Rules having rare exceptions may be interpreted as assertions of high conditional probability. In other words, a rule If X then Y may be interpreted as meaning that Pr(Y∣X) 1. A general approach to reasoning with such rules, based on second-order probability, is advocated. Within this general approach, different reasoning methods are needed, with the selection of a specific method being dependent upon what knowledge is available about the relative sizes, across rules, of upper bounds on each rule's exception probabilities Pr(¬Y∣X). A method of reasoning, entailment with universal near surety, is formulated for the case when no information is available concerning the relative sizes of upper bounds on exception probabilities. Any conclusion attained under these conditions is robust in the sense that it will still be attained if information about the relative sizes of exception probability bounds becomes available. It is shown that reasoning via entailment with universal near surety is equivalent to reasoning in a particular type of argumentation system having the property that when two subsets of the rule base conflict with each other, the effectively more specific subset overrides the other. As stepping stones toward attaining this argumentation result, theorems are proved characterizing entailment with universal near surety in terms of upper envelopes of probability measures, upper envelopes of possibility measures, and directed graphs. In addition, various attributes of entailment with universal near surety, including property inheritance, are examined.
Similar content being viewed by others
References
E.W. Adams, Probability and the logic of conditionals, in: Aspects of Inductive Logic, eds. J. Hintikka and P. Suppes (North Holland Publishing, 1966) pp. 265–316.
E.W. Adams, The Logic of Conditionals (Reidel, 1975).
E.W. Adams, On the logic of high probability, Journal of Philosophical Logic 15 (1986) 255–279.
E.W. Adams, Four probability-preserving properties of inferences, Journal of Philosophical Logic 25 (1996) 1–24.
J. Aitchison, The Statistical Analysis of Compositional Data (Chapman and Hall, 1986).
F. Bacchus, A.J. Grove, J.Y. Halpern and D. Koller, From statistical knowledge bases to degrees of belief, Artificial Intelligence 87 (1996) 75–143.
D. Bamber, Probabilistic entailment of conditionals by conditionals, IEEE Transactions on Systems, Man, and Cybernetics 24 (1994) 1714–1723.
D. Bamber, How Probability theory can help us design rule-based systems, in: Proceedings of the 1998 Command & Control Research & Technology Symposium (Naval Postgraduate School, Monterey, California, June 29–July 1, 1998) pp. 441–451.
D. Bamber, Entailment with near surety of scaled assertions of high conditional probability, Journal of Philosophical Logic 29 (2000) 1–74.
D. Bamber and I.R. Goodman, New uses of second order probability techniques in estimating critical probabilities in command & control decision-making, in: Proceedings of the 2000 Command & Control Research & Technology Symposium (Naval Postgraduate School, Monterey, California, June 26–28, 2000).
D. Bamber and I.R. Goodman, Reasoning with assertions of high conditional probability: Entailment with universal near surety, in: Proceedings of the Second International Symposium on Imprecise Probabilities and Their Applications (ISIPTA '01), eds. G. De Cooman, T. L. Fine, and T. Seidenfeld (Cornell University, Ithaca, New York, 26–29 June 2001), pp. 17–26.
D. Bamber, I.R. Goodman and H.T. Nguyen, Extension of the concept of propositional deduction from classical logic to probability: An overview of probability selection approaches, Information Sciences 131 (2001) 195–250.
D. Bamber, I.R. Goodman and H.T. Nguyen, Deduction from conditional knowledge, Soft Computing 8 (2004) 247–255.
J. Bang-Jensen and G. Gutin Digraphs: Theory, Algorithms and Applications (Springer, 2000).
S. Benferhat, D. Dubois, and H. Prade, Nonmonotonic reasoning, conditional objects and possibility theory, Artificial Intelligence 92 (1997) 259–276.
S. Benferhat, D. Dubois and H. Prade, Possibilistic and standard probabilistic semantics of conditional knowledge bases, Journal of Logic and Computation 9 (1999) 873–895.
