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Martin Biehl
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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
. 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 Life68-75, (September 4–8, 2017) 10.1162/isal_a_015
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This is a contribution to the formalization of the concept of agents in multivariate Markov chains. Agents are commonly defined as entities that act, perceive, and are goal-directed. In a multivariate Markov chain (e.g. a cellular automaton) the transition matrix completely determines the dynamics. This seems to contradict the possibility of acting entities within such a system. Here we present definitions of actions and perceptions within multivariate Markov chains based on entitysets. Entity-sets represent a largely independent choice of a set of spatiotemporal patterns that are considered as all the entities within the Markov chain. For example, the entityset can be chosen according to operational closure conditions or complete specific integration. Importantly, the perceptionaction loop also induces an entity-set and is a multivariate Markov chain. We then show that our definition of actions leads to non-heteronomy and that of perceptions specialize to the usual concept of perception in the perception-action loop.
Proceedings Papers
. alif2016, ALIFE 2016, the Fifteenth International Conference on the Synthesis and Simulation of Living Systems722-729, (July 4–6, 2016) 10.1162/978-0-262-33936-0-ch115
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We present some arguments why existing methods for representing agents fall short in applications crucial to artificial life. Using a thought experiment involving a fictitious dynamical systems model of the biosphere we argue that the metabolism, motility, and the concept of counterfactual variation should be compatible with any agent representation in dynamical systems. We then propose an information-theoretic notion of integrated spatiotemporal patterns which we believe can serve as the basic building block of an agent definition. We argue that these patterns are capable of solving the problems mentioned before. We also test this in some preliminary experiments.
Proceedings Papers
. ecal2015, ECAL 2015: the 13th European Conference on Artificial Life511, (July 20–24, 2015) 10.1162/978-0-262-33027-5-ch089
Proceedings Papers
. alife2014, ALIFE 14: The Fourteenth International Conference on the Synthesis and Simulation of Living Systems949-956, (July 30–August 2, 2014) 10.1162/978-0-262-32621-6-ch154
Proceedings Papers
. ecal2013, ECAL 2013: The Twelfth European Conference on Artificial Life1099-1106, (September 2–6, 2013) 10.1162/978-0-262-31709-2-ch165