Computational Intelligence - February 2017 - 51

Table 1 Summary of LCS Literature on Games.
reF

YOP

TOg

Sub-CaT

NOg

TOlCS

[58], [94]

1995

STrATegy

reSTriCTed gAmeS

ring gAme

CS-1

STrATegy

non-CooperATive gAmeS

eleCTriCiTy mArKeT

XCS

[97]
[99]

2001

STrATegy

non-SequenTiAl,
non-CooperATive

iTerATed priSoner'S dilemmA

CS-1

[29]

2002

STrATegy

minoriTy

el FArol BAr

ZCS

[60]-[62]

2002

role plAying

mmorpg

ryZom

mHiCS

[66]

2003

role plAying

mulTi-AgenT

roBoCup

XCS

[73]

2003

role plAying

mulTi-AgenT

TAnK BATTle

ZCS

[90]

2003

SimulATion

eduCATion, poliCy

BuSineSS SimulATor

XCS

[82], [83]

2005

ComBinAToriAl

BoArd

ConneCT4

XCS

[86]

2005

STrATegy

vAriouS

nim, ipd, mATriX CHoiCe

XCS

[68]-[72]

2005

role plAying

mulTi-AgenT

roBoSoCCer

XCS

[63]

2006

role plAying

Single-AgenT

v-mAn

XCS

[106]

2007

STrATegy

non-SequenTiAl,
non-CooperATive

priSoner'S dilemmA

CS-1

[74], [75]

2008

role plAying

Single-AgenT

WArguS

XCS

[76]

2009

role plAying

Single-AgenT

unreAl TournAmenT

piTTSBurgH-STyle

[107]

2010

STrATegy

SequenTiAl, non-CooperATive

ulTimATum gAme

CS-1

[77]

2010

role plAying

mulTi-AgenT

roBoCode

XCS

[81]

2010

role plAying

Single-AgenT

pAC-mAn

oCS

[88]

2011

ComBinAToriAl

BoArd

doTS And BoXeS

ArCS

[78]

2011

role plAying

Single-AgenT

BATTle gAme

ZCS

[91]

2011

lAnguAge gAme

lAnguAge gAme

XCS

[104]

2013

STrATegy

minoriTy

mulTi eFB

XCS

[80]

2013

role plAying

Single-AgenT

BATTleSHip

XCS

[92]

2014

SimulATion

poliCy

SToCK mArKeT

XCS

[64]

2015

role plAying

Single-AgenT

Air-ComBAT

edS

legend: yop=year of publication; Tog=Type of game; SuB-CAT=Sub-category of game; nog=name of game; TolCS = Type of lCS.

which LCS is applied; names of games;
and types of LCS used to model agents
in these games. The earliest application
of LCS to games occurred in the mid
1990s. The initial studies mostly used
Holland's CS-1 in their models. Later,
the focus shifted to the use of XCS,
which became the most frequently and
successfully used classifier system in this
literature. However, the original LCS
models continue to be used to this day.
A key feature that has been highlighted by this survey is the flexibility of
LCS, which allows modeling, both simple as well as meta-learning agent architectures, under the same framework.
This powerful feature can be leveraged
in scaling LCS to higher-fidelity and

more complex game environments. The
methods used to represent the interfacing environments, or the type of input
to the system, and encoding classifiers
seemed to play a significant role in the
performance of LCS-based agents.
While binary encoding was the dominant method used in the reviewed
approaches, this could be limiting in
providing adequate control resolution
and the range of behaviors that the
agents could exhibit. The use of real-valued [109], [110] or other types of
encoding [111], [112] could be explored,
with consideration given to their own
costs [113].
Most of the reviewed works were
published in conferences or workshops.

A major shortcoming of most publications was that they did not provide
important implementation details for
non-LCS users, making it harder to
adopt LCS for games outside the LCS
research community. The availability of
online source codes for specific LCSbased game agent implementations could
help improve the visibility of these
approaches, as well as their acceptance by
the wider research community. Many of
the reviewed works did not provide a
deep analysis of the system and its performance, especially for more complex
system implementations, such as MHiCS.
Therefore, the development of analysis
tools for game environments is another
important research direction in this field.

FEbruary 2017 | IEEE ComputatIonal IntEllIgEnCE magazInE

51



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