Synergistic integration between machine learning and agent-based modeling: A multidisciplinary review
Agent-based modeling (ABM) involves developing 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 …
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. Developing valid and useful agent-based models (ABMs) imposes challenges on …
domains. Developing valid and useful agent-based models (ABMs) imposes challenges on …
[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 …
[HTML][HTML] Multi-agent learning of asset maintenance plans through localised subnetworks
MP Hernández, A Puchkova, AK Parlikad - Engineering Applications of …, 2024 - Elsevier
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 …
[PDF][PDF] Live simulations
S Swarup, HS Mortveit - … of the 19th International Conference on …, 2020 - aamas.csc.liv.ac.uk
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] 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 …
Modelling agent decision making in agent-based simulation-analysis using an economic technology uptake model
F Klügl, H Kyvik Nordås - … 2023), London, United Kingdom, May 29 …, 2023 - diva-portal.org
ABSTRACT Agent-based Simulation Modelling focuses on the agents' decision making in
their individual context. The decision making details may substantially affect the simulation …
their individual context. The decision making details may substantially affect the simulation …
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 © …