scholarly journals Modeling Belief in Dynamic Systems, Part II: Revision and Update

1999 ◽  
Vol 10 ◽  
pp. 117-167 ◽  
Author(s):  
N. Friedman ◽  
J. Y. Halpern
Keyword(s):  
Artificial Intelligence ◽  
Belief Revision ◽  
Dynamic Systems ◽  
Belief Change ◽  
Companion Paper ◽  
Minimal Change ◽  
Special Cases ◽  
Epistemic Modalities ◽  
Over Time ◽  
New Framework

The study of belief change has been an active area in philosophy and AI. In recent years two special cases of belief change, belief revision and belief update, have been studied in detail. In a companion paper (Friedman & Halpern, 1997), we introduce a new framework to model belief change. This framework combines temporal and epistemic modalities with a notion of plausibility, allowing us to examine the change of beliefs over time. In this paper, we show how belief revision and belief update can be captured in our framework. This allows us to compare the assumptions made by each method, and to better understand the principles underlying them. In particular, it shows that Katsuno and Mendelzon's notion of belief update (Katsuno & Mendelzon, 1991a) depends on several strong assumptions that may limit its applicability in artificial intelligence. Finally, our analysis allow us to identify a notion of minimal change that underlies a broad range of belief change operations including revision and update.

scholarly journals A Semantics for Hyperintensional Belief Revision Based on Information Bases

Studia Logica ◽  
2021 ◽  
Author(s):  
Sena Bozdag
Keyword(s):  
Belief Revision ◽  
Epistemic Logic ◽  
Belief Change ◽  
Dynamic Epistemic Logic ◽  
Doxastic Logic ◽  
Dynamic Operation ◽  
Belief Dynamics ◽  
Dynamic Doxastic Logic ◽  
New Framework

AbstractI propose a novel hyperintensional semantics for belief revision and a corresponding system of dynamic doxastic logic. The main goal of the framework is to reduce some of the idealisations that are common in the belief revision literature and in dynamic epistemic logic. The models of the new framework are primarily based on potentially incomplete or inconsistent collections of information, represented by situations in a situation space. I propose that by shifting the representational focus of doxastic models from belief sets to collections of information, and by defining changes of beliefs as artifacts of changes of information, we can achieve a more realistic account of belief representation and belief change. The proposed dynamic operation suggests a non-classical way of changing beliefs: belief revision occurs in non-explosive environments which allow for a non-monotonic and hyperintensional belief dynamics. A logic that is sound with respect to the semantics is also provided.

Dualities, Dialectics, and Paradoxes in Organizational Life

2018 ◽  
Keyword(s):  
Dynamic Systems ◽  
Empirical Studies ◽  
Organizational Studies ◽  
Organizational Life ◽  
Organizational Processes ◽  
Organization Studies ◽  
Process Studies ◽  
Convergence And Divergence ◽  
Insight Into ◽  
Over Time

Organizational contradictions and process studies offer interwoven and complementary insights. Studies of dialectics, paradox, and dualities depict organizational contradictions that are oppositional as well as interrelated such that they persistently morph and shift over time. Studies of process often examine how contradictions fuel emergent, dynamic systems and stimulate novelty, adaptation, and transformation. Drawing from rich conversations at the Eighth International Symposium on Process Organization Studies, the contributors to this volume unpack these relationships in more depth. The chapters explore three main, connected themes through both conceptual and empirical studies, including (1) offering insight into how process theorizing advances understandings of organizational contradictions; (2) shedding light on how dialectics, paradoxes, and dualities fuel organizational processes that affect persistence and transformation; and (3) exploring the convergence and divergence of dialectics, paradox, and dualities lenses. Taken together, this book offers key insights in order to inform persistent, contradictory dynamics in organizations and organizational studies.

scholarly journals Variability as normal as apple pie

2021 ◽  
Vol 7 (s2) ◽  
Author(s):  
Marjolijn Verspoor ◽  
Wander Lowie ◽  
Kees de Bot
Keyword(s):  
Dynamic Systems ◽  
Dynamic Systems Theory ◽  
Complex Dynamic ◽  
Controlled Processes ◽  
Complex Dynamic Systems ◽  
L2 Development ◽  
Limited Degree ◽  
Development Phases ◽  
Longitudinal Case Studies ◽  
Over Time

Abstract In recent studies in second language (L2) development, notably within the focus of Complex Dynamic Systems Theory (CDST), non-systematic variation has been extensively studied as intra-individual variation, which we will refer to as variability. This paper argues that variability is functional and is needed for development. With examples of four longitudinal case studies we hope to show that variability over time provides valuable information about the process of development. Phases of increased variability in linguistic constructions are often a sign that the learner is trying out different constructions, and as such variability can be evidence for change, and change can be learning. Also, a limited degree of variability is inherent in automatic or controlled processes. Conversely, the absence of variability is likely to show that no learning is going on or the system is frozen.

Smart design of intelligent companion toys for preschool children

2020 ◽  
pp. 1-14
Author(s):  
Xin Wang ◽  
Nian Yin ◽  
Zhinan Zhang
Keyword(s):  
Artificial Intelligence ◽  
Early Childhood ◽  
Preschool Children ◽  
Essential Role ◽  
Childhood Education ◽  
Child Centered ◽  
Toy Design ◽  
Complete Framework ◽  
New Framework

Abstract Early childhood education has long-lasting influences on people, and an appropriate companion toy can play an essential role in children's brain development. This paper establishes a complete framework to guide the design of intelligent companion toys for preschool children from 2 to 6 years old, which is child-centered and environment-oriented. The design process is divided into three steps: requirement confirmation, the smart design before the sale, and the iterative update after the sale. This framework considers the characteristics of children and highlights the integration of human and artificial intelligence in design. A case study is provided to prove the superiority of the new framework. In addition to enriching the research on intelligent toy design, this paper also guides for practitioners to design smart toys and helps in children's cognitive development.

