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Conclusion

ドキュメント内 協調探索における通信戦略の研究 (ページ 40-65)

Chapter 6

92 Conclusion

an important problem. The implementation and thr ('Yaluatio11 of stra.trgirs ha.-;rd 011 �<'paratt'd historie are a future plan. It will also work on hetc-rogenrous ageuts.

In this thesis. we proposed a COOJWration schemr 011 cl istrihutrd systrms. Hnw<'HT coop­

erative processing is not only usdul on distributed .-ystrms hut also OIL concmT<'llt s�·strms.

One example that requires cooprration, rYrn if thr C'OllllllHllication cost ran hr iguon'cl. is a parallel search based on grnetic algorithms. A ·ea.r-ch procrss would fall in a local lllllllllltllll position by over-distribution of the best code at each st.rp. Thus broadcasting the 1H'st codc' pattern does not necessarily lead to the optimal form of computation. The sam< discussion is held on heuristic-base parallel search. In the. e examples, cooprration rluwgrs the way of sharing information between agents. Thus we can define cooprration as nwthods for selrcting an appropriate tructure of procrssing demrnt .. Its application is not rPstrictrcl to distributed systems. We think that local-history based mrthods like ours will br useful for buildiug sdtcmrs on such systems.

Our study assumes that the intention of a programmer is prrsentecl in a program but not a specification. Therefore we consider about neither rule-ba.srcl or operator-based clC'sniption nor MAS. Thus our approach has a restriction necessarily for building strongly autonontous systems. But we think there is a hierarchy of autouomy. At the top of thE' hi(•rarchy, th(' unit is human or a very autonomous agent. Tlwy can be clescribrd wrll by the t<'rlll of 1H'li<'f, d<'sin>, and intention(they are called as BDI theory). But the bottom lrvrl thC' d<'scriptiou of tit<·

goal of a computational unit is decomposed to a sequencr of orclrrs. If wr assnuH' tlH' uutnlH'r of computation unit in the real world becomes very large, to usr th<'m <'ffectivC'ly, pulliug parallelism off is the important issue, even if the platform is distributed <:wd thus we cm1 uot ignore the communication cost on them. Siuce there will be a hi< rarchy of physical clos<'H<'ss of computational units our approach would be used in the low-level of problrm-solviug st ratc•gy·!->

hierarchy.

Of course, history-ba.srd estimation method will br usdnl in high-lrv<'l plcutuing. It will require probabilistic reasoning. The emergence of intC'lligeucr is our of thr important r< sra.rch themes in Artificial intellig nee. Recent . tuclies about emergent computation invE'stigatr a way to make information proces. ing machine from a pool of simple units [For91. FM90]. Making

93

more intelligent system from units by a kind of rYolntiou rrqmres a lll('ta-calculatiou about utility like stability or uniqueness. Thrrrfore .'electing tlw input of itsC'lf and sd<'ctiug rdatNl modules is crucial. The issue about computation of connccti,·ity between th<' nnits will lw emerged once again

[

Lan90. I\:HH89

]

. TherrforP mor<' study about conn<'diYity sltoulcl h<' expected.

94 Conclusion

Acknowledgments

I would like to thank to Professor 1\:azuo U hijima at I\:yushu U11in'rsity for supporting this research and improving the quality of this thesis. I am very grateful to ProfC'ssor Akifumi Makinouchi, Professor Ryuzo Hasegawa and Professor I\:otaro Hira.sav\'a at 1\:ynshu (nin'rsity.

Their valuable commments are gratefully acknowledged.

I would like to express my apprecation to Associate Professor orihiko Yoshida. at 1\:yushu University for discussion about DAI, IAS, OOPL, and direction of this rC'sea.rch for long ,YC'ars.

I would also like to note two graduate students: Hiroomi Ya.mamura, the implcmeutC'r of the freqeuncy control strategy and Kenji Yamasaki, who impclementecl tllC' core part of CLOS MOP-based system.

95

96 Conclusion

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Katia P. Sycara, Steven F. Roth, Norman Sadeh, and 1Ia.rk S. Fox. Distrilmt<'d constrained h uristic search. IEEE Trans. Sy!3t. Man CybeTn. (Special J.c;,.;?u: on Distributed AI

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BIBLIOGRAPHY 107

[Sta89]

(WD92]

[

W

er92]

[YDI

I(

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[YN90 ]

(YN91]

[YNU95]

[Yok93 ]

(Yok95]

Susan Leigh Star. Tlw structure of ill-strnctnrrd solutions: Boundary object:-.

and heterogeneous distributed problem sol\'iug. In Gassrr and Hnhns [GH 0].

chapter 2, pages 37 54.

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Makoto Yokoo, Edmund

H.

Durfer, Toru Ishida. and I(azuhiro I\ uwabara. Dis­

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orihiko Yoshida and Shuji

1

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pages 530 536. I

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the Cooperation I'vfodel 'Cellula'.

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[YSTH 7]

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In Akinori Yonf'zawa and 1Iario Tokoro, <'ditors, Objl'ct-Oriented Cmu .. u.r-rent Programming. pages 55 9. ThC' :\IIT Press, 19 7.

Index

A* search, 68 agent, 2

agent-meta-class, 75 bounded rationality, 2

class, 74

communication-strategy, 77

defagent, 81

Distributed Artificial Intelligence, 2 Distributed Hearsay-!!, 1

Distributed Problem Solving 3

Distributed Vehicle Monitoring Testbed, 10 ergodic process, 26

instance, 7 4

metaclass, 74

MetaObject Protocol, 74 Multi-Agent System, 3 NK-model, 23

objects, 7 4

Pursuit Problem, 13

shared object manag<'lllrllt. 6 . -!

Tra.vC'ling Sale s lllan ProblC'Hl, 53 utility of commnuicatiou, 22 virtual sharC'd memory, 4

109

110 INDEX

Y / 7f:/

7 L. Joint Symposium on Paralld Processing '90

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Norihiko Yoshida. Shuji _ arazaki. ·· A Cooperation and Conllnnniratio11 FraBH'\\'ork for Distributed Problem Solving", Proceedings of IEEE 2ud International CoufctTur<' Ou Tools For Artificial Intelligence(

·fLJ:&;

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