Markov analysis of qualitative dynamics1
作者:
JON DOYLE,
ELISHA P. SACKS,
期刊:
Computational Intelligence
(WILEY Available online 1991)
卷期:
Volume 7,
issue 1
页码: 1-10
ISSN:0824-7935
年代: 1991
DOI:10.1111/j.1467-8640.1991.tb00330.x
出版商: Blackwell Publishing Ltd
关键词: qualitative reasoning;commonsense reasoning;dynamical systems;qualitative dynamics;probabilistic estimation;Markov chains.;raisonnement qualitatif;raisonnement de sens commun;système dynamique;évaluation des probabilityés;chaǐnes de Markov.
数据来源: WILEY
摘要:
Common sense sometimes predicts events to be likely or unlikely rather than merely possible. We extend methods of qualitative reasoning to predict the relative likelihoods of possible qualitative behaviors by viewing the dynamics of a system as a Markov chain over its transition graph. This involves adding qualitative or quantitative estimates of transition probabilities to each of the transitions and applying the standard theory of Markov chains to distinguish persistent states from transient states and to calculate recurrence times, settling times, and probabilities for ending up in each state. Much of the analysis depends solely on qualitative estimates of transition probabilities, which follow directly from theoretical considerations and which lead to qualitative predictions about entire classes of systems. Quantitative estimates for specific systems are derived empirically and lead to qualitative and quantitative conclusions, most of which are insensitive to small perturbations in the estimated transition probabilities. The algorithms are straightforward and efficient.
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