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Approximate Reasoning by Parts: An Introduction to Rough Mereology 2011 ed. [Kõva köide]

  • Formaat: Hardback, 346 pages, kõrgus x laius: 235x155 mm, kaal: 1510 g, XIV, 346 p., 1 Hardback
  • Sari: Intelligent Systems Reference Library 20
  • Ilmumisaeg: 16-Jul-2011
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642222781
  • ISBN-13: 9783642222788
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  • Formaat: Hardback, 346 pages, kõrgus x laius: 235x155 mm, kaal: 1510 g, XIV, 346 p., 1 Hardback
  • Sari: Intelligent Systems Reference Library 20
  • Ilmumisaeg: 16-Jul-2011
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642222781
  • ISBN-13: 9783642222788
Teised raamatud teemal:
The monograph offers a view on Rough Mereology, a tool for reasoning under uncertainty, which goes back to Mereology, formulated in terms of  parts by Lesniewski, and borrows from Fuzzy Set Theory and Rough Set Theory ideas of the containment to a degree. The result is a theory based on the notion of a part to a degree.

 

One can invoke here a formula Rough: Rough Mereology : Mereology = Fuzzy Set Theory : Set Theory. As with Mereology, Rough Mereology finds important applications in problems of Spatial Reasoning, illustrated in this monograph with examples from Behavioral Robotics. Due to its involvement with concepts, Rough Mereology offers new approaches to Granular Computing, Classifier and Decision Synthesis, Logics for Information Systems, and are--formulation of  well--known ideas of Neural Networks and Many Agent Systems. All these approaches are discussed in this monograph.

 

To make the exposition self--contained,  underlying notions of Set Theory, Topology, and Deductive and Reductive Reasoning with emphasis on Rough and Fuzzy Set Theories along with a thorough exposition of Mereology both in Lesniewski and Whitehead--Leonard--Goodman--Clarke versions are discussed at length.

 

