Machine Learning (ML), specifically deep reinforcement learning has the potential to generate new ways to explore complex models, which can enhance traditional computational paradigms such as agent-based modeling. Recently, ML algorithms have proved an important contribution to the determination of semi-...
因而是一种adaptation，但通常不被称为狭义的learning，因为，这种算法是否是对agent而言最优的，没有保障...
毕竟Agent-based modeling的支持者批评的是主流理论里agent做Bayesian updating的惯例。所以取另一个极端—...
cognitive-behavioral independent variables.Space Syntax-based stepwise regressions suffer from heavy multicollinearity.Principal component analysis alleviated pedestrian volume models' multicollinearity.关键词: Pedestrian volume modelling Agent-based model Machine learning Principal component analysis Multiple regression...
Large language model (LLM) agent-based modeling and simulation A summary of works on LLM empowered agent-based modeling and simulation. Our survey Large language models empowered agent-based modeling and simulation: a survey and perspectives is published in Humanities and Social Sciences Communications...
Agent based modelingAgent based simulationComplex SystemsSimulation of socio-economic systemsDecision making support tolosmachine learningstatistical learningIn this paper, we give a succinct introduction to some basic concepts imported from the fields of Machine and Statistical Learning that can be useful ...
Agent-Based Models (ABMs) are used in several fields to study the evolution of complex systems from micro-level assumptions. However, a significant drawback of ABMs is their inability to estimate agent-specific (or “micro”) variables, which hinders the
Learning From Humans: Agent Modeling With Individual Human Behaviors Multiagent-based simulation (MABS) is a very active interdisciplinary area bridging multiagent research and social science. The key technology to conduct t... H Hattori,Y Nakajima,T Ishida - 《IEEE Transactions on Systems Man & ...
This promising research area has the potential to enhance the accuracy and realism of agent-based modeling and simulation, which has been limited by traditional rule-based or machine-learning methods. In this post, we will explore the possibilities of LLMs in agent-based simulation, as...
machine learning algorithms, which could include deep learning models like neural networks. These training models enable the AI to recognize patterns, make predictions, and generate responses based on the data on which it was trained. The AI agent can also learn from real-time interactions with ...