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Synergistic integration between machine learning and agent-based modeling: A multidisciplinary review
Agent-based modeling (ABM) involves develo** models in which agents make adaptive
decisions in a changing environment. Machine-learning (ML) based inference models can …
decisions in a changing environment. Machine-learning (ML) based inference models can …
A survey of inverse reinforcement learning
Learning from demonstration, or imitation learning, is the process of learning to act in an
environment from examples provided by a teacher. Inverse reinforcement learning (IRL) is a …
environment from examples provided by a teacher. Inverse reinforcement learning (IRL) is a …
[HTML][HTML] Agent decision-making: The Elephant in the Room-Enabling the justification of decision model fit in social-ecological models
Agent-based models are particularly suitable to reflect the dynamics of humans, nature, and
their interactions, making them a crucial approach for understanding social-ecological …
their interactions, making them a crucial approach for understanding social-ecological …
A framework proposal for machine learning-driven agent-based models through a case study analysis
Agent-based modeling (ABM) has been widely employed by researchers in various
domains. Develo** valid and useful agent-based models (ABMs) imposes challenges on …
domains. Develo** valid and useful agent-based models (ABMs) imposes challenges on …
[HTML][HTML] Generating synthetic bitcoin transactions and predicting market price movement via inverse reinforcement learning and agent-based modeling
In this paper, we present a novel method to predict Bitcoin price movement utilizing inverse
reinforcement learning (IRL) and agent-based modeling (ABM). Our approach consists of …
reinforcement learning (IRL) and agent-based modeling (ABM). Our approach consists of …
[PDF][PDF] Live simulations
The next exciting step for large-scaled, data-driven, agent-based simulations is to make
them live. In this article we describe what is meant by a live simulation, how this concept …
them live. In this article we describe what is meant by a live simulation, how this concept …
[HTML][HTML] Multi-agent learning of asset maintenance plans through localised subnetworks
Maintenance planning of networked multi-asset systems is a complex problem due to the
inherent individual and collective asset constraints and dynamics as well as the size of the …
inherent individual and collective asset constraints and dynamics as well as the size of the …
[PDF][PDF] Agent-based models using artificial intelligence: A literature review
M Hauff, A Lurz - 2022 - sce.carleton.ca
Simulations of behavior, in particular agent-based models (ABM), enhance informed
decision-making. At present, Covid-19's autonomous dispersion is a notable use case, but …
decision-making. At present, Covid-19's autonomous dispersion is a notable use case, but …
Big Data (R) evolution in Geography: Complexity Modelling in the Last Two Decades
The use of data and statistics along with computational systems heralded the beginning of a
quantitative revolution in Geography. Use of simulation models (Cellular Automata and …
quantitative revolution in Geography. Use of simulation models (Cellular Automata and …
Bayesian inverse reinforcement learning for collective animal movement
Bayesian inverse reinforcement learning for collective animal movement Page 1 The Annals
of Applied Statistics 2022, Vol. 16, No. 2, 999–1013 https://doi.org/10.1214/21-AOAS1529 © …
of Applied Statistics 2022, Vol. 16, No. 2, 999–1013 https://doi.org/10.1214/21-AOAS1529 © …