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  • Produktbild: The Knowledge Frontier
  • Produktbild: The Knowledge Frontier

The Knowledge Frontier Essays in the Representation of Knowledge

Aus der Reihe Artificial Intelligence

51,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

23.09.2011

Herausgeber

Nick Cercone + weitere

Verlag

Springer Us

Seitenzahl

512

Maße (L/B/H)

23,5/15,5/3 cm

Gewicht

826 g

Auflage

Softcover reprint of the original 1st ed. 1987

Sprache

Englisch

ISBN

978-1-4612-9158-9

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

23.09.2011

Herausgeber

Verlag

Springer Us

Seitenzahl

512

Maße (L/B/H)

23,5/15,5/3 cm

Gewicht

826 g

Auflage

Softcover reprint of the original 1st ed. 1987

Sprache

Englisch

ISBN

978-1-4612-9158-9

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: The Knowledge Frontier
  • Produktbild: The Knowledge Frontier
  • 1. What Is Knowledge Representation?.- 1.1. Introduction.- 1.2. Logic representations.- 1.2.1. Default logic.- 1.2.2. Fuzzy logic.- 1.3. Semantic networks.- 1.3.1. Partitioned networks.- 1.3.2. Marker propagation schemes.- 1.3.3. Topic hierarchies.- 1.3.4. Propositional networks.- 1.3.5. Semantic networks and logic.- 1.4. Procedural representations.- 1.4.1. Winograd’s work.- 1.4.2. Procedural semantic networks.- 1.5. Logic programming.- 1.6. Frame-based representations.- 1.7. Production system architectures.- 1.8. Knowledge representation languages.- 1.8.1. KL-One.- 1.8.2. KRYPTON.- 1.8.3. Other languages.- 1.9. Concluding remarks.- 2. Knowledge Representation: What’s Important About It?.- 2.1. Introduction.- 2.2. Knowledge for reasoning agents.- 2.3. Modeling the external world.- 2.4. Perception and reasoning by machine.- 2.5. The Nature of the world and its models.- 2.6. The functions of a knowledge representation system.- 2.7. The knowledge acquisition problem.- 2.8. The perception problem.- 2.9. Planning to act.- 2.10. Role of a conceptual taxonomy for an intelligent agent.- 2.11. The structure of concepts.- 2.12. An example of a conceptual taxonomy.- 2.13. The need for taxonomic organization.- 2.14. Recognizing/analyzing/parsing situations.- 2.15. Two aspects of knowledge representation.- 2.16. Expressive adequacy.- 2.17. Notational efficacy.- 2.18. The relationship to formal logic.- 2.19. Concepts are more than predicates.- 2.20. Conclusions.- 3. Some Remarks on the Place of Logic in Knowledge Representation.- 3.1. Introduction.- 3.2. What is logic?.- 3.3. On being logical.- 3.4. Reasoning and logic.- 3.5. Nonmonotonic logic.- 3.6. Conclusion.- 4. Logic and Natural Language.- 4.1. Introduction.- 4.2. Default logic for computing presuppositions.- 4.3. Modal logic for planning utterances.- 4.4. Temporal logic for reasoning about futures.- 4.5. Conclusion.- 5. Commonsense and Fuzzy Logic.- 5.1. Introduction.- 5.2. Meaning representation in test-score semantics.- 5.3. Testing and translation rules.- 5.3.1. Composition of elastic constraints.- 5.4. Representation of dispositions.- 5.5. Reasoning with dispositions.- 5.6. Concluding remark.- 6. Basic Properties of Knowledge Base Systems.- 6.1. Introduction.- 6.2. Basic notions.- 6.3. Completeness & consistency of rule-represented knowledge bases.- 6.4. The case of linear sets of rules.- 6.5. Dependency of rules on attributes.- 6.6. Partial information and defaults.- 6.7. Conclusion.- 7. First Order Logic and Knowledge Representation: Some Problems of Incomplete Systems.- 7.1. Introduction.- 7.2. Prolog & Absys: declarative knowledge manipulation systems.- 7.3. Primitive goal selection strategies in Absys and Prolog.- 7.4. Selection strategies and knowledge systems.- 7.5. Summary.- 8. Admissible State Semantics for Representational Systems.- 8.1. Introduction - the problem of practical semantics.- 8.2. Internal and external meanings.- 8.3. Admissible state semantics.- 8.4. Example: semantic networks.- 8.5. Example: k-lines.- 8.6. Conclusion.- 9. Accelerating Deductive Inference: Special Methods for Taxonomies, Colours and Times.- 9.1. Introduction.- 9.2. Recognizing type relationships.- 9.3. Recognizing part-of relationships.- 9.4. Recognizing colour relationships.- 9.5. Recognizing time relationships.- 9.6. Combining general and special methods.- 9.7. Concluding remarks.- 10. Knowledge Organization and Its Role in Temporal and Causal Signal Understanding: The ALVEN and CAA Projects.- 10.1. Introduction.- 10.2. The representational scheme.- 10.2.1. Knowledge packages: classes.