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Error Correlation and Error Reduction in Ensemble Classifiers
This paper focuses on data selection and classifier training methods, in order to 'prepare' classifiers for combining, and discusses several methods that make the classifiers in an ensemble more complementary.
An Introduction to Collective Intelligence
This paper surveys the emerging science of how to design a “COllective INtelligence” (COIN). A COIN is a large multi-agent system where: i) There is little to no centralized communication or control.
Evolution-Guided Policy Gradient in Reinforcement Learning
Evolutionary Reinforcement Learning (ERL), a hybrid algorithm that leverages the population of an EA to provide diversified data to train an RL agent, and reinserts the RL agent into theEA population periodically to inject gradient information into the EA.
Optimal Payoff Functions for Members of Collectives
It is demonstrated experimentally that using these new utility functions can result in significantly improved performance over that of previously investigated COIN payoff utilities, over and above those previous utilities' superiority to the conventional team game utility.
Distributed agent-based air traffic flow management
FACET based results show that agents receiving personalized rewards reduce congestion by up to 45% over agents receiving a global reward and byup to 67% over a current industry approach (Monte Carlo estimation).
Linear and Order Statistics Combiners for Pattern Classification
This chapter provides an analytical framework to quantify the improvements in classification results due to combining and derives expressions that indicate how much the median, the maximum and in general the i-th order statistic can improve classifier performance.
Collective Intelligence for Control of Distributed Dynamical Systems
A mathematical theory for such configuration applicable when (as in the bar problem) the global goal can be expressed as minimizing a global energy function and the nodes can be express as minimizers of local free energy functions is summarized.
Analyzing and visualizing multiagent rewards in dynamic and stochastic domains
This paper presents a new reward evaluation method that provides a visualization of the tradeoff between the level of coordination among the agents and the difficulty of the learning problem each agent faces, and shows that in the more difficult dynamic domain, the reward efficiency visualization method provides a two order of magnitude speedup in selecting good rewards.