Robots as cognitive tools

2002 ◽  
Vol 1 (1) ◽  
pp. 125-143 ◽  
Author(s):  
Rolf Pfeifer
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Main Tool ◽  
Human Cognition ◽  
Abstract Thinking ◽  
Cognitive Tools ◽  
Developmental Robotics ◽  
Information Theoretic ◽  
Natural Forms ◽  
Over Time

Artificial intelligence is by its very nature synthetic, its motto is “Understanding by building”. In the early days of artificial intelligence the focus was on abstract thinking and problem solving. These phenomena could be naturally mapped onto algorithms, which is why originally AI was considered to be part of computer science and the tool was computer programming. Over time, it turned out that this view was too limited to understand natural forms of intelligence and that embodiment must be taken into account. As a consequence the focus changed to systems that are able to autonomously interact with their environment and the main tool became the robot. The “developmental robotics” approach incorporates the major implications of embodiment with regard to what has been and can potentially be learned about human cognition by employing robots as cognitive tools. The use of “robots as cognitive tools” is illustrated in a number of case studies by discussing the major implications of embodiment, which are of a dynamical and information theoretic nature.

Reinforcement Learning for Cloud Computing Digital Library

2014 ◽  
Vol 571-572 ◽  
pp. 105-108
Author(s):  
Lin Xu
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Reinforcement Learning ◽  
Digital Library ◽  
Learning Algorithms ◽  
Experimental Results ◽  
Current Status ◽  
Self Learning ◽  
New Framework

This paper proposes a new framework of combining reinforcement learning with cloud computing digital library. Unified self-learning algorithms, which includes reinforcement learning, artificial intelligence and etc, have led to many essential advances. Given the current status of highly-available models, analysts urgently desire the deployment of write-ahead logging. In this paper we examine how DNS can be applied to the investigation of superblocks, and introduce the reinforcement learning to improve the quality of current cloud computing digital library. The experimental results show that the method works more efficiency.

Long-Run Effects in Dynamic Systems: New Tools for Cross-Lagged Panel Models

2021 ◽  
pp. 109442812199322
Author(s):  
Ali Shamsollahi ◽  
Michael J. Zyphur ◽  
Ozlem Ozkok
Keyword(s):  
Recent Work ◽  
Dynamic Systems ◽  
Quantitative Methods ◽  
Simulated Dataset ◽  
Impulse Responses ◽  
Panel Models ◽  
Long Run ◽  
Short Run ◽  
User Friendly ◽  
Over Time

Cross-lagged panel models (CLPMs) are common, but their applications often focus on “short-run” effects among temporally proximal observations. This addresses questions about how dynamic systems may immediately respond to interventions, but fails to show how systems evolve over longer timeframes. We explore three types of “long-run” effects in dynamic systems that extend recent work on “impulse responses,” which reflect potential long-run effects of one-time interventions. Going beyond these, we first treat evaluations of system (in)stability by testing for “permanent effects,” which are important because in unstable systems even a one-time intervention may have enduring effects. Second, we explore classic econometric long-run effects that show how dynamic systems may respond to interventions that are sustained over time. Third, we treat “accumulated responses” to model how systems may respond to repeated interventions over time. We illustrate tests of each long-run effect in a simulated dataset and we provide all materials online including user-friendly R code that automates estimating, testing, reporting, and plotting all effects (see https://doi.org/10.26188/13506861 ). We conclude by emphasizing the value of aligning specific longitudinal hypotheses with quantitative methods.

(DIS)BELIEF CHANGE AND ARGUED FEED-BACK DIALOG

2005 ◽  
Vol 13 (05) ◽  
pp. 511-538
Author(s):  
LAURENT PERRUSSEL ◽  
JEAN-MARC THÉVENIN
Keyword(s):  
Belief Revision ◽  
Belief Change ◽  
Preference Relation ◽  
Belief State ◽  
Temporal Aspects ◽  
Incoming Information ◽  
Multi Agent

This paper focuses on the features of belief change in a multi-agent context where agents consider beliefs and disbeliefs. Disbeliefs represent explicit ignorance and are useful to prevent agents to entail conclusions due to their ignorance. Agents receive messages holding information from other agents and change their belief state accordingly. An agent may refuse to adopt incoming information if it prefers its own (dis)beliefs. For this, each agent maintains a preference relation over its own beliefs and disbeliefs in order to decide if it accepts or rejects incoming information whenever inconsistencies occur. This preference relation may be built by considering several criteria such as the reliability of the sender of statements or temporal aspects. This process leads to non-prioritized belief revision. In this context we first present the * and − operators which allow an agent to revise, respectively contract, its belief state in a non-prioritized way when it receives an incoming belief, respectively disbelief. We show that these operators behave properly. Based on this we then illustrate how the receiver and the sender may argue when the incoming (dis)belief is refused. We describe pieces of dialog where (i) the sender tries to convince the receiver by sending arguments in favor of the original (dis)belief and (ii) the receiver justifies its refusal by sending arguments against the original (dis)belief. We show that the notion of acceptability of these arguments can be represented in a simple way by using the non-prioritized change operators * and −. The advantage of argumentation dialogs is twofold. First whenever arguments are acceptable the sender or the receiver reconsider its belief state; the main result is an improvement of the reconsidered belief state. Second the sender may not be aware of some sets of rules which act as constraints to reach a specific conclusion and discover them through argumentation dialogs.

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