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Computer Science - Artificial Intelligence | Case-Based Approximate Reasoning

Case-Based Approximate Reasoning

H├╝llermeier, Eyke

2007, XVI, 370 p.

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Case-based reasoning (CBR) has received a great deal of attention in recent years and has established itself as a core methodology in the field of artificial intelligence. The key idea of CBR is to tackle new problems by referring to similar problems that have already been solved in the past.

Making use of different frameworks of approximate reasoning and reasoning under uncertainty, notably probabilistic and fuzzy set-based techniques, this book develops formal models of the above inference principle, which is fundamental to CBR. The case-based approximate reasoning methods thus obtained especially emphasize the heuristic nature of case-based inference and aspects of uncertainty in CBR. This way, the book contributes to a solid foundation of CBR which is grounded on formal concepts and techniques from the aforementioned fields. Besides, it establishes interesting relationships between CBR and approximate reasoning, which not only cast new light on existing methods but also enhance the development of novel approaches and hybrid systems.

Content Level » Research

Keywords » artificial intelligence - case-based reasoning - fuzzy - intelligence - knowledge engineering - knowledge-based system - knowledge-based systems - learning - modeling - probabilistic reasoning - uncertainty

Related subjects » Artificial Intelligence - Mathematics - Statistics - Theoretical Computer Science

Table of contents 

Notation.- 1. Introduction.1.1 Similarity and case-based reasoning.1.2 Objective of this book. 1.3 Overview.- 2. Similarity and Case-Based Inference. 2.1 Model-based and instance-based approaches. 2.2 Similarity-based methods. 2.4 Case-based inference. 2.5 Summary and remarks.- 3. Constraint-Based Modeling of Case-Based Inference. 3.1 Basic concepts. 3.2 Constraint-based inference. 3.3 Case-based approximation. 3.4 Learning similarity hypotheses. 3.5 Application to statistical inference. 3.6 Summary and remarks.- 4. Probabilistic Modeling of Case-Based Inference. 4.1 Basic probabilistic concepts. 4.2 Case-based inference, probabilistic reasoning, and statistical inference. 4.3 Learning probabilistic similarity hypotheses. 4.4 Experiments with regression and label ranking. 4.5 Case-based inference as evidential reasoning. 4.6 Assessment of cases. 4.7 Complex similarity hypotheses. 4.8 Approximate probabilistic inference. 4.9 Summary and remarks.- 5. Fuzzy Set-Based Modeling of Case-Based Inference I. 5.1 Background on possibility theory . 5.2 Fuzzy rule-based modeling of the CBI hypothesis. 5.3 Generalized possibilistic. 5.4 Extensions of the basic model. 5.5 Experimental studies. 5.6 Calibration of CBI models. 5.7 Relations to other fields. 5.8 Summary and remarks. 6.1 Gradual inference rules. 6.2 Certainty rules. 6.3 Cases as information sources. 6.4 Exceptionality and assessment of cases. 6.5 Local rules. 6.6 Summary and remarks.- 7. Case-Based Decision Making. 7.1 Case-based decision theory. 7.2 Nearest Neighbor decisions. 7.4 Fuzzy quantification in act evaluation. 7.5 A CBI framework of CBDM. 7.6 CBDM models: A discussion of selected issues. 7.7 Experience-based decision making. 7.8 Summary and remarks.- 8. Conclusions and Outlook A. Possibilistic Dominance in Qualitative Decisions.- References.

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