| Vladimir Lifschitz. Nested Abnormality Theories. To appear in Artificial Intelligence, 1995. |
....Since it only allows changes that are caused, it must assume that waiting causes the car to be stolen. So not only is the car stolen in the first waiting phase, it will always be stolen whenever you wait. Several authors have augmented the causal formalism to better address problems of this sort. Lifschitz and Rabinov [ 1988 ] for example, allow miracles, i.e. changes that cannot be explained. When their new predicate M iracle is minimized at a lower priority than Causes, they are able to conclude that a miracle occurred during either the first waiting action or during the second. Another approach, proposed by ....
....if it is performed in a particular situation one that is at time 1 in which Fred is alive and the car is not stolen. So in order to explain unexpected changes, our approach, rather than assuming the occurrence of additional actions (as in [ Morgenstern and Stein, 1988 ] or miracles (as in [ Lifschitz and Rabinov, 1988 ] instead extends the causal theory in a minimal (and admittedly peculiar) fashion. This is obviously not the right thing to do. There is no reason, however, why the improvements to causal minimization cannot also be integrated with the current approach. 7 Conclusion This paper has presented ....
Vladimir Lifschitz and Arkady Rabinov. Miracles in formal theories of action. Draft, 1988. To appear in Artificial Intelligence.
....time t 3 . The approach to default persistence proposed here does not suffer from this problem because of its insistence that every property that holds has an explanation in terms of events. Others have proposed similar solutions using deduction (Morgenstern and Stein [11] Lifschitz and Rabinov [10]) But using abduction, rather than adding (4.1) and (6.1) to the set of axioms D, they are added to the set of theorems G. This leads to the rebalancing of the sequent T X D p G via the abduction of axioms (5.1) to (5.3) to explain (4.1) as described above, and also the abduction of the following ....
....itself, and Poole ( 12] also presents an abductive framework for default reasoning. This suggests that both persistence and explanation could be done in a purely abductive framework, but this possibility needs further investigation. Morgenstern and Stein ( 11] and Lifschitz and Rabinov ([10]) tackle a similar problem to the one addressed in this paper, the former using model preference and the latter using circumscription. The relationship between the three approaches is not yet clear and warrants further study. A prototype of the system described has been implemented in Prolog. This ....
Lifschitz V. and Rabinov A., Miracles in Formal Theories of Action, Stanford University Technical Report (1988), to appear in Artificial Intelligence.
....developed by McCarthy [1986] involves the use of an abnormality predicate and the application of circumscription to minimize its extent. McCarthy explored several possible strategies to perform the circumscription of abnormality, but none of them turned out to be completely satisfactory. Lifschitz [1995] proposed a new approach to the use of circumscription for representing knowledge. This new framework is called Nested Abnormality Theories (NATs) The main feature of this formalization is that it may have a nested structure. Each level of nesting gives a block that corresponds to one ....
....time, we get a stronger formula: CIRC[A;P ; Q] j 8x:Q(x) 8x:P(x) This is because any model of 8x [Q(x) oe P(x ) in which the extent of P is not empty can be improved by making both P and Q empty. 2. 3 Nested Abnormality Theories (NATs) Nested Abnormality Theories (NATs) were proposed by Lifschitz [1995] as a new approach to the use of circumscription for representing knowledge. They are similar to simple abnormality theories introduced by McCarthy [1986] except that their axioms may have a nested structure, with each level corresponding to another application of the circumscription operator. ....
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Vladimir Lifschitz. Nested Abnormality Theories. To appear in Artificial Intelligence, 1995.
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