Knowledge-free & Learning-based Methods in Intelligent Game Playing

252 Pages·2010·2.7 MB·english

by  Jacek Mandziuk

Jacek Man´dziuk
Knowledge-Free and
Learning-Based Methods in
Intelligent Game Playing
123
Prof.JacekMan´dziuk
FacultyofMathematicsand
InformationScience
WarsawUniversityofTechnology
PlacPolitechniki1
00-661Warsaw
Poland
E-mail:[email protected]
ISBN 978-3-642-11677-3 e-ISBN 978-3-642-11678-0
DOI 10.1007/978-3-642-11678-0
Studiesin Computational Intelligence ISSN1860-949X
Library of Congress Control Number:2009943991
(cid:2)c 2010 Springer-VerlagBerlin Heidelberg
Foreword by David B. Fogel
At a time when biologicallyinspiredmachine intelligence tools arebecoming
crucial to the development of game-playing strategies, Jacek Man´dziuk has
written a book that focuses on exactly these tools. His book Knowledge-free
and Learning-based Methods in Intelligent Game Playing concentrates on
games requiring mental ability “mind games” as Man´dziuk describes them.
His extensivesurvey ofthe literature is aimedmainly onthose methods that
offerthepotentialtohaveacomputerteachitselfhowtoplayagamestarting
withverylittle (orno)knowledgeofthegame,ortolearnhowtoimproveits
play basedonfeedback about the quality ofits performance.His perspective
isoneofafuturist,lookingaheadtograndchallengesandthe computational
intelligence methods that may be used to meet those challenges.
To date, the greatest achievements in game play in most standard “mind
games” have come from knowledge-intensive methods, not knowledge-free
methods. They have come from programming human knowledge into the
software, rather than having the software learn for itself. This is true, for
example, for Othello, checkers,and chess.
But this is going to have to change because the games that now really
challengeusaresufficientlycomplicatedthattraditionalknowledge-intensive
methods will not yield satisfactory results. Computing power alone is un-
likely to be the basis for addressing Go, and even if that computational
power does become sufficient, we could easily imagine Go on a 190×190
board instead of the 19×19 traditional game, which our modern computers
alreadyfindvexing.Inthatcase,MonteCarlotreesearchestothe endofthe
game,endgamedatabases,andopeningbooksbasedonhumangrandmaster
knowledge would no longer be very helpful to the computers.
When compared to Go, the games than we humans play in the real world
areoffargreatercomplexity.Forexample,wecouldlooktoreal-timestrategy
video games that have thousands of “agents” acting simultaneously, but we
could just as well consider, say, the games of economics that are played be-
tween nations. Determining which countries should receive favorable trading
status and which should have tariffs placed on their goods is a game.Its not
played on a board or a table or a computer, but it is game nonetheless, and
the consequencesofwinning orlosingareusuallyfargreaterthanis foundin
acontestbetween,say,twocheckersplayers.Iwonderifanyonetrulybelieves
that the usual devices of traditional artificial intelligence will ever be very
successful in addressing such games. Certainly, I do not believe they will be.
An alternativeis to focus onhow humans adaptto different challenges,or
moregenerallyhowanyintelligentsystemsdothis.Intelligencethenbecomes
a matter of adapting behavior to meet goals across a range of environments,
andwecanobserveexamplesnotonlyourselves,butalsoinsocieties(suchas
ants) and in evolving organisms. Neural networks, evolutionary algorithms,
and reinforcement learning take center stage, and here Man´dziuk highlights
their application to games including backgammon, checkers, and Othello,
with additionalremarkstowardpoker,bridge,chess,Go,andother gamesof
strategy. His perspective is that the key to making major advancements in
intelligent game playing comes from capturing our human abilities such as
creativity, selective focus, and generalization.His conviction is compelling.
Thisisabookthatisbothbroadanddetailed,comprehensiveandspecific,
anditoffersavisiontowardthechallengesthatawaitus.Withtheexpanding
interest in game playing Man´dziuk offers a timely book that will serve the
growing games community for years to come. I hope you find it a source of
continual inspiration.
David B. Fogel
Lincoln Vale CA LP
Natural Selection, Inc.
San Diego, California, USA
Foreword by Simon M. Lucas
Sincetheseminalworkofthefoundingfathersofcomputerscienceandinfor-
mation theory, people such as Alan Turing and Claude Shannon, there has
been a rich history of researchapplied to games,and without doubt this has
contributedgreatlytothedevelopmentofartificialintelligenceasadiscipline.
