ALIFE 14: The Fourteenth International Conference on the Synthesis and Simulation of Living Systems498-505, (July 30–August 2, 2014) 10.1162/978-0-262-32621-6-ch080
Proceedings Papers
. ecal2013, ECAL 2013: The Twelfth European Conference on Artificial Life240-247, (September 2–6, 2013) 10.1162/978-0-262-31709-2-ch036
Proceedings Papers
. alife2012, ALIFE 2012: The Thirteenth International Conference on the Synthesis and Simulation of Living Systems457-464, (July 19–22, 2012) 10.1162/978-0-262-31050-5-ch060
Proceedings Papers
. ecal2011, ECAL 2011: The 11th European Conference on Artificial Life121, (August 8–12, 2011) 10.7551/978-0-262-29714-1-ch121
Proceedings Papers
. ecal2011, ECAL 2011: The 11th European Conference on Artificial Life124, (August 8–12, 2011) 10.7551/978-0-262-29714-1-ch124
Proceedings Papers
. ecal2011, ECAL 2011: The 11th European Conference on Artificial Life40, (August 8–12, 2011) 10.7551/978-0-262-29714-1-ch040