- 10.2.2. Knowledge organization.- 10.2.3. Multi-dimensional levels of detail.- 10.2.4. Time.- 10.2.5. Exceptions and similarity relations.- 10.2.6. Partial results and levels of description.- 10.3. The interpretation control structure.- 10.4. The ALVEN project.- 10.4.1. Overview.- 10.4.2. LV dynamics knowledge and its representation.- 10.5. The CAA project.- 10.5.1. Overview.- 10.5.2. Representation of causal connections.- 10.5.3. Use of causal links.- 10.5.4. Recent research related to causality.- 10.5.5. Representation of domain knowledge.- 10.5.6. Knowledge-base stratification and projection links.- 10.5.7. Recognition strategies and control.- 10.6. Conclusions.- 11. SNePS Considered as a Fully Intensional Propositional Semantic Network.- 11.1. Introduction.- 11.1.1. The SNePS environment.- 11.1.2. SNePS as a knowledge representation system.- 11.1.3. Informal description of SNePS.- 11.2. Intensional knowledge representation.- 11.3. Description of SNePS/CASSIE.- 11.3.1. CASSIE - A model of a mind.- 11.3.2. A conversation with CASSIE.- 11.3.3. Syntax and semantics of SNePS/CASSIE.- 11.3.4. The conversation with CASSIE, revisited.- 11.4. Extensions and applications of SNePS.- 11.4.1. SNePS as a database management system.- 11.4.2. Address recognition for mail sorting.- 11.4.3. NEUREX.- 11.4.4. Representing visual knowledge.- 11.4.5. SNeBR: A belief revision package.- 11.5. Knowledge-based natural language understanding.- 11.5.1. Temporal structure of narrative.- 11.6. Conclusion: SNePS and SNePS/CASSIE as Semantic Networks.- 11.6.1. Criteria for semantic networks.- 11.6.2. SNePS and SNePS/CASSIE vs. KL-One.- 12. Representing Virtual Knowledge Through Logic Programming.- 12.1. Introduction.- 12.2. Representing knowledge in Prolog.- 12.3. Asking for inferences - virtual knowledge.- 12.4. Representing problem-solving knowledge.- 12.5. Representing database knowledge.- 12.6. Limitations.- 12.7. Conclusions.- 13. Theorist: A Logical Reasoning System for Defaults and Diagnosis.- 13.1. Introduction.- 13.2. Prolog as a representation system.- 13.3. The Theorist framework.- 13.4. Tasks appropriate for the Theorist framework.- 13.4.1. Nonmonotonic reasoning - reasoning with default and generalised knowledge.- 13.4.2. Diagnosis.- 13.4.3. Learning as theory construction.- 13.4.4. User modelling as theory maintenance.- 13.4.5. Choices in mundane tasks.- 13.5. Representation and reasoning in theorist.- 13.5.1. Extending Horn clauses to full first order logic.- 13.5.2. Reasoning as the construction of consistent theories.- 13.6. Implementing a Theorist prototype in Prolog.- 13.6.1. Not parallelism.- 13.7. Status and conclusions.- 14. Representing and Solving Temporal Planning Problems.- 14.1. Introduction.- 14.2. The Time Map Manager.- 14.2.1. A Predicate Calculus Database.- 14.2.2. Adding Basic Concepts of Time.- 14.2.3. Events and Persistences.- 14.2.4. Temporal Database Queries.- 14.2.5. Chaining Rules in a Temporal Database.- 14.2.6. A Simple Planner Based on the TMM.- 14.3. The Heuristic Task Scheduler.- 14.3.1. Describing a Resource.- 14.3.2. Describing a Plan.- 14.3.3. Specifying Plan Resource Use.- 14.3.4. Specifying Plan Tasks.- 14.3.5. Specifying Plan Constraints.- 14.3.6. Producing a Completed Linear Task Ordering.- 14.4 Summary and Conclusions.- 15. Analogical Modes of Reasoning and Process Modelling.- 15.1. Introduction to analogical reasoning.- 15.2. WHISPER: A program using analogs.- 15.3. Observations on the use of analogs.- 15.4. Mental rotation as an analog process.- 15.5. Conclusions.- 16. Representing and Using Knowledge of the Visual World.- 16.1. Introduction.- 16.2. Progress in high-level vision.- 16.3. The complexity barrier.- 16.4. Achieving descriptive adequacy.- 16.5. Achieving procedural adequacy.- 16.6. Conclusion.- 17. On Representational Aspects of VLSI-CADT Systems.- 17.1. Introduction.- 17.2. VLSI design process.- 17.2.1. Use of multiple perspectives.- 17.2.2. Almost hierarchical design.- 17.2.3. Constraints and partial specifications.- 17.3. VLSI design knowledge.- 17.3.1. Knowledge about VLSI design.- 17.4. VLSI design representation.- 17.4.1. Representation of designed artifact.- 17.4.2. Design plan.- 17.5. Analysis, testing, and diagnosis of VLSI circuits.- 17.5.1. Reasoning with constraints.- 17.5.2. Qualitative analysis.- 17.5.3. Design for testability frames.- 17.5.4. Logic programming in VLSI design.- 17.5.5. Diagnostic reasoning.- 17.6. Natural language interfaces.- 17.7. Concluding remarks.