LEADER 03895nam 2200637 450 001 9910460564603321 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(MiAaPQ)EBC1787101 035 $a(Au-PeEL)EBL1787101 035 $a(CaPaEBR)ebr11049409 035 $a(CaONFJC)MIL808341 035 $a(OCoLC)909907883 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 608 $aElectronic books. 615 0$aEvolutionary robotics. 676 $a629.8/92 700 $aKo?nig$b Lukas$01049330 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910460564603321 996 $aComplex behavior in evolutionary robotics$92485955 997 $aUNINA