LEADER 03932nam 22006375 450 001 9910254162503321 005 20200629124310.0 010 $a3-319-53609-5 024 7 $a10.1007/978-3-319-53609-5 035 $a(CKB)3710000001079865 035 $a(DE-He213)978-3-319-53609-5 035 $a(MiAaPQ)EBC6298165 035 $a(MiAaPQ)EBC5578297 035 $a(Au-PeEL)EBL5578297 035 $a(OCoLC)973879035 035 $a(PPN)198869150 035 $a(EXLCZ)993710000001079865 100 $a20170215d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAchieving Consensus in Robot Swarms $eDesign and Analysis of Strategies for the best-of-n Problem /$fby Gabriele Valentini 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XIV, 146 p. 46 illus., 37 illus. in color.) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v706 311 $a3-319-53608-7 320 $aIncludes bibliographical references. 327 $aIntroduction -- Part 1:Background and Methodology -- Discrete Consensus Achievement in Arti?cial Systems -- Modular Design of Strategies for the Best-of-n Problem -- Part 2:Mathematical Modeling and Analysis -- Indirect Modulation of Majority-Based Decisions -- Direct Modulation of Voter-Based Decisions -- Direct Modulation of Majority-Based Decisions -- Part 3:Robot Experiments -- A Robot Experiment in Site Selection -- A Robot Experiment in Collective Perception -- Part 4:Discussion and Annexes -- Conclusions -- Background on Markov Chains. 330 $aThis book focuses on the design and analysis of collective decision-making strategies for the best-of-n problem. After providing a formalization of the structure of the best-of-n problem supported by a comprehensive survey of the swarm robotics literature, it introduces the functioning of a collective decision-making strategy and identi?es a set of mechanisms that are essential for a strategy to solve the best-of-n problem. The best-of-n problem is an abstraction that captures the frequent requirement of a robot swarm to choose one option from of a ?nite set when optimizing bene?ts and costs. The book leverages the identi?cation of these mechanisms to develop a modular and model-driven methodology to design collective decision-making strategies and to analyze their performance at different level of abstractions. Lastly, the author provides a series of case studies in which the proposed methodology is used to design different strategies, using robot experiments to show how the designed strategies can be ported to different application scenarios. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v706 606 $aComputational intelligence 606 $aRobotics 606 $aAutomation 606 $aArtificial intelligence 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aRobotics and Automation$3https://scigraph.springernature.com/ontologies/product-market-codes/T19020 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aComputational intelligence. 615 0$aRobotics. 615 0$aAutomation. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aRobotics and Automation. 615 24$aArtificial Intelligence. 676 $a006.3824 700 $aValentini$b Gabriele$4aut$4http://id.loc.gov/vocabulary/relators/aut$0911790 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254162503321 996 $aAchieving Consensus in Robot Swarms$92041863 997 $aUNINA LEADER 02619nam 22004453 450 001 9910163208803321 005 20250827080354.0 010 $a9781782898146 010 $a178289814X 035 $a(CKB)3710000001046234 035 $a(BIP)054487290 035 $a(VLeBooks)9781782898146 035 $a(Perlego)3018585 035 $a(MiAaPQ)EBC32202947 035 $a(Au-PeEL)EBL32202947 035 $a(Exl-AI)993710000001046234 035 $a(OCoLC)1534806444 035 $a(EXLCZ)993710000001046234 100 $a20250827d2014 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAppeasement Reconsidered 205 $a1st ed. 210 1$aWaipu :$cPickle Partners Publishing,$d2014. 210 4$dİ2014. 215 $a1 online resource (64 p.) 330 8 $aThe appeasement of Nazi Germany by the western democracies during the 1930s and the subsequent outbreak of World War II have been a major referent experience for U.S. foreign policymakers since 1945. From Harry Truman's response to the outbreak of the Korean War to George W. Bush's decision to overthrow Saddam Hussein, American presidents have repeatedly affirmed the "lesson" of Munich and invoked it to justify actual or threatened uses of force. However, the conclusion that the democracies could easily have stopped Hitler before he plunged the world into war and holocaust, but lacked the will to do so, does not survive serious scrutiny. Appeasement proved to be a horribly misguided policy against Hitler, but this conclusion is clear only in hindsight - i.e., through the lens of subsequent events.Dr. Jeffrey Record takes a fresh look at appeasement within the context of the political and military environments in which British and French leaders operated during the 1930s. He examines the nature of appeasement, the factors underlying Anglo-French policies toward Hitler from 1933 to 1939, and the reasons for the failure of those policies. He finds that Anglo-French security choices were neither simple nor obvious, that hindsight has distorted judgments on those choices, that Hitler remains without equal as a state threat, and that invocations of the Munich analogy should always be closely examined. 606 $aNazi propaganda$7Generated by AI 615 0$aNazi propaganda 700 $aRecord$b Professor Jeffrey$01434292 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910163208803321 996 $aAppeasement Reconsidered$94424513 997 $aUNINA