- author = "Rory Greig and Jordi Arranz",
- title = "Generating Agent Based Models From Scratch With Genetic Programming",
- booktitle = "2021 Conference on Artificial Life",
- year = "2021",
- address = "online",
- month = "19-23 " # jul,
- organisation = "ISAL",
- publisher = "Massachusetts Institute of Technology",
- note = "Best Paper Award",
- keywords = "genetic algorithms, genetic programming, program synthesis, genetic programming, agent-based modeling, opinion dynamics, Flocking, evolutionary computing, machine learning, artificial intelligence, model induction, program induction, structural calibration, micro-simulation, Julia",
-
URL = "
https://direct.mit.edu/isal/proceedings/isal2020/32/1/98387",
-
URL = "
https://direct.mit.edu/isal/proceedings-pdf/isal/33/64/1956387/isal_a_00383.pdf",
-
DOI = "
doi:10.1162/isal_a_00383",
- size = "10 page",
- abstract = "Program synthesis (PS) and genetic programming (GP) allow non-trivial programs to be generated from example data. Agent-based models (ABMs) are a promising field of application as their complexity at a macro level arises from simple agent-level rules. Previous attempts at using evolutionary algorithms to learn the structure of ABMs have focused on modifying and recombining existing models targeted to the domain in question, which requires prior domain knowledge. We demonstrate a new domain-independent approach which is able to evolve interpretable agent logic of an ABM from scratch. We employ a flexible domain specific language (DSL) which consists of basic mathematical building blocks. The flexibility of our method is demonstrated by learning symbolic models in two different domains: flocking and opinion dynamics, targeting data produced from reference models. We show that the evolved solutions are behaviourally identical to the reference models and generalise extremely well.",
-
notes = "See also \cite{Greig:2021:IGSS}
Prague, Czech Republic.
Broken Jan 2024 isal_a_00383.pdf https://direct.mit.edu/isal/isal/volume/33",
