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Norihiro Maruyama
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Proceedings Papers
. isal2024, ALIFE 2024: Proceedings of the 2024 Artificial Life Conference92, (July 22–26, 2024) 10.1162/isal_a_00713
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We introduce a simulation environment to facilitate research into emergent collective behaviour, with a focus on replicating the dynamics of ant colonies. By leveraging real-world data, the environment simulates a target ant trail that a controllable agent must learn to replicate, using sensory data observed by the target ant. This work aims to contribute to the neuroevolution of models for collective behaviour, focusing on evolving neural architectures that encode domain-specific behaviours in the network topology. By evolving models that can be modified and studied in a controlled environment, we can uncover the necessary conditions required for collective behaviours to emerge. We hope this environment will be useful to those studying the role of interactions in emergent behaviour within collective systems.
Proceedings Papers
. isal2024, ALIFE 2024: Proceedings of the 2024 Artificial Life Conference106, (July 22–26, 2024) 10.1162/isal_a_00804
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This study applies the information-theoretic measure of Non- Trivial Information Closure (NTIC) to quantify the autonomy of individual ants within a colony. We calculate the degree to which an ant’s future behavior is determined by its own past states versus being influenced by its local environment. Results show that individual ants exhibit consistent levels of autonomy across different timescales. This suggests that ant behavior reflects a non-trivial processing of both internal and external information, rather than being a simple reflexive response to stimuli. The approach demonstrates the utility of NTIC as a metric for assessing autonomy in complex biological systems. These findings lay the groundwork for future studies of autonomy and information processing in swarms.
Proceedings Papers
. isal2023, ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference112, (July 24–28, 2023) 10.1162/isal_a_00676
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Artificial intelligence (AI) has seen drastic advances given recently powerful models with astonishing individual capacities, while the biological evolutionary strategy focuses more on collective intelligence. We seek to bridge biological collective intelligence with artificial intelligence, by studying collective motion of agents, inspired by the biological ants collectively solving tasks while using chemical pheromone for communication. We train agent in a single setting to acquire chemotaxis and duplicate the trained agents to form a population. We observe several interesting dynamics where collective intelligence is realized in our AI models, and expect to further analyze the impact of communication on collective dynamics.
Proceedings Papers
. isal2023, ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference116, (July 24–28, 2023) 10.1162/isal_a_00698
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We conducted a comprehensive tracking study of 64 unmarked ants within the same arena to examine the dynamics of individual behaviors within a collective, aiming to understand the underlying mechanisms that drive the colony's collective behaviors. Specifically, we analyzed the movement patterns of the ants to identify the “algorithm” governing their actions. One such approach we employed is the ϵ -machine method, pioneered by Crutchfield and colleagues, which predicts motion using a stochastic finite state machine. The results of our study revealed that individual ants exhibited either deterministic or stochastic behaviors, contingent upon their roles within the colony. Ants contributing to cluster formation displayed deterministic behaviors, whereas those exploring outside of the cluster were more likely to demonstrate stochastic behaviors.
Proceedings Papers
. isal2023, ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference42, (July 24–28, 2023) 10.1162/isal_a_00635
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In this study, we introduce a novel system whereby a humanoid robot, named Alter3, employs a selective combination of three strategies - Mimicking, Imitation, and Dream - to replicate human behavior observed through its camera-based eyes. This work builds upon previous research [Masumori et al. (2021); Ikegami et al. (2021)]. In Mimicking mode, Alter3 recreates “how” a human moves by calculating joint angles. In Imitation mode, it identifies and reproduces symbolic poses through a pre-trained Variational AutoEncoder (VAE), essentially replicating “what” the human did. When imitation proves unsuccessful, Alter3 engages its Dream mode, where it recalls altered memories through selection and mutation processes, allowing it to generate movements based on experience. Moreover, in the absence of a human subject, Alter3, with its eyes closed, retrieves and performs movements from memory. Our findings reveal that the concurrent use of the three strategies (Mimicking, Imitation, Dreaming) stabilizes the latent space state and optimizes the range of identifiable poses. Furthermore, the behavior that Alter3 generates through Dream mode evolves from symbolic movements via the Imitation pathway. These findings suggest that new movements can be created from concept-based motions by selectively employing both methodical (Mimicking) and symbolic (Imitation) motions.
Proceedings Papers
. isal2023, ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference101, (July 24–28, 2023) 10.1162/isal_a_00634
Proceedings Papers
. isal2021, ALIFE 2021: The 2021 Conference on Artificial Life53, (July 18–22, 2021) 10.1162/isal_a_00463
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In this study, we report the investigations conducted on the mimetic behavior of a new humanoid robot called Alter3. Alter3 autonomously imitates the motions of a human in front of him and stores the motion sequences in its memory. Alter3 also contains a self-simulator that simulates its own motions before executing them and generates a self-image. We investigate how this mimetic behavior evolves with human interaction, by analyzing memory dynamics and information flow between Alter3 and humans. One important observation from this study is that when Alter3 fails to imitate human motion, humans tend to imitate Alter3 instead. This tendency is quantified by the alternation of the direction of information flow. At the conference we will also report on the experiments we carried out recently, in which two Alters imitated each other, and in which we let people possess and imitate Alter.
Proceedings Papers
. alife2018, ALIFE 2018: The 2018 Conference on Artificial Life163-170, (July 23–27, 2018) 10.1162/isal_a_00037
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Our previous study showed that embodied cultured neural networks and spiking neural networks with spike-timing dependent plasticity (STDP) can learn a behavior as they avoid stimulation from outside. In a sense, the embodied neural network can autonomously change their activity to avoid external stimuli. In this paper, as a result of our experiments using cultured neurons, we find that there is also a second property allowing the network to avoid stimulation: if the network cannot learn to avoid the external stimuli, it tends to decrease the stimulus-evoked spikes as if to ignore the input neurons. We also show such a behavior is reproduced by spiking neural networks with asymmetric-STDP. We consider that these properties can be regarded as autonomous regulation of self and non-self for the network.
Proceedings Papers
. ecal2015, ECAL 2015: the 13th European Conference on Artificial Life373-380, (July 20–24, 2015) 10.1162/978-0-262-33027-5-ch067
Proceedings Papers
. alife2014, ALIFE 14: The Fourteenth International Conference on the Synthesis and Simulation of Living Systems769-770, (July 30–August 2, 2014) 10.1162/978-0-262-32621-6-ch124