LEADER 01432nam2-2200409li-450 001 990000202660203316 005 20180312154755.0 010 $a3-540-56869-7 035 $a0020266 035 $aUSA010020266 035 $a(ALEPH)000020266USA01 035 $a0020266 100 $a20001109d1993----km-y0itay0103----ba 101 0 $aeng 102 $aGW 200 1 $aAdvances in spatial database$ethird International symposium, SSD'93$eSingapore, June 23-25, 1993 :proceedings$fDavid Abel, (ed.) 210 $aBerlin [etc.]$cSpringer-Verlag$dcopyr. 1993 215 $aXIII, 529 p.$cill.$d23 cm 225 2 $aLecture notes in computer science$v692 410 0$10010020264$12001$aLecture notes in computer science 610 1 $aarchivi di dati$acongressi$a1993 610 1 $acongressi$asingapore$a1993 676 $a00574$9Archivi di dati e basi di dati 702 1$aAbel,$bDavid 710 12$aInternational symposium SSD'93$d3.$eSingapore$f1993$0753232 801 $aSistema bibliotecario di Ateneo dell' Università di Salerno$gRICA 912 $a990000202660203316 951 $a001 LNCS (692)$b0015010$c001$d00102937 959 $aBK 969 $aSCI 979 $c19971124 979 $c20001110$lUSA01$h1714 979 $aALANDI$b90$c20010129$lUSA01$h1604 979 $c20020403$lUSA01$h1628 979 $aPATRY$b90$c20040406$lUSA01$h1615 996 $aAdvances in spatial database$91515168 997 $aUNISA LEADER 03901nam 2200637 450 001 9910817699203321 005 20200520144314.0 010 $a1-5231-0453-8 010 $a3-11-040855-4 010 $a3-11-040918-6 024 7 $a10.1515/9783110408553 035 $a(CKB)3710000000393036 035 $a(EBL)1787101 035 $a(SSID)ssj0001458299 035 $a(PQKBManifestationID)12603626 035 $a(PQKBTitleCode)TC0001458299 035 $a(PQKBWorkID)11451794 035 $a(PQKB)11325771 035 $a(DE-B1597)445215 035 $a(OCoLC)979626854 035 $a(DE-B1597)9783110408553 035 $a(Au-PeEL)EBL1787101 035 $a(CaPaEBR)ebr11049409 035 $a(CaONFJC)MIL808341 035 $a(OCoLC)909907883 035 $a(CaSebORM)9783110408546 035 $a(MiAaPQ)EBC1787101 035 $a(EXLCZ)993710000000393036 100 $a20141210h20152015 uy| 0 101 0 $aeng 135 $aur|nu---|u||u 181 $ctxt 182 $cc 183 $acr 200 10$aComplex behavior in evolutionary robotics /$fLukas Ko?nig 210 1$aBoston :$cDe Gruyter,$d[2015] 210 4$d©2015 215 $a1 online resource (262 p.) 300 $aDescription based upon print version of record. 311 0 $a3-11-040917-8 311 0 $a3-11-040854-6 320 $aIncludes bibliographical references and index. 327 $tFront matter --$tAcknowledgements --$tContents --$tList of Figures --$tList of Tables --$tList of Notations --$t1. Introduction --$t2. Robotics, Evolution and Simulation --$t3. The Easy Agent Simulation --$t4. Evolution Using Finite State Machines --$t5. Evolution and the Genotype-Phenotype Mapping --$t6. Data Driven Success Prediction of Evolution in Complex Environments --$t7. Conclusion --$tReferences --$tIndex 330 $aEs werden vier neue Lösungsansätze für Probleme aus dem Bereich Evolutionäre Robotik bzw. Agenten-Simulation wissenschaftlich untersucht. Von besonderem Interesse ist eine neuartige Methode zur Imitierung der natürlichen Evolution in ihrer Fähigkeit, die eigenen Mutations- und Rekombinationsoperationen während der Evolution von Robotern anzupassen. 330 $aToday, autonomous robots are used in a rather limited range of applications such as exploration of inaccessible locations, cleaning floors, mowing lawns etc. However, ongoing hardware improvements (and human fantasy) steadily reveal new robotic applications of significantly higher sophistication. For such applications, the crucial bottleneck in the engineering process tends to shift from physical boundaries to controller generation. As an attempt to automatize this process, Evolutionary Robotics has successfully been used to generate robotic controllers of various types. However, a major challenge of the field remains the evolution of truly complex behavior. Furthermore, automatically created controllers often lack analyzability which makes them useless for safety-critical applications. In this book, a simple controller model based on Finite State Machines is proposed which allows a straightforward analysis of evolved behaviors. To increase the model's evolvability, a procedure is introduced which, by adapting the genotype-phenotype mapping at runtime, efficiently traverses both the behavioral search space as well as (recursively) the search space of genotype-phenotype mappings. Furthermore, a data-driven mathematical framework is proposed which can be used to calculate the expected success of evolution in complex environments. 606 $aEvolutionary robotics 615 0$aEvolutionary robotics. 676 $a629.8/92 700 $aKo?nig$b Lukas$01663123 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910817699203321 996 $aComplex behavior in evolutionary robotics$94092612 997 $aUNINA