The focus of this book is on Mind Games, games of skill such as Chess, Go,
and Bridge which predate computers. There have been two approaches to
designingagentscapableofahighstandardofplayinsuchgames:handpro-
gramming, and machine learning. Both methods have had their high-profile
successes,with Deep Blue (Chess)being anexemplar ofexpert hand-tuning,
and TD-Gammon (Backgammon), Logistello (Othello) and Blondie (Check-
ers) being exponents of the machine-learning approaches (though each of
these differed in the nature of the learning). As the book argues, there is
much more to this field than simply designing agents that are capable of a
highstandardofplay.Thewayinwhichitisachievedisofgreatimportance.
Hand programmedagents tend to be specific to a particular game. Learning
agents on the other hand offer the potential of a more general ability, and
the ability to learn is central to most meaningful definitions of intelligence.
Games offer an excellent application domain and a demanding test-bed for
AI and CI techniques.
This bookoffersaunique perspectiveonthesubject,embracingbothcon-
ventionalAIapproachestogame-playingaswellastheopen-endedchallenges
that games pose for computational intelligence. It gives a detailed account
of the main events in the development of the field, and includes the most
important algorithms in game-tree search, temporal difference learning, and
in evolutionary game learning, and describes in detail how these have been
appliedtoarangeofdifferentmindgames.Thefocusonmindgamesenables
a deep coverage of the most important researchin this area.
The book is organised into four parts. The first part covers the founda-
tions including the main game-tree searchalgorithms and then discusses the
state of the art in the most important mind games. Part two describes the
main computational intelligence methods that have been applied to learn
gamestrategy,anddescribesselectedTDandneuro-evolutionaryapproaches
to specific games including Backgammon, checkers, and Othello. Part three
then goes into greater detail on how to get the best out of these methods,
including the choice of function approximation architecture, the selection of
game-features, and tuning the algorithms for best performance. It also dis-
cusses the main approaches to move ordering, and to opponent modelling.
Part four describes some of the grand challenges in game playing, organised
into chapters on intuition, creativity and knowledge discovery, and general
game playing.
This is a wellbalancedbook that celebratesthe greatachievementsofthe
field while pointing out that many basic questions have yet to be answered.
Atalltimes the authorshowsanimpressiveknowledgeofthe subjectandan
ability to communicate this clearly.The book makes fascinating reading and
should be of great interest to researchers involved in all kinds of computa-
tionalintelligence andAI, and willhelp attractmorepeople to study games.
For PhD students starting out in the area it should be regarded as essen-
tial, and would also be useful as recommended reading for taught courses in
computational intelligence and games.
Professor Simon M. Lucas
University of Essex, UK
Editor-in-Chief
IEEE Transactions on Computational Intelligence and AI in Games
Preface
Humans and machines are very different in their approaches to game play-
ing. Humans use intuition, perception mechanisms, selective search, creativ-
ity, abstraction, heuristic abilities and other cognitive skills to compensate
their (comparably) slow information processing speed, relatively low mem-
ory capacity, and limited search abilities. Machines, on the other hand, are
extremely fast and infallible in calculations, capable of effective brute-force-
type search, use “unlimited” memory resources, but at the same time are
poor at using reasoning-based approaches and abstraction-based methods.
The above major discrepancies in the human and machine problem solving
methods underlined the development of traditional machine game playing
as being focused mainly on engineering advances rather than cognitive or
psychological developments. In other words, as described by Winkler and
Fu¨rnkranz [347, 348] with respect to chess, human and machine axes of
game playingdevelopmentareperpendicular,but the mostinteresting,most
promising,andprobablyalsomostdifficult researcharealieson the junction
between human-compatible knowledge and machine compatible processing. I
undoubtedly share this point of view and strongly believe that the future
of machine game playing lies in implementation of human-type abilities (ab-
straction,intuition,creativity,selectiveattention,andother)whilestilltaking
advantage of intrinsic machine skills.
Thebookisfocusedonthedevelopmentsandprospectivechallengingprob-
lemsintheareaofmindgameplaying(i.e.playinggames that require mental
skills) using Computational Intelligence (CI) methods, mainly neural net-
works, genetic/evolutionary programming and reinforcement learning.
The majority of discussed game playing ideas were selected based on
their functional similarity to human game playing. These similarities in-
clude:learningfromscratch,autonomousexperience-basedimprovementand
example-basedlearning. The above features determine the major distinction
betweenCIandtraditionalAImethodsrelyingmostlyonusingeffectivegame
tree search algorithms, carefully tuned hand-crafted evaluation functions or
hardware-basedbrute-force methods.
On the other hand, it should be noted that the aim of this book is by no
means to underestimate the achievementsoftraditionalAI methods in game
playing domain. On the contrary,the accomplishments of AI approachesare
undisputableandspeakforthemselves.Thegoalisrathertoexpressmybelief
that other alternative ways of developing mind game playing machines are
possible and urgently needed.
