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8. 5 Summary In this chapter we have identified three basic patterns of influences that lead to ambiguity in the QP analysis of the basic active furnace state. We have then shown how modification of these patterns, by adding equilibrium values and sensitivity annotations on influence arcs, could permit resolu tion of the ambiguities. Finally, we have described in detail the extensions needed to the basic influence resolution algorithm in QP theory to oper ate on these extended descriptions. We have also shown that the modified influence resolution algorithm corrects an error in Forbus' original method for combining influences. We have then presented an extended example in which introduction of equilibrium assumptions eliminates all ambigu ity in the influence resolution deduction. In the next chapter we extend these techniques further, by developing a qualitative perturbation analysis technique that permits us to answer "what ir' control questions; then we extend this technique to obtain quantitative, as well as qualitative, effects of hypothetical control actions. 8.
Sommario
1 Overview.- 2 Fuzzy Logic Control.- 2.1 Classical Control Theory.- 2.2 A New Approach to Control of Complex Systems.- 2.3 Fuzzy Control.- 2.4 Extensions of the Fuzzy Control Paradigm.- 2.5 The Next Step: Fuzzy-Model-Based Control.- 3 Introduction to Qualitative Process Theory.- 3.1 Uses of QP Theory.- 3.2 Process versus Device-Centered Theories.- 3.3 Qualitative Process Theory - Definitions and Examples.- 3.4 Reasoning in QP Theory.- 3.5 Historical Background.- 3.6 Summary.- 4 Application of QP Theory to Process Control - An Example.- 4.1 The Reaction Process.- 4.2 Countercurrent Heat Flow.- 4.3 Basic Deductions.- 5 Ambiguity in QP Theory.- 5.1 Ambiguity in QP Theory.- 5.2 Representation Alternatives.- 5.3 Related Work.- 5.4 Linguistic Extensions to QP Descriptions.- 6 Linguistic Variables.- 6.1 Introduction to Linguistic Variables.- 6.2 Approximate Reasoning and the Compositional Rule of Inference.- 6.3 Similarity of Linguistic Variables and Relations.- 6.4 Support-Pair Certainty.- 6.5 Truth Maintenance with Numeric Certainty Estimates.- 6.6 Summary.- 7 Linguistic Quantity Spaces.- 7.1 A Functional Overview.- 7.2 Computing the Consequences of a Linguistic Quantity Space.- 7.3 Linguistic Measurement Interpretation.- 8 Characterization of Functional Relationships.- 8.1 Sources of Ambiguity in QP Theory Models.- 8.2 Fuzzy Relational Algorithms.- 8.3 Annotation Management.- 8.4 Examples.- 8.5 Summary.- 9 Qualitative Perturbation Analysis.- 9.1 Qualitative Perturbation Analysis.- 9.2 Extended Perturbation Analysis.- 9.3 Linguistic Perturbation Analysis.- 9.4 LPA Extended Example.- 9.5 Summary.- 10 Evaluation and Conclusion.- 10.1 Review.- 10.2 Evaluation.- 10.3 Further Research.- 10.4 Conclusion.- 10.5 References.
Riassunto
Finally, we have described in detail the extensions needed to the basic influence resolution algorithm in QP theory to oper ate on these extended descriptions. In the next chapter we extend these techniques further, by developing a qualitative perturbation analysis technique that permits us to answer "what ir' control questions;
