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Keywords:
uncertainty; relational knowledge
uncertainty; relational knowledge
Summary:
This paper aims to develop an analysis of how ignorance affects the reasoning activity and is related to the concept of uncertainty. With reference to a simple inferential reasoning step, involving a single piece of relational knowledge, we identify four types of ignorance and show how they give rise to different types of uncertainty. We then introduce the concept of reasoning attitude, as a basic choice about how reasoning should be carried out in presence of ignorance. We identify two general attitudes, analyze how they are related to different types of ignorance, and propose some general requirements about how they should affect the reasoning activity. A formalism for uncertain reasoning explicitly including the different types of uncertainty identified and satisfying the stated requirements is finally introduced and its performance is analyzed in simple examples.
References:
[1] Bacchus F.: Representing and Reasoning with Probabilistic Knowledge. A Logical Approach to Probabilities. MIT Press, Cambridge 1990 MR 1133623
[2] Baroni P., Guida G., Mussi S.: Modeling default reasoning through $A$–uncertainty. In: Proceedings IPMU’ 96, International Conference on Information Processing and Management of Uncertainty in Knowledge–Based Systems, Granada 1996, pp. 1197–1204
[3] Benferhat S., Saffiotti A., Smets P.: Belief functions and default reasoning. In: Proc. UAI 95 11th Conference on Uncertainty in Artificial Intelligence, Montreal 1995 MR 1785699 | Zbl 0948.68112
[4] Dubois D., Lang J., Prade H.: Automated reasoning using possibilistic logic: Semantics, belief revision, and variable certainty weights. IEEE Trans. Knowledge Data Engineering KDE–6 (1994), 1, 64–71 DOI 10.1109/69.273026 | MR 1281429
[5] Halpern J. Y.: An analysis of first–order logics of probability. Artificial Intelligence 46 (1990), 311–350 DOI 10.1016/0004-3702(90)90019-V | MR 1084887 | Zbl 0723.03007
[6] Group, Léa Sombé: Reasoning under incomplete information in artificial intelligence: A comparison of formalisms using a single example. Internat. J. Intelligent Systems 5 (1990), 4, 323–472 DOI 10.1002/int.4550050403 | MR 1094371
[7] Moses Y., Shoham Y.: Belief as defeasible knowledge. Artificial Intelligence 64 (1993), 299–321 DOI 10.1016/0004-3702(93)90107-M | MR 1259580 | Zbl 0787.68095
[8] Nilsson N. J.: Probabilistic logic. Artificial Intelligence 28 (1986), 71–87 DOI 10.1016/0004-3702(86)90031-7 | MR 0832294 | Zbl 0589.03007
[9] Pearl J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo 1991 MR 0965765 | Zbl 0746.68089
[10] Reiter R.: Nonmonotonic reasoning. Ann. Rev. Computer Science 2 (1987), 147–186 DOI 10.1146/annurev.cs.02.060187.001051 | MR 0921495
[11] Saffiotti A.: Using Dempster–Shafer theory in knowledge representation. In: Uncertainty in Artificial Intelligence 6 (P. P. Bonissone, M. Henrion, L. N. Kanal and J. F. Lemmer, eds.), Elsevier, New York 1991, pp. 417–431
[12] Saffiotti A.: A belief–function logic In: Proc. AAAI–92 10th National Conference on Artificial Intelligence, San Jose, 1992, pp. 642–647 MR 1203139
[13] Smets P.: The nature of unnormalized beliefs encountered in the transferable belief model. In: Proc. of Uncertainty in AI 92, pp. 292–297
[14] Smets P., Hsia Y. T.: Default reasoning and the transferable belief model. In: Uncertainty in Artificial Intelligence 6 (P. P. Bonissone, M. Henrion, L. N. Kanal and J. F. Lemmer, eds.). Elsevier, New York 1991, pp. 495–504
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