Biological and artificial cognition: what can we learn about mechanisms by modelling physical cognition problems using artificial intelligence planning techniques?

Jackie Chappell, Nick Hawes
Author Information
  1. Jackie Chappell: School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK. j.m.chappell@bham.ac.uk

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.

References

  1. Curr Biol. 2011 Feb 8;21(3):R116-9 [PMID: 21300275]
  2. Biol Lett. 2011 Aug 23;7(4):619-22 [PMID: 21508016]
  3. Behav Brain Sci. 2001 Dec;24(6):1033-50; discussion 1050-94 [PMID: 12412325]
  4. Curr Biol. 2006 Apr 4;16(7):697-701 [PMID: 16581516]
  5. Trends Cogn Sci. 2006 Jul;10(7):287-91 [PMID: 16807064]
  6. Philos Trans R Soc Lond B Biol Sci. 2012 Oct 5;367(1603):2743-52 [PMID: 22927573]
  7. Curr Opin Neurobiol. 2006 Apr;16(2):199-204 [PMID: 16563737]
  8. Trends Cogn Sci. 2006 Jul;10(7):294-300 [PMID: 16793323]
  9. Anim Cogn. 2012 Jan;15(1):121-33 [PMID: 21761145]
  10. Biol Cybern. 2000 Mar;82(3):247-69 [PMID: 10664111]
  11. Cognition. 2011 Sep;120(3):302-21 [PMID: 21269608]
  12. Philos Trans R Soc Lond B Biol Sci. 2012 Oct 5;367(1603):2773-83 [PMID: 22927576]
  13. Philos Trans R Soc Lond B Biol Sci. 2012 Oct 5;367(1603):2704-14 [PMID: 22927569]
  14. Curr Biol. 2006 Apr 4;16(7):R244-5 [PMID: 16581498]
  15. Philos Trans R Soc Lond B Biol Sci. 2012 Oct 5;367(1603):2677-85 [PMID: 22927566]

MeSH Term

Animals
Artificial Intelligence
Association Learning
Behavior, Animal
Cognition
Computational Biology
Computer Simulation
Models, Neurological
Pongo pygmaeus
Problem Solving

Word Cloud

Created with Highcharts 10.0.0cognitionexperimentsmechanismsmodelsunderstandproblemspresentanimalinformationunderlyingphysicaltaskdomaincanusingplanningorangutansresultsartificialfullystructuresubjectsrequiredsolvethem?currentlygoodunderstandingbehaviouralneurobiologicalassociativelearningprocessesmuchlesscomplexformsanimalsstudyproposalnewwaythinkingdescribeprocessdecomposedcomponentpartsconstructedrepresentcausaleventsavailableagentimplementsimplesetlanguageMAPLwithinMAPSIMsimulationenvironmentapplyingpuzzletubepreviouslypresenteddiscusscomparedescribingadvantagesapproachwaysextendedBiologicalcognition:learnmodellingintelligencetechniques?

Similar Articles

Cited By