It is hoped that the monograph offers researchers in various areas of Artificial Intelligence a  new tool to deal with analysis of relations among concepts.
1 On Concepts. Aristotelian and Set-Theoretic Approaches
1(44)
1.1 An Aristotelian View on Concepts
1(5)
1.2 From Local to Global: Set Theory
6(9)
1.2.1 Naive Set Theory
7(1)
1.2.2 Algebra of Sets
7(5)
1.2.3 A Formal Approach
12(3)
1.3 Relations and Functions
15(3)
1.3.1 Algebra of Relations
16(2)
1.4 Ordering Relations
18(3)
1.5 Lattices and Boolean Algebras
21(1)
1.6 Infinite Sets
22(1)
1.7 Well-Ordered Sets
23(3)
1.8 Finite versus Infinite Sets
26(2)
1.9 Equipotency
28(2)
1.10 Countable Sets
30(1)
1.11 Filters and Ideals
31(2)
1.12 Equivalence Relations
33(2)
1.13 Tolerance Relations
35(2)
1.14 A Deeper Insight into Lattices and Algebras
37(8)
References
42(3)
2 Topology of Concepts
45(34)
2.1 Metric Spaces
45(5)
2.2 Products of Metric Spaces
50(1)
2.3 Compact Metric Spaces
51(2)
2.4 Complete Metric Spaces
53(3)
2.5 General Topological Spaces
56(2)
2.6 Regular Open and Regular Closed Sets
58(3)
2.7 Compactness in General Spaces
61(3)
2.8 Continuity
64(2)
2.9 Topologies on Subsets
66(1)
2.10 Quotient Spaces
67(1)
2.11 Hyperspaces
68(5)
2.11.1 Topologies on Closed Sets
68(5)
2.12 Cech Topologies
73(6)
References
77(2)
3 Reasoning Patterns of Deductive Reasoning
79(66)
3.1 The Nature of Exact Reasoning
79(1)
3.2 Propositional Calculus
80(8)
3.3 Many-Valued Calculi: 3-Valued Logic of Lukasiewicz
88(8)
3.4 Many-Valued Calculi: n-Valued Logic
96(3)
3.5 Many-Valued Calculi: [ 0,1]-Valued Logics
99(17)
3.5.1 MV-Algebras
114(2)
3.6 Many-Valued Calculi: Logics of Residual Implications
116(3)
3.7 Automated Reasoning
119(2)
3.8 Predicate Logic
121(12)
3.9 Modal Logics
133(12)
3.9.1 Modal Logic K
133(4)
3.9.2 Modal Logic T
137(1)
3.9.3 Modal Logic S4
138(1)
3.9.4 Modal Logic S5
138(3)
References
141(4)
4 Reductive Reasoning Rough and Fuzzy Sets as Frameworks
145(46)
4.1 Rough Set Approach Main Lines
145(3)
4.2 Decision Systems
148(2)
4.3 Decision Rules
150(2)
4.4 Dependencies
152(1)
4.5 Topology of Rough Sets
153(3)
4.6 A Rough Set Reasoning Scheme: The Approximate Collage Theorem
156(2)
4.7 A Rough Set Scheme for Reasoning about Knowledge
158(3)
4.8 Fuzzy Set Approach: Main Lines
161(7)
4.9 Residual Implications
168(1)
4.10 Topological Properties of Residual Implications
169(6)
4.11 Equivalence and Similarity in Fuzzy Universe
175(6)
4.12 Inductive Reasoning: Fuzzy Decision Rules
181(2)
4.13 On the Nature of Reductive Reasoning
183(8)
References
187(4)
5 Mereology
191(38)
5.1 Mereology: The Theory of Lesniewski
191(8)
5.2 A Modern Structural Analysis of Mereology
199(2)
5.3 Mereotopology
201(2)
5.4 Timed Mereology
203(4)
5.5 Spatio-temporal Reasoning: Cells
207(2)
5.6 Mereology Based on Connection
209(6)
5.7 Classes in Connection Mereology
215(1)
5.8 C-Quasi-Boolean Algebra
216(3)
5.9 C-Mereotopology
219(1)
5.10 Spatial Reasoning: Mereological Calculi
220(9)
5.10.1 On Region Connection Calculus
223(2)
References
225(4)
6 Rough Mereology
229(30)
6.1 Rough Inclusions
230(1)
6.2 Rough Inclusions: Residual Models
231(3)
6.3 Rough Inclusions: Archimedean Models
234(2)
6.4 Rough Inclusions: Set Models
236(1)
6.5 Rough Inclusions: Geometric Models
236(1)
6.6 Rough Inclusions: Information Models
237(3)
6.7 Rough Inclusions: Metric Models
240(1)
6.8 Rough Inclusions: A 3-Valued Rough Inclusion on Finite Sets
240(1)
6.9 Symmetrization of Rough Inclusions
241(1)
6.10 Mereogeometry
241(5)
6.11 Rough Mereotopology
246(6)
6.11.1 The Case of Transitive and Symmetric Rough Inclusions
246(4)
6.11.2 The Case of Transitive Non-symmetric Rough Inclusions
250(2)
6.12 Connections from Rough Inclusions
252(2)
6.12.1 The Case of Transitive and Symmetric Rough Inclusions
252(1)
6.12.2 The Case of Symmetric Non-transitive Rough Inclusions and the General Case
252(2)
6.13 Rough Inclusions as Many-Valued Fuzzy Equivalences
254(5)
References
256(3)
7 Reasoning with Rough Inclusions
259(38)
7.1 On Granular Reasoning
259(2)
7.2 On Methods for Granulation of Knowledge
261(2)
7.2.1 Granules from Binary Relations
261(1)
7.2.2 Granules in Information Systems from Indiscernibility
262(1)
7.2.3 Granules from Generalized Descriptors
263(1)
7.3 Granules from Rough Inclusions
263(1)
7.4 Rough Inclusions on Granules
264(1)
7.5 General Properties of Rough Mereological Granules
265(1)
7.6 Reasoning by Granular Rough Mereological Logics
266(3)
7.7 A Logic for Information Systems
269(3)
7.7.1 Relations to Many-Valued Logics
271(1)
7.8 A Graded Notion of Truth
272(5)
7.9 Dependencies and Decision Rules
277(1)
7.10 An Application to Calculus of Perceptions
278(2)
7.11 Modal Aspects of Rough Mereological Logics
280(3)
7.11.1 A Modal Logic with Ingredient Accessibility
281(1)
7.11.2 Modal Logic of Rough Set Approximations
282(1)
7.12 Reasoning in Multi-agent and Distributed Systems
283(5)
7.13 Reasoning in Cognitive Schemes
288(9)
7.13.1 Rough Mereological Perceptron
290(2)
References
292(5)
8 Reasoning by Rough Mereology in Problems of Behavioral Robotics
297(22)
8.1 Planning of Robot Motion
297(3)
8.2 Potential Fields from Rough Inclusions
300(3)
8.3 Planning for Teams of Robots
303(5)
8.4 Rough Mereological Approach to Robot Formations
308(11)
References
315(4)
9 Rough Mereological Calculus of Granules
319(16)
9.1 On Decision Rules
319(3)
9.2 The Idea of Granular Rough Mereological Classifiers
322(2)
9.3 Classification by Granules of Training Objects
324(2)
9.4 A Treatment of Missing Values
326(3)
9.5 Granular Rough Mereological Classifiers Using Residuals
329(2)
9.6 Granular Rough Mereological Classifiers with Modified Voting Parameters
331(4)
References
333(2)
Author Index 335(6)
Term Index 341