M. Goldszmidt et J. Pearl, QUALITATIVE PROBABILITIES FOR DEFAULT REASONING, BELIEF REVISION, ANDCAUSAL-MODELING, Artificial intelligence, 84(1-2), 1996, pp. 57-112
Citations number
76
Categorie Soggetti
Computer Sciences, Special Topics","Computer Science Artificial Intelligence",Ergonomics
This paper presents a formalism that combines useful properties of bot
h logic and probabilities, Like logic, the formalism admits qualitativ
e sentences and provides symbolic machinery for deriving deductively c
losed beliefs and, like probability, it permits us to express if-then
rules with different levels of firmness and to retract beliefs in resp
onse to changing observations. Rules are interpreted as order-of-magni
tude approximations of conditional probabilities which impose constrai
nts over the rankings of worlds. Inferences are supported by a unique
priority ordering on rules which is syntactically derived from the kno
wledge base. This ordering accounts for rule interactions, respects sp
ecificity considerations and facilitates the construction of coherent
states of beliefs, Practical algorithms are developed and analyzed for
testing consistency, computing rule ordering, and answering queries,
Imprecise observations are incorporated using qualitative versions of
Jeffrey's rule and Bayesian updating, with the result that coherent be
lief revision is embodied naturally and tractably. Finally, causal rul
es are interpreted as imposing Markovian conditions that further const
rain world rankings to reflect the modularity of causal organizations.
These constraints are shown to facilitate reasoning about causal proj
ections, explanations, actions and change.