0000000001157375
AUTHOR
Sanjay Jain
Parsimony hierarchies for inductive inference
AbstractFreivalds defined an acceptable programming system independent criterion for learning programs for functions in which the final programs were required to be both correct and “nearly” minimal size. i.e.. within a computable function of being purely minimal size. Kinber showed that this parsimony requirement on final programs limits learning power. However, in scientific inference, parsimony is considered highly desirable. Alim-computable functionis (by definition) one calculable by a total procedure allowed to change its mind finitely many times about its output. Investigated is the possibility of assuaging somewhat the limitation on learning power resulting from requiring parsimonio…
Kolmogorov numberings and minimal identification
Identification of programs for computable functions from their graphs by algorithmic devices is a well studied problem in learning theory. Freivalds and Chen consider identification of ‘minimal’ and ‘nearly minimal’ programs for functions from their graphs. To address certain problems in minimal identification for Godel numberings, Freivalds later considered minimal identification in Kolmogorov Numberings. Kolmogorov numberings are in some sense optimal numberings and have some nice properties. We prove certain hierarchy results for minimal identification in every Kolmogorov numbering. In addition we also compare minimal identification in Godel numbering versus minimal identification in Kol…
Issues in synthetic data generation for advanced manufacturing
To have any chance of application in real world, advanced manufacturing research in data analytics needs to explore and prove itself with real-world manufacturing data. Limited access to real-world data largely contrasts with the need for data of varied types and larger quantity for research. Use of virtual data is a promising approach to make up for the lack of access. This paper explores the issues, identifies challenges, and suggests requirements and desirable features in the generation of virtual data. These issues, requirements, and features can be used by researchers to build virtual data generators and gain experience that will provide data to data scientists while avoiding known or …
Ordinal mind change complexity of language identification
The approach of ordinal mind change complexity, introduced by Freivalds and Smith, uses constructive ordinals to bound the number of mind changes made by a learning machine. This approach provides a measure of the extent to which a learning machine has to keep revising its estimate of the number of mind changes it will make before converging to a correct hypothesis for languages in the class being learned. Recently, this measure, which also suggests the difficulty of learning a class of languages, has been used to analyze the learnability of rich classes of languages. Jain and Sharma have shown that the ordinal mind change complexity for identification from positive data of languages formed…
Too Little, Too Early: Introduction Timing and New Product Performance in the Personal Digital Assistant Industry
The authors address the following key questions: (1) When should a firm introduce a new product? (2) What should its performance level be? and (3) How do the decisions of a competing firm affect a firm's timing and product performance decisions? The authors present a detailed case study of the initial competitors in the personal digital assistant (PDA) industry on the basis of which they construct a stylized game-theoretic model of entry timing and product performance level decisions in a duopoly. Situations in which the duopolists are symmetric as well as asymmetric in terms of their estimates of market size and product development capabilities are considered. When firms are symmetric, th…
TOWARDS SMART MANUFACTURING WITH VIRTUAL FACTORY AND DATA ANALYTICS
International audience; Virtual factory models can help improve manufacturing decision making when augmented with data analytics applications. Virtual factory models provide the capability of simulating real factories and generating realistic data streams at the desired level of resolution. Deeper insights can be gained and underlying relationships quantified by channeling the simulation output data to an external analytics tool. This paper describes integration of a virtual factory prototype with a neural network analytics application. The combined capability is used to create a neural network capable of predicting the expected cycle times for a small job shop. The capability can adapt by …
Kolmogorov numberings and minimal identification
Abstract Identification of programs for computable functions from their graphs by algorithmic devices is a well studied problem in learning theory. Freivalds and Chen consider identification of ‘minimal’ and ‘nearly minimal’ programs for functions from their graphs. To address certain problems in minimal identification for Godel numberings, Freivalds later considered minimal identification in Kolmogorov numberings. Kolmogorov numberings are in some sense optimal numberings and have some nice properties. We prove certain separation results for minimal identification in every Kolmogorov numbering. In addition we also compare minimal identification in Godel numberings versus minimal identifica…
Towards a virtual factory prototype
International audience; A virtual factory should represent most of the features and operations of the corresponding real factory. Some of the key features of the virtual factory include the ability to assess performance at multiple resolutions and generate analytics data similar to what is possible in a real factory. One should be able to look at the overall factory performance and be able to drill down to a machine and analyze its performance. It will require a large amount of effort and expertise to build such a virtual factory. This paper describes an effort to build a multiple resolution model of a manufacturing cell. The model provides the ability to study the performance at the cell l…