DuetorapiddevelopmentoftheAI/CIapplicationsinmindgameplaying,
discussingorevenmentioningallrelevantpublicationsinthisareaiscertainly
beyond the scope of the book. Hence, the choice of references and presented
methodshadtobe restrictedtoarepresentativesubset,selectedsubjectively
based on my professional knowledge and experience.
The book starts with the Introduction followed by 14 chapters grouped
into4parts.Theintroductorychapterprovidesabriefoverviewofthebook’s
subject, further elaborating on the reasons behind its origin and discussing
its content in more detail. The first part of the book entitled AI tools and
state-of-the-artaccomplishments inmindgames coversabriefoverviewofthe
field, discusses basic AI methods and concepts used in mind game research
and presents some seminal examples of AI game playing systems.
Part II - CI methods in mind games. Towards human-like playing starts
withanoverviewofmajorsubdisciplines withinCI,namelyneuralnetworks,
evolutionary methods, and reinforcement learning, in the context of game
playing developments. The other chapter in Part II emphasizes the selected
accomplishments of Computational Intelligence in the field.
The third part - An overview of challenges and open problems presents,
chapter-by-chapter, five aspects of mind game playing, which I consider
challenging for CI methods. These areas are related to evaluation function
learning,efficientgamerepresentations,effectivetrainingschemes,search-free
playing and modeling the opponent. The topics are presented in the context
ofachievementsmadetodate,openproblems,andpossiblefuturedirections.
The final part of the book, entitled Grand challenges, is devoted to the
most challenging problems in the field, namely: implementation of intuitive
playing,creativityandmulti-gameplaying.Allthree above-mentionedissues
are typical for human approaches to mind games and at the same time ex-
tremely hardto implement in game playing machines - partly due to not yet
fully uncovered mechanisms of perception, representation and processing of
game information in the human brain. The last chapter highlights the main
conclusions of the book and summarizes prospective challenges.
Insummary,Ibelievethattheneedforfurtherdevelopmentofhuman-like,
knowledge-free methods in mind games is unquestionable, and the ultimate
goal that can be put forward is building a truly autonomous, human-like
multi-game playing agent. In order to achieve this goal several challenging
problems have to be addressed and solved on the way. Presentation of these
challengingissuesisthe essenceofthis book.I hopethe bookwillbe helpful,
especially for young scientists entering the field, by summarizing the state-
of-the-art accomplishments and rising new research perspectives.
IwouldliketothankProf.JanuszKacprzykfortheinspirationforwriting
this book and Prof. Wl(cid:4)odzis(cid:4)law Duch for fruitful discussions concerning the
scopeofthebookandrelatedsubjects.I’mverygratefultoProf.DavidFogel
and Prof. Simon Lucas for writing forewords and for encouraging comments
regardingthebook’sscopeandcontent.Iwouldalsoliketothankmyformer
and current PhD students: Cezary Dendek, Krzysztof Mossakowski, Daniel
Osman and Karol Wale¸dzik, as well as other co-authors of my papers dis-
cussed in this book for a satisfying and productive cooperation. I wish to
thank KarolWale¸dzik for his valuable comments andsuggestionsconcerning
the initial version of the book. Last but not least I’m grateful to my family
for supporting me in this undertaking.
Warsaw,
September 2009 Jacek Man´dziuk
Contents
1 Introduction............................................. 1
Part I: AI Tools and State-of-the-Art Accomplishments
in Mind Games
2 Foundations of AI and CI in Games. Claude Shannon’s
Postulates ............................................... 11
3 Basic AI Methods and Tools............................. 15
3.1 Definitions and Notation .............................. 15
3.2 Game Tree Searching ................................. 16
3.2.1 Minimax Algorithm ........................... 17
3.2.2 Alpha-Beta Search ............................ 18
3.2.3 Heuristic Enhancements ....................... 20
3.2.4 SCOUT and Negascout (PVS).................. 27
3.2.5 MTD(f) and MTD-bi ......................... 29
3.3 Rollout Simulations and Selective Sampling.............. 31
3.3.1 UCT Algorithm .............................. 36
4 State of the Art ......................................... 41
4.1 ..., Deep Blue, Fritz, Hydra, Rybka, ... .................. 41
4.2 Chinook – A Perfectly Playing Checkers Machine......... 43
4.3 Logistello – The Othello World Champion............... 44
4.4 Maven and Quackle – The Top Level Machine Scrabble
Players ............................................. 45
4.5 Poker and Bridge – Slightly behind Humans ............. 47
4.6 Go – The Grand Challenge ............................ 49