Parsons S. Qualitative Methods for Reasoning under Uncertainty

Massachusetts Institute of Technology, 2001. — 510 p. — ISBN: 0-262-16168-0.In this book Simon Parsons describes qualitative methods for reasoning under uncertainty, "uncertainty" being a catch-all term for various types of imperfect information. The advantage of qualitative methods is that they do not require precise numerical information. Instead, they work with abstractions such as interval values and information about how values change. The author does not invent completely new methods for reasoning under uncertainty but provides the means to create qualitative versions of existing methods. To illustrate this, he develops qualitative versions of probability theory, possibility theory, and the Dempster-Shafer theory of evidence.
According to Parsons, these theories are best considered complementary rather than exclusive. Thus the book supports the contention that rather than search for the one best method to handle all imperfect information, one should use whichever method best fits the problem. This approach leads naturally to the use of several different methods in the solution of a single problem and to the complexity of integrating the results problem to which qualitative methods provide a solution.All about uncertainty.
Taxonomies of uncertainty.
Sources of imperfect information.
Uncertainty and entropy.
Human reasoning under uncertainty.
Ground rules for formal systems.
Quantitative methods for reasoning with imperfect information.
The main models.
Other important models.
Computational techniques.
Quantiied logics.
Qualitative methods for reasoning with imperfect information.
Qualitative physics.
Interval-based systems.
Abstractions of quantitative systems.
Defeasible reasoning.
Combining and relating formalisms.
A framework for studying diferent methods.
Eclecticism and the integration problem.
A general framework.
Examples of integration and incompleteness.
Using qualitative algebras.
An algebra with qualitative values.
An algebra of interval values.
Other qualitative algebras.
An example of handling integration.
An example of handling incompleteness.
The theory of qualitative change.
Basic concepts of qualitative change.
Causal reasoning.
Evidential reasoning.
Handling incompleteness and integration.
Further results in the theory of qualitative change.
Synergy.
Propagation in multiply-connected networks.
Intercausal reasoning.
Related work.
Implementing the qualitative approaches.
Implementing qualitative algebras.
Implementing the theory of qualitative change.
Qualitative protein topology prediction.
Protein topology prediction.
A first approach to modeling the uncertainty.
A second approach to modeling the uncertainty.
Discussion.
Summary and conclusions.
Appendix A: Proofs of theorems.
Appendix B: Conditional belief calculations.