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AppliedMath, Volume 4, Issue 3

September 2024 - 21 articles

Cover Story: Most optimisation research focuses on simple cases: one decision-maker, one objective and a set of constraints. However, real-world optimisation problems might be multi-objective, multi-agent, multi-stage or multi-level, involving partial knowledge, uncertainty and decision-dependent distributions. We define a broad class of discrete optimisation problems called an Influence Program, and a solver based on multi-agent multi-objective reinforcement learning with sampling. We model and solve a range of problems spanning stochastic programming, game theory, influence diagrams, Bayesian networks and constraint programming. View this paper

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1 Citations

1,903 Views

22 Pages

The Mechanics Underpinning Non-Deterministic Computation in Cortical Neural Networks

Elizabeth A. Stoll

AppliedMath2024, 4(3), 806-827;https://doi.org/10.3390/appliedmath4030043

26 June 2024

Cortical neurons integrate upstream signals and random electrical noise to gate signaling outcomes, leading to statistically random patterns of activity. Yet classically, the neuron is modeled as a binary computational unit, encoding Shannon entropy....

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AppliedMath - ISSN 2673-9909

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