- author = "Peter Lane and Noman Javed and Angelo Pirrone and Laura Bartlett and Fernand Gobet",
- title = "Evolving Time-Dependent Cognitive Models",
- booktitle = "AISB 2023 convention proceedings. The Society for the Study of Artificial Intelligence and Simulation Behaviour",
- year = "2023",
- editor = "Berndt Mueller",
- pages = "64--66",
- address = "Swansea, UK",
- month = "13-14 " # apr,
- keywords = "genetic algorithms, genetic programming, Delayed Match To Sample task (DMTS",
- isbn13 = "978-1-908187-85-7",
-
URL = "
http://eprints.lse.ac.uk/120847/",
-
URL = "
https://aisb.org.uk/wp-content/uploads/2023/05/aisb2023.pdf",
- size = "3 pages",
- abstract = "Developing, understanding and verifying the behaviour of cognitive models is a non-trivial task. A good cognitive model will provide some explanation of how a human performs in a particular experimental setting, and even provide predictions for new settings. In many cases, cognitive models are based around computer programs which need to be designed and written for the given experiment and behaviour: ideally, models will be developed to cover a wide range of possible explanations, but often time constraints or natural bias(oversights) lead to models written by human programmers being constrained to particular groups of solutions. This paper will describe, discuss and illustrate a proposed system for helping to automate the construction of programs to form high quality cognitive models.",
-
notes = "p65 'Application of a clustering algorithm divides
this space of (GP) models into two basic groups,
studying individual members of those groups enables a
scientist to develop an explanation of behaviour in
this
domain.'
https://aisb.org.uk/aisb-convention-2023-non-members/",