S. Benferhat, A. Saffiotti and P. Smets, Belief functions and default reasoning, Artificial Intelligence 122(2000) 1–69.
V. Biazzo, A. Gilio and T. Lukasiewicz, Probabilistic logic under coherence, model-theoretic probabilistic logic, and default reasoning in System P, Journal of Applied Non-Classical Logics 12 (2002) 189–213.
R.A. Bourne and S. Parsons, Maximum entropy and variable strength defaults, in: Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI–99), Vol. 1 (Stockholm, July 31–August 6, (1999) pp. 50–55).
P.G. Calabrese, An algebraic synthesis of the foundations of logic and probability, Information Sciences 42 (1987) 187–237.
D.V. Carbogim, D. Robertson and J. Lee, Argument-based applications to knowledge engineering, Knowledge Engineering Review 15(2) (2000) 119–149.
F. Cozman, Introduction to the Theory of Sets of Probabilities, <http://www.cs.cmu.edu/∼qbayes/Tutorial/>.
J. P. Delgrande, A first-order conditional logic for prototypical properties, Artificial Intelligence 33 (1987) 105–130.
A. Gilio, Probabilistic reasoning under coherence in System P, Annals of Mathematics and Artificial Intelligence 34(1–3) (2002) 5–34.
M. Goldszmidt, P. Morris and J. Pearl, A maximum entropy approach to nonmonotonic reasoning, IEEE Transactions on Pattern Analysis and Machine Intelligence, 15 (1993) 220–232.
M. Goldszmidt and J. Pearl, Qualitative probabilities for default reasoning, belief revision, and causal modeling, Artificial Intelligence 84 (1996) 57–112.
I.R. Goodman, A Decision Aid for Nodes in Command & Control Systems Based on Cognitive Probability Logic, in: Proceedings of the 1999 Command & Control Research & Technology Symposium (U.S. Naval War College, Newport, Rhode Island, June 29–July 1, 1999) pp. 898–941.
I.R. Goodman, D. Bamber and H.T. Nguyen, New relations between Adams-Calabrese and product space conditional event algebras, with applications to second order Bayesian inference, in: Proceedings of Workshop on Conditionals, Information, and Inference, eds. G. Kern-Isberner and W. Rödder (Hagen, Germany, May 13–15, 2002) pp. 149–163.
I.R. Goodman and H.T. Nguyen, Mathematical foundations of conditionals and their probability assignments, International Journal of Uncertainty, Fuzziness, and Knowledge-Based Systems 3(3) (1995) 247–339.
I.R. Goodman and H.T. Nguyen, Adams' high probability deduction and combination of information in the context of product probability conditional event algebra, in: Proceedings of the International Conference on Multisource-Multisensor Information Fusion (FUSION-98), Vol. 1, (Las Vegas, Nevada, July 6–9, 1998) pp. 1–8.
I.R. Goodman and H.T. Nguyen, Computational aspects of quantitative second order probability logic and fuzzy if–then rules: Part 1, Basic representations as integrals, in: Proceedings of the Fifth Joint Conference on Information Sciences, Vol.1 (Atlantic City, New Jersey, February 27–March 3, 2000) pp. 64–67.
T. Hailperin, Sentential Probability Logic: Origins, Development, Current Status, and Technical Applications (Associated University Presses, 1996).
J.Y. Halpern and R. Pucella, A logic for reasoning about upper probabilities, Journal of Artificial Intelligence Research 17 (2002) 57–81.
L.C. Hill and J.B. Paris, When maximizing entropy gives the rational closure, Journal of Logic and Computation 13 (2003) 51–68.
E.T. Jaynes, On the rationale of maximum-entropy methods, Proceedings of the IEEE 70 (1982) 939–952.
S. Kraus, D. Lehmann and M. Magidor, Nonmonotonic reasoning, preferential models and cumulative logics, Artificial Intelligence 44 (1990) 167–207.
H.E. Kyburg Jr, Combinatorial semantics: Semantics for frequent validity, Computational Intelligence 13 (1997) 215–257.
D. Lehmann, Another perspective on default reasoning, Annals of Mathematics and Artificial Intelligence 15 (1995) 61–82.
D. Lehmann and M. Magidor, What does a conditional knowledge base entail? Artificial Intelligence 55 (1992) 1–60.
D.V. Lindley. Bayesian Statistics, A Review (Society for Industrial and Applied Mathematics, 1971).
