Manoj K. Raut

Bio: Manoj K. Raut is an academic researcher from Dhirubhai Ambani Institute of Information and Communication Technology. The author has contributed to research in topics: Prime (order theory) & Modal logic. The author has an hindex of 4, co-authored 12 publications receiving 49 citations. Previous affiliations of Manoj K. Raut include VIT University.

Prime Implicates of First Order Formulas.

Abstract: This paper extends the notion of prime implicates to first order logic formulas without equality which are assumed to be in Skolem Conjunctive Normal Form. Using the extended notions of consensus and subsumption it is shown that the consensus-subsumption algorithm for computing prime implicates well known for propositional formulas can be conditionally lifted to first order formulas.

An Incremental Algorithm for Computing Prime Implicates in Modal Logic

Abstract: The algorithm to compute prime implicates and prime implicants in modal logic \({\mathcal{K}}\) has been suggested in [1]. In this paper we suggest an incremental algorithm to compute the prime implicates of a knowledge base KB and a new knowledge base F from Π(KB) ∧ F in modal logic \({\mathcal{K}}\), where Π(KB) is the set of prime implicates of KB and we also prove the correctness of the algorithm.

On octonary codes and their covering radii.

Abstract: This paper introduces new reduction and torsion codes for an octonary code and determines their basic properties. These could be useful for the classification of self-orthogonal and self dual codes over Z8. We also focus our attention on the covering radius problem of octonary codes. In particular, we determine lower and upper bounds of the covering radius of several classes of repetition codes, simplex codes of type α and type β and their duals, MacDonald codes, and Reed-Muller codes over Z8.

On Octonary Codes and their Covering Radii

Abstract: This paper introduces new reduction and torsion codes for an octonary code and determines their basic properties. These could be useful for the classification of self-orthogonal and self dual codes over $\mathbb{Z}_8$. We also focus our attention on covering radius problem of octonary codes. In particular, we determine lower and upper bounds of the covering radius of several classes of Repetition codes, Simplex codes of Type $\alpha$ and Type $\beta$ and their duals, MacDonald codes, and Reed-Muller codes over $\mathbb{Z}_8$.

Prime implicants of first order formulas via transversal clauses

Abstract: Knowledge compilation techniques are usually applied for propositional knowledge bases. In this article, an extension of the notion of prime implicants of a knowledge base using first order formulas in Skolem conjunctive normal form is suggested. The method of transversal clauses used earlier for computing prime implicants of a propositional formula in conjuctive normal form is adopted to the first order case via substitutions. Partial correctness of the algorithm is proved. The algorithm is adopted heuristically for computing approximate prime implicants.

Process SEER: A Tool for Semantic Effect Annotation of Business Process Models

Abstract: A key challenge in devising solutions to a range of problems associated with business process management: process life cycle management, compliance management, enterprise process architectures etc. is the problem of identifying process semantics. The current industry standard business process modeling notation, BPMN, provides little by way of semantic description of the effects of a process (beyond what can be conveyed via the nomenclature of tasks and the decision conditions associated with gateways). In this paper, we describe the conceptual underpinnings, design, implementation and evaluation of the ProcessSEER tool that supports several strategies for obtaining semantic effect descriptions of BPMN process models, without imposing an overly onerous burden of using formal specification on the analyst. The tool requires analysts to describe the immediate effects of each task. These are then accumulated in an automated fashion to obtain cumulative effect annotations for each task in a process. The tool leverages domain ontologies wherever they are available. The tool permits the analyst to specify immediate effect annotations in a practitioner-accessible controlled natural language, which enables formal specification using a limited repertoire of natural language sentence formats. The tool also leverages semantic web services in a similar fashion.

A Framework for Detecting Interactions Between Co-Incident Clinical Processes

Abstract: The detection of treatment conflicts between multiple treatment protocols that are co-incident is a difficult and open problem that is particularly exacerbated regarding the treatment of multiple medical conditions co-occurring in aged patients. For example, a clinical protocol for prostate cancer treatment requires the administration of androgen-suppressing medication, which may negatively interact with another, co-incident protocol if the same patient were being treated for renal disease via haemodialysis, where androgen-enhancers are frequently administered. These treatment conflicts are subtle and difficult to detect using automated means. Traditional approaches to clinical decision support would require significant clinical knowledge. In this paper, the authors present an alternative approach that relies on encoding treatment protocols via process models in BPMN and annotating these models with semantic effect descriptions, which automatically detects conflicts. This paper describes an implemented tool ProcessSEER used for semantic effect annotation of a set of 12 cancer trial protocols and depicts the machinery required to detect treatment conflicts. The authors also argue whether the semantic effect annotations of treatment protocols can be leveraged for other tasks.

Prime implicate generation in equational logic

Abstract: We present an algorithm for the generation of prime implicates in equational logic, that is, of the most general consequences of formulae containing equations and disequations between first-order terms. This algorithm is defined by a calculus that is proved to be correct and complete. We then focus on the case where the considered clause set is ground, i.e., contains no variables, and devise a specialized tree data structure that is designed to efficiently detect and delete redundant implicates. The corresponding algorithms are presented along with their termination and correctness proofs. Finally, an experimental evaluation of this prime implicate generation method is conducted in the ground case, including a comparison with state-of-the-art propositional and first-order prime implicate generation tools.

An Incremental Algorithm for Computing Prime Implicates in Modal Logic

Abstract: The algorithm to compute prime implicates and prime implicants in modal logic \({\mathcal{K}}\) has been suggested in [1]. In this paper we suggest an incremental algorithm to compute the prime implicates of a knowledge base KB and a new knowledge base F from Π(KB) ∧ F in modal logic \({\mathcal{K}}\), where Π(KB) is the set of prime implicates of KB and we also prove the correctness of the algorithm.