Epistemic and pedagogical assumptions for informative and persuasive speaking practices: disinterring dichotomy.(Essay): An article from: Argumentation and Advocacy

This digital document is an article from Argumentation and Advocacy, published by Thomson Gale on June 22, 2006. The length of the article is 5893 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available in your Amazon.com Digital Locker immediately after purchase. You can view it with any web browser.

Citation Details
Title: Epistemic and pedagogical assumptions for informative and persuasive speaking practices: disinterring dichotomy.(Essay)
Author: Mark A. Gring
Publication: Argumentation and Advocacy (Magazine/Journal)
Date: June 22, 2006
Publisher: Thomson Gale
Volume: 43 Issue: 1 Page: 41(10)

Article Type: Essay

Distributed by Thomson Gale

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Knowledge Contributors

In the late 1950’s and early 1960’s seminal works on the logic of knowledge and belief were published by notably von Wright and Hintikka. Epistemic and doxastic logics have since then grown into mature disciplines enjoying many important applications in philosophy, computer science, game theory, economics and linguistics to mention but a few fields. The aim of this thematically unified anthology is to track the history of epistemic logic, to consider some important applications of these logics of knowledge and belief in a variety of fields, and finally to discuss future directions of research with particular emphasis on ‘active agenthood’ and multi-modal systems. Knowledge Contributors include H. van Ditmarsch, R. Fagin, J. Halpern, J. Hintikka, W. van der Hoek, B. Kooi, W. Lenzen, Y. Moses, H. Rott, J. Sowa, M. Vardi and R. Wojcicki. Knowledge Contributors is accessible to researchers as well as graduate students in philosophy, computer science, game theory, economics and related disciplines utilizing the means and methods of epistemic logic.

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Information, Interaction, and Agency

Contemporary epistemological and cognitive studies, as well as recent trends in computer science and game theory have revealed an increasingly important and intimate relationship between Information, Interaction, and Agency. Agents perform actions based on the available information and in the presence of other interacting agents. From this perspective Information, Interaction, and Agency neatly ties together classical themes like rationality, decision-making and belief revision with games, strategies and learning in a multi-agent setting. Unified by the central notions Information, Interaction, and Agency, the essays in this volume provide refreshing methodological perspectives on belief revision, dynamic epistemic logic, von Neumann games, and evolutionary game theory; all of which in turn are central approaches to understanding our own rationality and that of other agents. Reprinted from Synthese, 139:2 and 142:2 (2004), Special Section Knowledge, Rationality, and Action.

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Uncertainty, probability and information-gaps [An article from: Reliability Engineering and System Safety]

This digital document is a journal article from Reliability Engineering and System Safety, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

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This paper discusses two main ideas. First, we focus on info-gap uncertainty, as distinct from probability. Info-gap theory is especially suited for modelling and managing uncertainty in system models: we invest all our knowledge in formulating the best possible model; this leaves the modeller with very faulty and fragmentary information about the variation of reality around that optimal model. Second, we examine the interdependence between uncertainty modelling and decision-making. Good uncertainty modelling requires contact with the end-use, namely, with the decision-making application of the uncertainty model. The most important avenue of uncertainty-propagation is from initial data- and model-uncertainties into uncertainty in the decision-domain. Two questions arise. Is the decision robust to the initial uncertainties? Is the decision prone to opportune windfall success? We apply info-gap robustness and opportunity functions to the analysis of representation and propagation of uncertainty in several of the Sandia Challenge Problems.

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Epistemic Logic for AI and Computer Science (Cambridge Tracts in Theoretical Computer Science)

Epistemic logic has grown from its philosophical beginnings to find diverse applications in computer science, and as a means of reasoning about the knowledge and belief of agents. This book provides a broad introduction to the subject, along with many exercises and their solutions. The authors begin by presenting the necessary apparatus from mathematics and logic, including Kripke semantics and the well-known modal logics K, T, S4 and S5. Then they turn to applications in the context of distributed systems and artificial intelligence. These include the notions of common knowledge, distributed knowledge, explicit and implicit belief, the interplays between knowledge and time, and knowledge and action, as well as a graded (or numerical) variant of the epistemic operators. The authors also discuss extensively the problem of logical omniscience. They cover Halpern & Moses’ theory of honest formulas, and they make a digression into the realm of nonmonotonic reasoning and preferential entailment. They discuss Moore’s autoepistemic logic, together with Levesque’s related logic of “all I know”. Furthermore, they show how one can base default and counterfactual reasoning on epistemic logic. Graduate students in philosophy or in computer science, especially those with an interest in AI, will find this book useful.

