LEADER 02482oam 2200649 450 001 9910706799303321 005 20171221105535.0 035 $a(CKB)5470000002457460 035 $a(OCoLC)896809971 035 $a(OCoLC)995470000002457460 035 $a(EXLCZ)995470000002457460 100 $a20141123d1971 ua 0 101 0 $aeng 135 $aurmn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSedimentary features of the Blackhawk formation (Cretaceous) in the Sunnyside District, Carbon County, Utah /$fby John O. Maberry 210 1$aWashington :$cUnited States Department of the Interior, Geological Survey,$d1971. 215 $a1 online resource (vi, 44 pages, 3 unnumbered pages of plates) $cillustrations, maps 225 1 $aGeological Survey professional paper ;$v688 300 $aTitle from title screen (viewed September 30, 2014). 300 $a"A discussion of the trace fossils, stratigraphy, depositional paleoenvironment, and engineering geology of coal-bearing rocks in a part of the Book Cliffs coal field of central Utah." 320 $aIncludes bibliographical references (pages 41-42) and index. 517 $aSedimentary features of the Blackhawk formation 606 $aGeology, Stratigraphic$yCretaceous 606 $aPaleontology$zUtah$zCarbon County 606 $aPetrology$zUtah$zCarbon County 606 $aSedimentary rocks$zUtah$zCarbon County 606 $aGeology$zBook Cliffs (Utah and Colo.) 606 $aCretaceous Geologic Period$2fast 606 $aGeology, Stratigraphic$2fast 606 $aPaleontology$2fast 606 $aPetrology$2fast 606 $aSedimentary rocks$2fast 607 $aUtah$zCarbon County$2fast 615 0$aGeology, Stratigraphic 615 0$aPaleontology 615 0$aPetrology 615 0$aSedimentary rocks 615 0$aGeology 615 7$aCretaceous Geologic Period. 615 7$aGeology, Stratigraphic. 615 7$aPaleontology. 615 7$aPetrology. 615 7$aSedimentary rocks. 700 $aMaberry$b John O.$f1935-$01400147 712 02$aGeological Survey (U.S.), 801 0$bCOP 801 1$bCOP 801 2$bOCLCO 801 2$bOCLCF 801 2$bGPO 906 $aBOOK 912 $a9910706799303321 996 $aSedimentary features of the Blackhawk formation (Cretaceous) in the Sunnyside District, Carbon County, Utah$93466552 997 $aUNINA LEADER 04213nam 22006495 450 001 9910983060803321 005 20250217115400.0 010 $a9783031747625 010 $a3031747623 024 7 $a10.1007/978-3-031-74762-5 035 $a(CKB)37627721600041 035 $a(MiAaPQ)EBC31910921 035 $a(Au-PeEL)EBL31910921 035 $a(DE-He213)978-3-031-74762-5 035 $a(OCoLC)1503844525 035 $a(EXLCZ)9937627721600041 100 $a20250217d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMulti-valued Logic for Decision-Making Under Uncertainty /$fby Evgeny Kagan, Alexander Rybalov, Ronald Yager 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Birkhäuser,$d2025. 215 $a1 online resource (296 pages) 225 1 $aComputer Science Foundations and Applied Logic,$x2731-5762 311 08$a9783031747618 311 08$a3031747615 327 $a1. Introduction -- 2. Background -- 3. Probability-generated multi-valued logic -- 4. Muli-valued logic algebra of subjective trusts -- 5. Algebra with non-commutative norms -- 6. Implementation of subjective trusts in control. 330 $aMulti-valued and fuzzy logics provide mathematical and computational tools for handling imperfect information and decision-making with rational collective reasoning and irrational individual judgements. The suggested implementation of multi-valued logics is based on the uninorm and absorbing norm with generating functions defined by probability distributions. Natural extensions of these logics result in non-commutative and non-distributive logics. In addition to Boolean truth values, these logics handle subjective truth and false values and model irrational decisions. Dynamics of decision-making are specified by the subjective Markov process and learning ? by neural network with extended Tsetlin neurons. Application of the suggested methods is illustrated by modelling of irrational economic decisions and biased reasoning in the wisdom-of-the-crowd method, and by control of mobile robots and navigation of their groups. Topics and features: Bridges the gap between fuzzy and probability methods Includes examples in the field of machine-learning and robots? control Defines formal models of subjective judgements and decision-making Presents practical techniques for solving non-probabilistic decision-making problems Initiates further research in non-commutative and non-distributive logics The book forms a basis for theoretical studies and practice of decision-making under uncertainty and will be useful for computer scientists and mathematicians interested in multi-valued and fuzzy logic, as well as for engineers working in the field of data mining and data analysis. Dr. Evgeny Kagan is with the Faculty of Engineering, Ariel University, Israel; Dr. Alexander Rybalov is with the LAMBDA Laboratory, Tel-Aviv University, Israel; and Prof. Ronald Yager is with the Machine Learning Institute, Yona College, New York, USA. 410 0$aComputer Science Foundations and Applied Logic,$x2731-5762 606 $aComputer science 606 $aAlgebraic logic 606 $aComputer science$xMathematics 606 $aMathematical statistics 606 $aComputer Science Logic and Foundations of Programming 606 $aAlgebraic Logic 606 $aProbability and Statistics in Computer Science 615 0$aComputer science. 615 0$aAlgebraic logic. 615 0$aComputer science$xMathematics. 615 0$aMathematical statistics. 615 14$aComputer Science Logic and Foundations of Programming. 615 24$aAlgebraic Logic. 615 24$aProbability and Statistics in Computer Science. 676 $a004.0151 700 $aKagan$b Evgeny$01785625 701 $aRybalov$b Alexander$01785626 701 $aYager$b Ronald$01785627 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910983060803321 996 $aMulti-valued Logic for Decision-Making Under Uncertainty$94317147 997 $aUNINA