This book is based on the doctoral dissertation of R. Al-Asady, completed at Exeter University, in 1993. It is concerned with creating an inheritance theroy from the AI point of view. Within Artificial Intelligence, the need to create sophisticated intelligent behavior based on commonsense reasoning has long been recognized. Such commonsense reasoning is characterized by the withdrawing of previously drawn conclusions when new information comes along. Research has demonstrated theft formalisms for dealing with commonsense reasoning require nonmonotonic capabilities where, typically, inferences based on incomplete knowledge need to be revised in the light of later information that fills in some of the gaps. In this book, an inheritance theory based on multiple inheritance structures with exceptions (nonmonotonic inheritance structures) is proposed. Without an adequate nonmonotonic inheritance reasoning technique, such as exceptional inheritance reasoning (EIR) as proposed in this volume, inheritance networks will produce inconsistencies. A number of nonmonotonic properties that enable EIR to subsume existing formalisms such as default logic and inferential distance ordering have been included within the reasoning technique presented here. An inheritance algorithm is also presented and a demonstration is included to show how it can be used to specify and implement various nonmonotonic inheritance problems. In addition, an inheritance formalism has been developed that is capable of dealing with ambiguous situations and can handle other classes of nonmonotonic problems apart from those already presented in the literature.
Finally, in the application section, this inheritance formalism has been applied to two important domains, namely causal reasoning and analogical reasoning, to demonstrate the conceptual power and expressiveness of the formalism.
Author(s): R. Al-Asady
Publisher: Ablex Publishing Corporation
Year: 1995
Language: English
Pages: 204
Contents
1 Introduction
1.1 What is Intelligence?
1.2 Hierarchical Organization of Knowledge
1.2.1 Representation of Knowledge (Facts) Using Logic
1.2.2 Representation of Knowledge Using Semantic Networks
1.3 Inheritance
2 Inheritance Hierarchies
2.1 AI, Knowledge Representation and Inheritance
2.2 Inheritance Hierarchy Components Represented as Semantic Networks
2.3 Inheritance Hierarchical Structures
2.4 Exceptions
2.4.1 Redundant Links
2.4.2 Ambiguity
2.5 Mechanisms with Inheritance Structures
2.5.1 Directions of PathBased Reasoning
2.6 Inheritance Formula As A Representation Language
3 Current Approaches to Nonmonotonic Reasoning
3.1 Introduction
3.2 PathBased Review Literature
3.3 LogicBased Approach
3.3.1 Monotonic Logic View
3.3.2 Nonmonotonic logic view
3.3.3 Modal Nonmonotonic Logic
3.3.4 Autoepistemic Logic
3.3.5 Default logic
3.3.6 Circumscription
3.3.7 Conditional logic
3.3.8 Probabilistic reasoning
3.3.9 Other LogicalBased Review Literature
3.4 LatticeBased Approach
4 The Problem: A Clash of Intuitions
4.1 Introduction
4.2 Summary
5 EIR: An ExceptionBased Approach to Nonmonotonic Reasoning
5.1 Introduction
5.2 Exceptional Inheritance Reasoning
5.2.1 Typical and Exceptional Classes
5.2.2 Acquired and Inheritable Properties
5.3 The Exceptional Class,
5.4 Conceptual Foundations of EIR
5.5 A Semiformal Introduction to EIR
5.6 EIR Algorithm
5.7 Examples
5.7.1 The Royal. Elephant Problem
5.7.2 Clyde, the Three. Legged. Thing
5.7.3 The TweetyPenguin Problem
5.7.4 The Unicorn Problem
5.8 The George Problem
5.8.1 OnPath (or Acquired Properties) Versus OffPath Revisited
5.9 The Generalization of EIR
5.10 Related Works
5.11 Conclusion
6 Default Correlation: An Approach to Inheritance With Conflict
6.1 Introduction
6.2 Inheritance
6.2.1 Ambiguity
6.2.2 Related Works
6.3 Ambiguity Revisited
6.3.1 Default Correlation Framework
6.3.2 Ambiguity Revisited with DC
6.4 Default Correlation Framework Algorithm
6.5 A Formal Description of the Representation Language
6.5.1 Example
6.6 Conclusion
7 Application: Causal Reasoning and EIR
7.1 Introduction
7.2 Causal Reasoning: Artificial Intelligence Issues
7.3 Relation Between Inheritance Structures and Causal Structures
7.4 Causality and EIR
7.4.1 Nonmonotonic Causation: EIR Explanation for Causal Structure
7.4.2 A Semiformal Introduction to InheritanceCausal Relation
7.5 Exceptional InheritanceCausal Algorithm
7.6 ScriptStory Understanding
8 Application: Analogical Reasoning and EIR
8.1 Introduction
8.2 The Role of Analogical Reasoning in AI
8.3 Reviewed Literature
8.4 Analogical Reasoning Revisited
8.5 Analogical Inheritance Reasoning
8.6 EIR and Analogical Reasoning
9 Conclusion
9.1 What has been Achieved
9.2 Outstanding problems
