A granularity-based framework of deduction, induction, and abduction

AbstractIn this paper, we propose a granularity-based framework of deduction, induction, and abduction using variable precision rough set models proposed by Ziarko and measure-based semantics for modal logic proposed by Murai et al. The proposed framework is based on α-level fuzzy measure models on the basis of background knowledge, as described in the paper. In the proposed framework, deduction, induction, and abduction are characterized as reasoning processes based on typical situations about the facts and rules used in these processes. Using variable precision rough set models, we consider β-lower approximation of truth sets of nonmodal sentences as typical situations of the given facts and rules, instead of the truth sets of the sentences as correct representations of the facts and rules. Moreover, we represent deduction, induction, and abduction as relationships between typical situations

At the Biological Modeling and Simulation Frontier

We provide a rationale for and describe examples of synthetic modeling and simulation (M&S) of biological systems. We explain how synthetic methods are distinct from familiar inductive methods. Synthetic M&S is a means to better understand the mechanisms that generate normal and disease-related phenomena observed in research, and how compounds of interest interact with them to alter phenomena. An objective is to build better, working hypotheses of plausible mechanisms. A synthetic model is an extant hypothesis: execution produces an observable mechanism and phenomena. Mobile objects representing compounds carry information enabling components to distinguish between them and react accordingly when different compounds are studied simultaneously. We argue that the familiar inductive approaches contribute to the general inefficiencies being experienced by pharmaceutical R&D, and that use of synthetic approaches accelerates and improves R&D decision-making and thus the drug development process. A reason is that synthetic models encourage and facilitate abductive scientific reasoning, a primary means of knowledge creation and creative cognition. When synthetic models are executed, we observe different aspects of knowledge in action from different perspectives. These models can be tuned to reflect differences in experimental conditions and individuals, making translational research more concrete while moving us closer to personalized medicine

An automated reasoning framework for translational research

AbstractIn this paper we propose a novel approach to the design and implementation of knowledge-based decision support systems for translational research, specifically tailored to the analysis and interpretation of data from high-throughput experiments. Our approach is based on a general epistemological model of the scientific discovery process that provides a well-founded framework for integrating experimental data with preexisting knowledge and with automated inference tools.In order to demonstrate the usefulness and power of the proposed framework, we present its application to Genome-Wide Association Studies, and we use it to reproduce a portion of the initial analysis performed on the well-known WTCCC dataset. Finally, we describe a computational system we are developing, aimed at assisting translational research. The system, based on the proposed model, will be able to automatically plan and perform knowledge discovery steps, to keep track of the inferences performed, and to explain the obtained results

Interactive Knowledge Construction in the Collaborative Building of an Encyclopedia

International audienceOne of the major challenges of Applied Artificial Intelligence is to provide environments where high level human activities like learning, constructing theories or performing experiments, are enhanced by Artificial Intelligence technologies. This paper starts with the description of an ambitious project: EnCOrE2. The specific real world EnCOrE scenario, significantly representing a much wider class of potential applicative contexts, is dedicated to the building of an Encyclopedia of Organic Chemistry in the context of Virtual Communities of experts and students. Its description is followed by a brief survey of some major AI questions and propositions in relation with the problems raised by the EnCOrE project. The third part of the paper starts with some definitions of a set of “primitives” for rational actions, and then integrates them in a unified conceptual framework for the interactive construction of knowledge. To end with, we sketch out protocols aimed at guiding both the collaborative construction process and the collaborative learning process in the EnCOrE project.The current major result is the emerging conceptual model supporting interaction between human agents and AI tools integrated in Grid services within a socio-constructivist approach, consisting of cycles of deductions, inductions and abductions upon facts (the shared reality) and concepts (their subjective interpretation) submitted to negotiations, and finally converging to a socially validated consensus

Knowledge in Sound Design: The Silent Electric Vehicle—A Relevant Case Study

This article builds on a large industry-driven sound design experiment focusing on the underexplored area of sound signature for silent electric vehicles. On the basis of some retrospective observations, and in the conceptual framework of design research, we propose a post-analysis that leads to provide insights on sound design as a discipline, considering its status, the status of its performers (sound designers), and its specific position between science and arts. The main aim of the article is to contribute to increase the general knowledge on sound design and to study it from the perspective of its principles, practices, and procedures

A Framework for Theory Development in Design Science Research: Multiple Perspectives

One point of convergence in the many recent discussions on design science research in information systems (DSRIS) has been the desirability of a directive design theory (ISDT) as one of the outputs from a DSRIS project. However, the literature on theory development in DSRIS is very sparse. In this paper, we develop a framework to support theory development in DSRIS and explore its potential from multiple perspectives. The framework positions ISDT in a hierarchy of theories in IS design that includes a type of theory for describing how and why the design functions: Design-relevant explanatory/predictive theory (DREPT). DREPT formally captures the translation of general theory constructs from outside IS to the design realm. We introduce the framework from a knowledge representation perspective and then provide typological and epistemological perspectives. We begin by motivating the desirability of both directive-prescriptive theory (ISDT) and explanatory-predictive theory (DREPT) for IS design science research and practice. Since ISDT and DREPT are both, by definition, mid-range theories, we examine the notion of mid-range theory in other fields and then in the specific context of DSRIS. We position both types of theory in Gregor’s (2006) taxonomy of IS theory in our typological view of the framework. We then discuss design theory semantics from an epistemological view of the framework, relating it to an idealized design science research cycle. To demonstrate the potential of the framework for DSRIS, we use it to derive ISDT and DREPT from two published examples of DSRIS

Probabilistic logic as a unified framework for inference

I argue that a probabilistic logical language incorporates all the features of deductive, inductive, and abductive inference with the exception of how to generate hypotheses ex nihilo. In the context of abduction, it leads to the Bayes theorem for confirming hypotheses, and naturally captures the theoretical virtue of quantitative parsimony. I address common criticisms against this approach, including how to assign probabilities to sentences, the problem of the catch-all hypothesis, and the problem of auxiliary hypotheses. Finally, I make a tentative argument that mathematical deduction fits in the same probabilistic framework as a deterministic limiting case

Systemic risk in artificial worlds, using a new tool in the ABM perspective

We propose SLAPP, or Swarm-Like Agent Protocol in Python, as a simplified application of the original Swarm protocol, choosing Python as a simultaneously simple and complete object-oriented framework. With SLAPP we develop two test models in the Agent-Based Models (ABM) perspective, building an artificial world related to the actual important issue of interbank payment and liquidity