Biological and artificial cognition: what can we learn about mechanisms by modelling physical cognition problems using artificial intelligence planning techniques?
Philos Trans R Soc Lond B Biol Sci
; 367(1603): 2723-32, 2012 Oct 05.
Article
en En
| MEDLINE
| ID: mdl-22927571
ABSTRACT
Do we fully understand the structure of the problems we present to our subjects in experiments on animal cognition, and the information required to solve them? While we currently have a good understanding of the behavioural and neurobiological mechanisms underlying associative learning processes, we understand much less about the mechanisms underlying more complex forms of cognition in animals. In this study, we present a proposal for a new way of thinking about animal cognition experiments. We describe a process in which a physical cognition task domain can be decomposed into its component parts, and models constructed to represent both the causal events of the domain and the information available to the agent. We then implement a simple set of models, using the planning language MAPL within the MAPSIM simulation environment, and applying it to a puzzle tube task previously presented to orangutans. We discuss the results of the models and compare them with the results from the experiments with orangutans, describing the advantages of this approach, and the ways in which it could be extended.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Solución de Problemas
/
Inteligencia Artificial
/
Cognición
/
Biología Computacional
Límite:
Animals
Idioma:
En
Revista:
Philos Trans R Soc Lond B Biol Sci
Año:
2012
Tipo del documento:
Article
País de afiliación:
Reino Unido