Heuristic search of heuristics 
Created by W.Langdon from
gp-bibliography.bib Revision:1.8612
- @InProceedings{pirrone:2023:SGAI,
- 
  author =       "Angelo Pirrone and Peter C. R. Lane and 
Laura Bartlett and Noman Javed and Fernand Gobet",
- 
  title =        "Heuristic search of heuristics",
- 
  booktitle =    "Artificial Intelligence XL",
- 
  year =         "2023",
- 
  editor =       "Max Bramer and Frederic Stahl",
- 
  volume =       "14381",
- 
  series =       "Lecture Notes in Computer Science",
- 
  pages =        "407--420",
- 
  address =      "Cambridge, UK",
- 
  month =        dec # " 12-14",
- 
  publisher =    "Springer Nature",
- 
  keywords =     "genetic algorithms, genetic programming, Decision
Making, Heuristic Search, Program Synthesis",
- 
  isbn13 =       "978-3-031-47994-6",
- 
  URL =          " https://researchprofiles.herts.ac.uk/files/49542282/Heuristic_search_of_heuristics.pdf", https://researchprofiles.herts.ac.uk/files/49542282/Heuristic_search_of_heuristics.pdf",
- 
  DOI =          " 10.1007/978-3-031-47994-6_36", 10.1007/978-3-031-47994-6_36",
- 
  size =         "14 pages",
- 
  abstract =     "How can we infer the strategies that human
participants adopt to carry out a task? One
possibility, which we present and discuss here, is to
develop a large number of strategies that participants
could have adopted, given a cognitive architecture and
a set of possible operations. Subsequently, the (often
many) strategies that best explain a dataset of
interest are highlighted. To generate and select
candidate strategies, we use genetic programming, a
heuristic search method inspired by evolutionary
principles. Specifically, combinations of cognitive
operators are evolved and their performance compared
against human participants performance on a specific
task. We apply this methodology to a typical
decision-making task, in which human participants were
asked to select the brighter of two stimuli. We
discover several understandable,
psychologically-plausible strategies that offer
explanations of participants performance. The
strengths, applications and challenges of this
methodology are discussed.",
- 
  notes =        "500 generations",
- }
Genetic Programming entries for 
Angelo Pirrone
Peter C R Lane
Laura Bartlett
Noman Javed
Fernand Gobet
Citations
