On the Relationship between Parsimonious Covering and Boolean Minimization

Minimization of Boolean switching functions is a basic problem in the design of logic circuits. The designer first comes up with a switching function expressed in terms of several binary input variables that satisfies the desired functionality, and then attempts to minimize the function as a sum of products or product of sums. It turns out that a sum of products form of a switching function that has no redundancy is a union of prime implicants of the function. In this paper we would like to explicate some of the relationships of the boolean minimization problem to a formalization of abductive inference called parsimonious covering. Abductive inference often occurs in diagnostic problems such as finding the causes of circuit faults [Reiter, 87] or determining the diseases causing the symptoms reported by a patient [Peng and Reggia, 90]. Parsimonious covering involves covering all observed facts by means of a parsimonious set of explanations that can account for the observations. The relationship of parsimonious covering to boolean minimization has been noted by the developers of the theory; we intend to pursue a detailed mapping here

C++ \u3e C + OOP (A Personal View of Teaching Introductory Programming Using C/C++)

Many Computer Science departments are moving toward C or C++ as the programming language used in teaching the introductory computer science sequence. Some departments and many students believe that an initial introduction to C is a good idea since it allows for a smooth transition later into C ++. In this paper, some of the problems in using C as an introductory programming language are briefly reviewed, and some specific issues and solutions that are possible when C ++ is adopted are pointed out. It is concluded that while both C and C++ have their disadvantages as introductory languages, the latter would be the better choice between the two

Catalog of Common Bugs in C++ Programming

In this paper, we briefly discuss the pedagogical issues surrounding the teaching of techniques to diagnose and correct programming errors. Then we catalog several common bugs students grapple with during the course of their programming projects. We found it very valuable to document them so students can help themselves, as well as be helped by the instructor

Logical Form Generation as Abduction - Part I. Representation of Linguistic Concepts

For some time, researchers have become increasingly aware that some aspects of natural language processing can be viewed as adductive inference. This article describes knowledge representation in dual-route parsimonious covering theory, based on an existing diagnostic adductive inference model, extended to address issues specific to logic form generation. The two routes of covering deal with syntactic and semantic aspects of language, and are integrated by attributing both syntactic and semantic facets to each “open class” concept. Such extensions reflect some fundamental differences between the two task domains. The syntactic aspect of covering is described to show the differences, and some interesting properties are established. The semantic associations are characterized in terms of how they can be used in an adductive model. A major significance of this work is that it paves the way for a nondeductive inference method for word sense disambiguation and logical form generation, exploiting the associative linguistic knowledge. This approach sharply contrasts with others, where knowledge has usually been laboriously encoded into pattern-action rules that are hard to modify. Further, this work represents yet another application for the general principle of parsimonious covering. [ABSTRACT FROM AUTHOR

Logical Form Generation as Abduction - Part II. A Dual-Route Parsimonious Covering Approach

Abductive inferences are commonplace during natural language processing. Having identified some limitations of an existing parsimonious covering model of adductive diagnostic inference, we developed an extended, dual-route version to address issues in word sense disambiguation and logical form generation. The details of representing knowledge in this framework and the syntactic route of covering are described in a companion article [V. Dasigi, Int. J. Intell. Syst., 9, 571-608 (1994)]. Here, we describe the semantic covering process in detail. A dual-route algorithm that integrates syntactic and semantic covering is given. Taking advantage of the “transitivity” of irredundant syntactic covering, plausible semantic covers are searched for, based on some heuristics, in the space of irredundant syntactic covers. Syntactic covering identifies all possible candidate.; for semantic covering, which in turn, helps focus syntactic covering. Attributing both syntactic and semantic facets to “open-class” linguistic concepts makes this integration possible. An experimental prototype has been developed to provide a proof of- concept for these ideas in the context of expert system interfaces. The prototype has at least some ability to handle ungrammatical sentences, to perform some nonmonotonic inferences, etc. We believe this work provides a starting point for a nondeductive inference method for logical form generation, exploiting the associative linguistic knowledge. [ABSTRACT FROM AUTHOR

