Reasoning about change requires predicting how long a proposition, having become true,
will continue to be so. Lacking perfect knowledge, an agent may be constrained to believe …

Reasoning about change requires predicting how long a proposition, having become true,
will continue to be so. Lacking perfect knowledge, an agent may be constrained to believe …

Temporal reasoning is a central component of research in artificial intelligence as well as of
our everyday reasoning. This dissertation investigates some problems that arise when one …

Intelligent agency requires some ability to predict the future. An agent must ask itself what is
presently its best course of action given what it now knows about what the world will be like …

I introduce a temporal belief-network representation of causal independence that a
knowledge engineer can use to elicit probabilistic models. Like the current, atemporal belief …

This article is an edited transcript of a lecture given at IJCAI-99, Stockholm, Sweden, on 4
August 1999. The article summarizes concepts, principles, and tools that were found useful …

We present a symbolic machinery that admits both probabilistic and causal information
about a given domain and produces probabilistic statements about the effect of actions and …

For many commonsense reasoning tasks associated with action domains, only a relatively
simple kind of causal knowledge (previously studied by Ge ner and Lin) is required. We de …

This paper develops axioms and formal semantics for statements of the form “X is causally
irrelevant to Y in context Z”, which we interpret to mean “Changing X will not affect Y once Z …

This paper introduces a representation of evidential relationships which permits updating of
belief in two simultaneous modes: causal (ie top-down) and diagnostic (ie bottom-up). It …