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Epistemic Logic Planning: Case-Based Planning Adaptation, Using Epistemic LogicRevision for Robot’s Decision Making

Artificial Intelligence algorithms can be dividedinto two groups according to the type of problemsthey solve. Knowledge-intensive domains containexplicit knowledge, whereas knowledge-poor domainscontain implicit knowledge. Rules, decision trees andlogical methods are more suitable for the first type.Neural networks and case-based reasoning (CBR) aremore suitable for the second type. This project combines the inferencing power ofepistemic logic (type 1) in the adaptation of CBRwith the performance of case-based planning (type 2).This method is proved to be more efficient then usingplanning algorithms alone. Here a simulated robot is assigned to deliverparts in a factory. The robot needs to plan the paththat it should choose to achieve its goals and usesepistemic logic to solve problems. Planningalgorithms are computationally expensive. CBR, usingKNN is used to make the process faster. A STRIPSplanner creates plans. The manager defines theproblem, KNN extracts a plan and a logic sub-systemadapts it according to belief revision theorems. Thenew plan is retained for future use.

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Epistemic cooperation scripts in online learning environments: fostering learning by reducing uncertainty in discourse? [An article from: Computers in Human Behavior]

This digital document is a journal article from Computers in Human Behavior, published by Elsevier in 2005. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
Using online learning environments in higher education offers innovative possibilities to support collaborative learning. However, online learning creates new kinds of problems for participants who have not previously worked with each other. One of these problems is uncertainty which occurs when participants do not know each other. According to the uncertainty reduction theory, low uncertainty level increases the amount of discourse and decreases the amount of information seeking. Therefore, uncertainty may influence online discourse and learning. This study investigates the effects of an epistemic cooperation script with respect to the amount of discourse, information seeking and learning outcomes in collaborative learning as compared to unscripted collaborative learning. The aim was also to explore how and what kind of information learners seek and receive and how learning partners react to such information exchange. The participants were 48 students who were randomly assigned to groups of three in two conditions, one with and one without an epistemic script. The results indicate that the epistemic script increased the amount of discourse and decreased the amount of information seeking activities. Without an epistemic script, however, learners achieved better learning outcomes. The results of two qualitative case-based analyses on information seeking will also be discussed.

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A Logical Theory of Nonmonotonic Inference and Belief Change

A monograph offering logical foundations for and a uniform description of nonmonotonic reasoning and belief change. Based on a notion of an epistemic state that subsumes existing models for both reasoning and belief change. For experts in artificial intelligence, as well as logicians.

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Knowledge in Flux: Modeling the Dynamics of Epistemic States

Knowledge in Flux presents a theory of rational changes of belief, focusing particularly on revisions that occur when the agent receives new information that is inconsistent with the present epistemic state. It brings together, systematizes, and enlarges upon an already influential body of work by the author and his colleagues on the dynamics of theories and epistemic states. The problem of knowledge representation is one of the most important current research problems in philosophy, artificial intelligence, and cognitive science in general. While most of the research has been devoted to analyzing the structure of epistemic states, this book is unique in describing the dynamics of knowledge and belief, and in presenting models of knowledge that focus on expansions, revisions, and contractions of epistemic states.

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Knowledge in Flux: Modeling the Dynamics of Epistemic States Reviews

Review by :

This book provides a simple, self-contained and coherent framework for belief revision. The ability to model how an intelligent entity should revise its beliefs as it learns more about its environment is a fundamental goal of Artifical Intelligence.

Review by Sebastian Melmoth:

This book provides a simple, self-contained and coherent framework for belief revision. The ability to model how an intelligent entity should revise its beliefs as it learns more about its environment is a fundamental goal of Artifical Intelligence.

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Knowledge Contributors (Synthese Library)

In the late 1950’s and early 1960’s seminal works on the logic of knowledge and belief were published by notably von Wright and Hintikka. Epistemic and doxastic logics have since then grown into mature disciplines enjoying many important applications in philosophy, computer science, game theory, economics and linguistics to mention but a few fields. The aim of this thematically unified anthology is to track the history of epistemic logic, to consider some important applications of these logics of knowledge and belief in a variety of fields, and finally to discuss future directions of research with particular emphasis on ‘active agenthood’ and multi-modal systems. Knowledge Contributors include H. van Ditmarsch, R. Fagin, J. Halpern, J. Hintikka, W. van der Hoek, B. Kooi, W. Lenzen, Y. Moses, H. Rott, J. Sowa, M. Vardi and R. Wojcicki. Knowledge Contributors is accessible to researchers as well as graduate students in philosophy, computer science, game theory, economics and related disciplines utilizing the means and methods of epistemic logic.

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Price: $ 60.93