It is widely accepted that the difficulty and expense
involved in acquiring the knowledge behind tactical
behaviours has been one limiting factor in the
development of simulated agents representing
adversaries and teammates in military and game
simulations. Several researchers have addressed this
problem with varying degrees of success. The problem
mostly lies in the fact that tactical knowledge is
difficult to elicit and represent through interactive
sessions between the model developer and the subject
matter expert. This paper describes a novel approach
that employs genetic programming in conjunction with
context-based reasoning to evolve tactical agents based
upon automatic observation of a human performing a
mission on a simulator. we describe the process used to
carry out the learning. A prototype was built to
demonstrate feasibility and it is described herein. The
prototype was rigorously and extensively tested. The
evolved agents exhibited good fidelity to the observed
human performance, as well as the capacity to
generalise from it.
%0 Journal Article
%1 FGGD06
%A Fernlund, Hans K. G.
%A Gonzalez, Avelino J.
%A Georgiopoulos, Michael
%A DeMara, Ronald F.
%D 2006
%J IEEE Transactions on Systems, Man and Cybernetics,
Part B
%K (artificial Context-based acquisition, agent agents, algorithms, behavioral behavioural context-based development, elicitation, genetic human inference intelligence), knowledge learning learning, mechanisms, modeling, observation, performance programming, reasoning, representation, simulation software tactical
%N 1
%P 128--140
%R doi:10.1109/TSMCB.2005.855568
%T Learning tactical human behavior through observation
of human performance
%U http://www.cal.ucf.edu/journal/j_fernlund_gonzalez_itsmc_04.pdf
%V 36
%X It is widely accepted that the difficulty and expense
involved in acquiring the knowledge behind tactical
behaviours has been one limiting factor in the
development of simulated agents representing
adversaries and teammates in military and game
simulations. Several researchers have addressed this
problem with varying degrees of success. The problem
mostly lies in the fact that tactical knowledge is
difficult to elicit and represent through interactive
sessions between the model developer and the subject
matter expert. This paper describes a novel approach
that employs genetic programming in conjunction with
context-based reasoning to evolve tactical agents based
upon automatic observation of a human performing a
mission on a simulator. we describe the process used to
carry out the learning. A prototype was built to
demonstrate feasibility and it is described herein. The
prototype was rigorously and extensively tested. The
evolved agents exhibited good fidelity to the observed
human performance, as well as the capacity to
generalise from it.
@article{FGGD06,
abstract = {It is widely accepted that the difficulty and expense
involved in acquiring the knowledge behind tactical
behaviours has been one limiting factor in the
development of simulated agents representing
adversaries and teammates in military and game
simulations. Several researchers have addressed this
problem with varying degrees of success. The problem
mostly lies in the fact that tactical knowledge is
difficult to elicit and represent through interactive
sessions between the model developer and the subject
matter expert. This paper describes a novel approach
that employs genetic programming in conjunction with
context-based reasoning to evolve tactical agents based
upon automatic observation of a human performing a
mission on a simulator. we describe the process used to
carry out the learning. A prototype was built to
demonstrate feasibility and it is described herein. The
prototype was rigorously and extensively tested. The
evolved agents exhibited good fidelity to the observed
human performance, as well as the capacity to
generalise from it.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Fernlund, Hans K. G. and Gonzalez, Avelino J. and Georgiopoulos, Michael and DeMara, Ronald F.},
biburl = {https://www.bibsonomy.org/bibtex/263927b4e1685ab9ca238f890ee2d001d/brazovayeye},
doi = {doi:10.1109/TSMCB.2005.855568},
interhash = {8bb4d7c87b5043c3451ea52ac00a52dd},
intrahash = {63927b4e1685ab9ca238f890ee2d001d},
issn = {1083-4419},
journal = {IEEE Transactions on Systems, Man and Cybernetics,
Part B},
keywords = {(artificial Context-based acquisition, agent agents, algorithms, behavioral behavioural context-based development, elicitation, genetic human inference intelligence), knowledge learning learning, mechanisms, modeling, observation, performance programming, reasoning, representation, simulation software tactical},
month = {February},
notes = {INSPEC Accession Number:8736964
Dept. of Culture, Dalarna Univ., Borlange, Sweden},
number = 1,
pages = {128--140},
size = {13 pages},
timestamp = {2008-06-19T17:39:34.000+0200},
title = {Learning tactical human behavior through observation
of human performance},
url = {http://www.cal.ucf.edu/journal/j_fernlund_gonzalez_itsmc_04.pdf},
volume = 36,
year = 2006
}