- author = "Sjors {van Berkel} and Daniel Turi and Andrei Pruteanu and Stefan Dulman",
- title = "Automatic discovery of algorithms for multi-agent systems",
- booktitle = "GECCO 2012 Evolutionary computation and multi-agent systems and simulation (ECoMASS)",
- year = "2012",
- editor = "Forrest Stonedahl and Rick Riolo",
- isbn13 = "978-1-4503-1178-6",
- keywords = "genetic algorithms, genetic programming",
- pages = "337--344",
- month = "7-11 " # jul,
- organisation = "SIGEVO",
- address = "Philadelphia, Pennsylvania, USA",
-
DOI = "
doi:10.1145/2330784.2330833",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- abstract = "Automatic algorithm generation for large-scale distributed systems is one of the holy grails of artificial intelligence and agent-based modeling. It has direct applicability in future engineered (embedded) systems, such as mesh networks of sensors and actuators where there is a high need to harness their capabilities via algorithms that have good scalability characteristics. NetLogo has been extensively used as a teaching and research tool by computer scientists, for example for exploring distributed algorithms. Inventing such an algorithm usually involves a tedious reasoning process for each individual idea. In this paper, we report preliminary results in our effort to push the boundary of the discovery process even further, by replacing the classical approach with a guided search strategy that makes use of genetic programming targeting the NetLogo simulator. The effort moves from a manual model implementation to an automated discovery process. The only activity that is required is the implementation of primitives and the configuration of the tool-chain. In this paper, we explore the capabilities of our framework by re-inventing five well-known distributed algorithms.",
-
notes = "Also known as \cite{2330833} Distributed at
GECCO-2012.
ACM Order Number 910122.",
