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Found 9 papers, 4 papers with code
Many-Objective Evolutionary Influence Maximization: Balancing Spread, Budget, Fairness, and Time
The Influence Maximization (IM) problem seeks to discover the set of nodes in a graph that can spread the information propagation at most.
Social Interpretable Reinforcement Learning
Our method mimics a social learning process, where each agent in a group learns to solve a given task based both on its own individual experience as well as the experience acquired together with its peers.
Quality Diversity Evolutionary Learning of Decision Trees
Addressing the need for explainable Machine Learning has emerged as one of the most important research directions in modern Artificial Intelligence (AI).
Interpretable AI for policy-making in pandemics
For this reason, several works have applied machine learning techniques, often with the help of special-purpose simulators, to generate policies that were more effective than the ones obtained by governments.
Interpretable pipelines with evolutionarily optimized modules for RL tasks with visual inputs
However, they struggle when working with raw data, especially when the input dimensionality increases and the raw inputs alone do not give valuable insights on the decision-making process.
Black-box adversarial attacks using Evolution Strategies
Several methods able to generate adversarial samples make use of gradients, which usually are not available to an attacker in real-world scenarios.
A Signal-Centric Perspective on the Evolution of Symbolic Communication
The evolution of symbolic communication is a longstanding open research question in biology.
Genetic Improvement of Routing Protocols for Delay Tolerant Networks
We apply this methodology, in silico, to six test cases of urban networks made of hundreds of nodes, and find that GI produces consistent gains in delivery probability in four cases.
Evolutionary learning of interpretable decision trees
We present a two-level optimization scheme that combines the advantages of evolutionary algorithms with the advantages of Q-learning.
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