PARSING = PARSIMONIOUS COVERING? (Abduction in Logical Form Generation) * Abstract

Many researchers believe that certain aspects of natural language processing, such as word sense disambiguation and plan recognition in stories, constitute abductive inferences. We have been working with a specific model of abduction, called parsimonious covering, applied in diagnostic problem solving, word sense disambiguation and logical form generation in some restricted settings. Diagnostic parsimonious covering has been extended into a dualroute model to account for syntactic and semantic aspects of natural language. The two routes of covering are integrated by defining "open class " linguistic concepts, aiding each other. The diagnostic model has dealt with sets, while the extended version, where syntactic considerations dictate word order, deals with sequences of linguistic concepts. Here we briefly describe the original model and the extended version, and briefly characterize the notions of covering and different criteria of parsimony. Finally we examine the question of whether parsimonious covering can serve as a general framework for parsing.

Practical Program Outcomes Assessment - A Case Study

Conference proceeding from the International Conference on Engineering Education, Puerto Rico, July 2006, pp. T1A1-T1A5. In this paper, we characterize assessment in terms of outcomes that indicate that the stated objectives are met, and assessment methods that measure outcomes at desired levels of performance. We consider factors such as the need for some direct measures, issues of faculty buyin, the extra cost of formal assessment, etc. We identify two assessment methods, neither of which is new, but brought together, make it practical to assess many common outcomes. The Faculty Course Assessment Report (FCAR), developed by John Estell, provides for directly assessing course outcomes, which are mapped to program outcomes. The second direct assessment method involves using an Industry Advisory Board (IAB) in evaluating capstone courses. Here, many program outcomes, including students’ employability, are assessed. We discuss why these methods are practical and desirable. We describe some implementation considerations, as well as supplementary methods. Finally, we consider the need for an integrated approach to assessment

On the Relationship between Parsimonious Covering and Boolean Minimization

Minimization of Boolean switching functions is a basic problem in the design of logic circuits. The designer first comes up with a switching function expressed in terms of several binary input variables that satisfies the desired functionality, and then attempts to minimize the function as a sum of products or product of sums. It turns out that a sum of products form of a switching function that has no redundancy is a union of prime implicants of the function. In this paper we would like to explicate some of the relationships of the boolean minimization problem to a formalization of abductive inference called parsimonious covering. Abductive inference often occurs in diagnostic problems such as finding the causes of circuit faults [Reiter, 87] or determining the diseases causing the symptoms reported by a patient [Peng and Reggia, 90]. Parsimonious covering involves covering all observed facts by means of a parsimonious set of explanations that can account for the observations. The relationship of parsimonious covering to boolean minimization has been noted by the developers of the theory; we intend to pursue a detailed mapping here

Parsimonious Covering as a Method for Natural Language Interfaces to Expert Systems

Abductive inference has been characterized in the AI literature as ‘inference to the best explanation’ or as ‘plausible inference involving context-sensitive discrimination among explanatory hypotheses’. Analogously, understanding natural language involves context-sensitive discrimination among word senses, and there has been a growing awareness that it can be viewed as a type of abductive inference. Parsimonious covering theory, first formulated to model the abductive inference underlying medical diagnostic problem solving, is examined here as a method for automating natural language processing for medical expert system interfaces. The nature of ‘parsimony’ in natural language processing and the relationship of parsimonious covering to a notion of focus of attention are discussed. An experimental prototype developed to test these ideas in the context of a medical expert system is briefly described. This prototype is domain-independent in the same sense that a generic expert system shell is domain-independent. Given a knowledge base for a specific medical application, a vocabulary extractor extracts and indexes the linguistic information which it contains. In addition, an indexed domain-independent knowledge base that contains linguistic knowledge common to many domains is used. With a parsimonious covering inference mechanism superimposed on this knowledge, a natural language interface is generated for the specific application defined by the knowledge base