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Preisach Attention: A Hysteretic Model of Sequential Memory Vector Policy Optimization: Training for Diversity Improves Test-Time Search Cross-Species RSA Reveals Conserved Early Visual Alignment but Divergent Higher-Area Rankings Across Human fMRI and Macaque Electrophysiology Temporal Coding as a Substrate for Sensorimotor Object Inference: A Spiking Reinterpretation of Thousand Brains Architecture Engineering Hybrid Physics-Informed Neural Networks for Next-Generation Electricity Systems: A State-of-the-Art Review Dropout Universality: Scaling Laws and Optimal Scheduling at the Edge-of-Chaos Approximation Theory for Neural Networks: Old and New How to Build Marcus's Algebraic Mind: Algebro-Deterministic Substrate over Galois Fields Genetic Programming with Transformer-Based Mutation for Approximate Circuit Design E-ReCON: An Energy- and Resource-Efficient Precision-Configurable Sparse nvCIM Macro for Conventional and Spiking Neural Edge Inference Weight Decay Regimes in Grokking Transformers: Cheap Online Diagnostics What Do Evolutionary Coding Agents Evolve? 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Affinity Is Not Enough: Recovering the Free Energy Principle in Mixture-of-Experts Scalable Learning in Structured Recurrent Spiking Neural Networks without Backpropagation Geometric and dynamical analysis of attractor boundaries and storage limits in kernel Hopfield networks Attractor FCM Physical Foundation Models: Fixed hardware implementations of large-scale neural networks When Does Structure Matter in Continual Learning? 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Self-Organized Construction by Minimal Surprise
Tanja Katharina Kaiser, Heiko Hamann · 2024-05-05 · via cs.NE updates on arXiv.org

For the robots to achieve a desired behavior, we can program them directly, train them, or give them an innate driver that makes the robots themselves desire the targeted behavior. With the minimal surprise approach, we implant in our robots the desire to make their world predictable. Here, we apply minimal surprise to collective construction. Simulated robots push blocks in a 2D torus grid world. In two variants of our experiment we either allow for emergent behaviors or predefine the expected environment of the robots. In either way, we evolve robot behaviors that move blocks to structure their environment and make it more predictable. The resulting controllers can be applied in collective construction by robots.