Dana Angluin and Carl H. Smith. Inductive inference: Theory and methods. ACM Computing Surveys, 15(3):237-269, September 1983. Home/Search Document Not in Database Summary Related Articles Check |
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....versions of this work have been applied to policy coordination in hybrid system [Lemmon 1993b] and adaptivevariable structure control [Lemmon 1993a] The interesting aspect of the proposed adaptation algorithm is that it can be shown to converge after a finite numberofupdates. Inductive inference [Angluin 1983]isamachine learning protocol in whicha system learns by example. It has found significant and practical applications in computational learning theory, adaptivecontrol, parameter estimation, combinatorial optimization, linear programming, and pattern classification. The basic structure of all ....
D. Angluin, C.H. Smith, "Inductive Inference: Theory and Methods." Computing Surveys, 15(3):237-269, September 1983.
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....discovery problem described by Cook et al. CW98] the protocol inference problem can be cast in terms of a previously investigated grammar inference problem. The grammar inference problem is to discover a grammar for a regular language provided with sample sentences or sequences in that language [AS83]. There have been various algorithms on inferring a grammar or an FSA given some positive and negative sequences. Positive sequences are those sequences that are valid whereas negative sequences are those that are invalid. Previous research has shown that it is very difficult to learn the best ....
D. Angluin and C. H. Smith. Inductive inference: Theory and methods. ACM Computing Surveys, 15(3), pp. 237269, 1983. 21
Inference of Message Sequence Charts - Alur, Etessami, Yannakakis (2002) (24 citations) (Correct)
....algorithm to detect safely implied MSCs and solve safe realizability, avoiding the state explosion which typically arises in such analysis of concurrent system behavior. It is worth noting that inferring sequential state machines from example executions is a wellstudied topic in automata theory [8, 5]. In our setting, only positive examples are given, but 2 P P 1 NA UR UR NA inc double double double P 1 1 MSC Figure 1: Two seemingly correct scenarios, updating fuel amounts the executions are partially ordered and we infer distributed implementations. 2 Sample MSC Inference We ....
D. Angluin and C. H. Smith. Inductive inference: theory and methods. ACM Computing Surveys, 15:237--269, 1983.
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....a general rule from eventually incomplete data. Within the last three decades it received much attention from computer scientists. Nowadays inductive inference can be considered as a form of machine learning with potential applications to artificial intelligence (cf. e.g. Angluin and Smith [3, 4], Osherson, Stob and Weinstein [36] For more information concerning recent developments in inductive inference, the reader is referred to the annual Workshops on Computational Learning Theory, COLT (cf. e.g. Rivest et al. 37] Fulk and Case [15] Haussler [18] and Warmuth [39] the ....
Angluin, D., and Smith, C.H.: Inductive inference: theory and methods. Computing Surveys 15 (1983), 237 -- 269.
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.... Ko86, PF97] resource bounded universal probability in [LV91, LV97, Sch02b] Implementations are rare and mainly due to Schmidhuber [Con97, Sch97, SZW97, Sch02a] Excellent reviews with a philosophical touch are [LV92b, Sol97] For an older, but general review of inductive inference see Angluin [AS83]. Sequential decision theory. The other ingredient in our AI# model is sequential decision theory. We do not need much more than the maximum expected utility principle and the expectimax algorithm [Mic66, RN95] The book of von Neumann and Morgenstern [NM44] might be seen as the initiation of game ....
D. Angluin and C. H. Smith. Inductive inference: Theory and methods. ACM Computing Surveys, 15(3):237--269, 1983.
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....so act to pre x the trees of the higher resolution attractors, as illustrated in Figure 4.2. Furthermore, a nite sampling means that each tree equivalence class takes up some nite percentage of the space. Hence, despite the well established di culty of inducing grammars in the general case [2][37] 39] This behavior is reminiscent an L System [53] a type of rewriting system which contains rules for replacing one symbol with a list of symbols drawn from the same alphabet. As formal grammars are a more common approach to the analysis of recurrent networks than are L Systems, the ....
D. Angluin and C.H. Smith. Inductive inference: Theory and methods. ACM Computing Surveys, 15(3):237269, 1983.
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....this approach offers the analyst more than just a host of methods to analyze the available information. It is also possible to perform a kind of meta analysis: there are additional methods and information that can be used to analyze the analysis techniques and their relationships with the data [1], and the use of those techniques by the analyst, in the expectation of improving future analyses. This kind of computational reflection makes a computing system much more flexible [40] 22] 28] Problem Solving Methods can be viewed as problem paradigms in our sense, and we believe that ....
Dana Angluin, Carl H. Smith, "Inductive Inference: Theory and Methods", Computing Surveys, vol. 15, no. 3, p. 237-269 (September 1983)
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....5. 3 Problems in the synthesis of state diagrams The state machine synthesis has been treated as an inductive inference problem by Koskimies and M akinen [54] Inductive inference studies algorithms for inferring rules from their application instances, usually considering functions and languages [3, 4]. Applying the BK algorithm to the state diagram synthesis, however, differs from a typical inductive inference, causing inaccuracies to the synthesized state machine. For this reason, learning results of the BK algorithm do not necessarily hold for state diagrams. The main source of problems is ....
....(except after the halt statement) but there is usually not a valid transition for every possible event in every state of a state diagram. Overgeneralization is considered the most severe problem of inductive inference from positive data, since only negative data can expose too general guesses [4]. The fact that the synthesized state diagram may generalize the given scenarios is usually exactly the desired effect, but in some cases the result is not what the user expects. An example of the overgeneralization problem and means to solve it are studied next. Consider a trace consisting of ....
Angluin D. and Smith C.H., Inductive inference: theory and methods, ACM Comput. Surv., 15, 3, 1983, pp. 237--269.
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Dana Angluin and Carl H. Smith. Inductive inference: Theory and methods. ACM Computing Surveys, 15(3):237-269, September 1983.
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D. Angluin and C. H. Smith. Inductive inference: theory and methods. ACM Computing Surveys, 15:237--269, 1983.
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D. Angluin, C. Smith, Inductive inference: theory and methods, ACM Computing Surveys 15 (3) (1983) 237--269.
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Dana Angluin and Carl H. Smith. Inductive inference: Theory and methods. ACM Computing Surveys, 15(3):237-269, September 1983.
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D. Angluin and C. H. Smith. Inductive inference: Theory and methods. ACM Computing Surveys, 15(3):237-269, 1983.
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D. Angluin and C. H. Smith, Inductive inference: theory and methods, Computing Surveys 15 (1983) 237--269.
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D. Angluin and C.H. Smith, `Inductive Inference: Theory and Methods', Computing Surveys, 15(3), 237--269, (1983).
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