Salience or centrality: Why do some features influence inductive generalization more than others?

2019 ◽  
Vol 55 (3) ◽  
pp. 612-622 ◽  
Author(s):  
Nicholaus S. Noles
Keyword(s):  
Inductive Generalization

scholarly journals Sample selection and inductive generalization

2009 ◽  
Vol 37 (5) ◽  
pp. 596-607 ◽  
Author(s):  
Chris A. Lawson ◽  
Charles W. Kalish
Keyword(s):  
Sample Selection ◽  
Inductive Generalization

scholarly journals Sampling Assumptions in Inductive Generalization

2011 ◽  
Vol 36 (2) ◽  
pp. 187-223 ◽  
Author(s):  
Daniel J. Navarro ◽  
Matthew J. Dry ◽  
Michael D. Lee
Keyword(s):  
Inductive Generalization

First Wave Feminism: Craftswomen in Plato’s Republic

Apeiron ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Emily Hulme
Keyword(s):  
Inductive Generalization ◽  
Historical Reality ◽  
Circular Argument ◽  
The One ◽  
Food Sales ◽  
The Guardian

Abstract Ancient Athenian women worked in industries ranging from woolworking and food sales to metalworking and medicine; Socrates’ mother was a midwife. The argument for the inclusion of women in the guardian class must be read in light of this historical reality, not least because it allows us retain an important manuscript reading and construe the passage as relying on an inductive generalization rather than a possibly circular argument. Ultimately, Plato fails to fully capitalize on the resources he has for a more egalitarian conclusion than the one he settles on, which regards women as “lesser than” yet “similar to” men.

scholarly journals Automated formal synthesis of provably safe digital controllers for continuous plants

2019 ◽  
Vol 57 (1-2) ◽  
pp. 223-244 ◽  
Author(s):  
Alessandro Abate ◽  
Iury Bessa ◽  
Lucas Cordeiro ◽  
Cristina David ◽  
Pascal Kesseli ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Bounded Model Checking ◽  
Digital Controllers ◽  
Inductive Generalization ◽  
Physical Plant ◽  
Continuous State Space ◽  
Continuous State ◽  
Linear Differential ◽  
Time Invariant ◽  
Physical Plants

Abstract We present a sound and automated approach to synthesizing safe, digital controllers for physical plants represented as time-invariant models. Models are linear differential equations with inputs, evolving over a continuous state space. The synthesis precisely accounts for the effects of finite-precision arithmetic introduced by the controller. The approach uses counterexample-guided inductive synthesis: an inductive generalization phase produces a controller that is known to stabilize the model but that may not be safe for all initial conditions of the model. Safety is then verified via bounded model checking: if the verification step fails, a counterexample is provided to the inductive generalization, and the process further iterates until a safe controller is obtained. We demonstrate the practical value of this approach by automatically synthesizing safe controllers for physical plant models from the digital control literature.

A simple network capable of inductive generalization

1972 ◽  
Vol 182 (1067) ◽  
pp. 233-247 ◽  
Keyword(s):  
Binary Vector ◽  
Difficult Problem ◽  
Threshold Logic ◽  
Inductive Generalization ◽  
Fixed Length ◽  
Binary Feature ◽  
Content Addressable Memories ◽  
Simple Network

Various networks have been described which will function as content addressable memories; when given an incomplete description of a stored item they will supply those features of the item which are missing from the description. In this paper we consider a more difficult problem, that of designing a network which will also accept a description to which no stored item corresponds, and will supplement such a description by inductive generalization over the items already in store. This problem is tractable only if the ensemble of possible items is restricted in some way. A reasonable restriction is to assume ( i ) that any item can be fully described by a binary vector of fixed length, where the value of any component indicates the value of the corresponding binary feature; ( ii ) that no two features are logically independent of one another. Condition ( ii ) implies for example, that the ensemble does not contain items answering to all four of the descriptions + + ... , + — ..., — + ... and — .... If these conditions are satisfied, the ensemble may be represented as a tree, in which each node corresponds to a possible item and each link to the alteration of one or more features. A simple network is described, incorporating modifiable switches and threshold logic, which will supply all the inferences which could be drawn about an incompletely described item by inspecting this tree. It is possible, furthermore, to give a clear interpretation to the output of the network when the input describes an item which could not possibly belong to the same ensemble as those in store.

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