T. Lukasiewicz, Probabilistic deduction with conditional constraints over basic events, Journal of Artificial Intelligence Research 10 (1999) 199–241.
T. Lukasiewicz, Probabilistic logic programming under inheritance with overriding, in: Proceedings of the 17th Conference on Uncertainty in Artificial Intelligence (UAI-2001) (Morgan Kaufmann, Seattle, Washington, August 2001) pp. 329–336.
T. Lukasiewicz, Probabilistic default reasoning with conditional constraints, Annals of Mathematics and Artificial Intelligence 34(1–3) (2002) 35–88.
J. McCarthy, Circumscription – A form of non-monotonic reasoning, Artificial Intelligence 13 (1980) 27–39.
V. McGee, Learning the impossible, in: Probability and Conditionals: Belief Revision and Rational Decision, eds. E. Eells and B. Skyrms (Cambridge University Press, Cambridge, 1994) pp. 179–199.
R.C. Moore, Semantical considerations on nonmonotonic logic, Artificial Intelligence 25 (1985) 75–94.
N.J. Nilsson, Probabilistic logic, Artificial Intelligence 28 (1986) 71–87.
J.B. Paris, The Uncertain Reasoner's Companion (Cambridge University Press, Cambridge, 1994).
J. Paris, Common sense and maximum entropy, Synthese 117 (1999) 75–93.
K. Parsaye and M. Chignell, Expert Systems for Experts (Wiley, New York, 1988).
J. Pearl, System Z: A natural ordering of defaults with tractable applications to nonmonotonic reasoning, in: Theoretical Aspects of Reasoning about Knowledge. Proceedings of the Third Conference (TARK 1990). R. Parikh ed., Morgan Kaufmann, (1990) pp. 121–135.
K. Popper, The Logic of Scientific Discovery, 2nd edition (Basic Books, New York, 1959).
H. Prakken and G. Vreeswijk, Logics for defeasible argumentation, in: Handbook of Philosophical Logic, 2nd edition, eds. D. Gabbay and F. Guenthener, Vol. 4 (Kluwer Academic, 2002) pp. 218–319.
R. Reiter, A logic for default reasoning, Artificial Intelligence 13 (1980) 81–132.
G.R. Simari and R.P. Loui, A mathematical treatment of defeasible reasoning and its implementation, Artificial Intelligence 53 (1992) 125–157.
G. Schurz, Probabilistic justification of default reasoning, in: KI-94: Advances in Artificial Intelligence. Proceedings of the German Annual Conference on Artificial Intelligence, eds. B. Knebel and L.D. Dreschler-Fischer, (Springer, 1994) pp. 248–259.
G. Schurz, Probabilistic default logic based on irrelevance and relevance assumptions. in: Qualitative and Quantitative Practical Reasoning, eds. D. Gabbay, et al. Lecture Notes in Artificial Intelligence, Vol. 1244 (Springer, 1997), pp. 536–553.
G. Schurz, Probabilistic semantics for Delgrande's conditional logic and a counterexample to his default logic, Artificial Intelligence 102 (1998) 81–95.
P. Snow, Diverse confidence levels in a probabilistic semantics for conditional logics, Artificial Intelligence 112 (1999) 269–279.
P. Walley, Statistical Reasoning with Imprecise Probabilities (Chapman and Hall, 1991).
P.H. Winston, Artificial Intelligence, 2nd Edition (Addison-Wesley, Reading, MA, 1984).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Bamber, D., Goodman, I.R. & Nguyen, H.T. Robust reasoning with rules that have exceptions: From second-order probability to argumentation via upper envelopes of probability and possibility plus directed graphs. Ann Math Artif Intell 45, 83–171 (2005). https://doi.org/10.1007/s10472-005-9008-8
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10472-005-9008-8
Keywords
- conditional probability
- second-order probability
- Bayesian inference
- reasoning under uncertainty
- non-monotonic logic
- default reasoning
- rule-based system
- threshold knowledge
- informant
- robustness
- directed graph
- argumentation system
- property inheritance