Reasoning about causal relationships: Inferences on causal networks.

Reasoning about causal relationships: Inferences on causal networks.

Over the last decade, a normative framework for making causal inferences, Bayesian Probabilistic Causal Networks, has come to dominate psychological studies of inference based on causal relationships. The following causal networks-[X→Y→Z, X←Y→Z, X→Y←Z]-supply answers for questions like, "Suppose both X and Y occur, what is the probability Z occurs?" or "Suppose you intervene and make Y occur, w...

Causal reasoning about quantities Kenneth

Causality plays an important role in human thinking . Yet we are far from having a complete account of causal reasoning. This paper presents an analysis of causal reasoning about changes in quantities . We abstract from Al theories of qualitative physics three dimensions along which causal reasoning about quantities may be decomposed . We then use this framework to make some psychological predi...

Symbolic Causal Networks for Reasoning about Actions and Plans

We present an approach for reasoning about actions and plans when domain knowledge is represented by a symbolic causal network, which is a principled, logical representation of a domain that explicates its perceived causal structure. The proposed approach shows that causal structures can play a key role in logical reasoning about actions given their effective role in dealing with some of the pr...

A software system for causal reasoning in causal Bayesian networks

Knowing the cause and effect is important to researchers who are interested in modeling the effects of actions, and Artificial Intelligence researchers are among them. One commonly used method for modeling cause and effect is graphical model. Bayesian Network is a probabilistic graphical model for representing and reasoning uncertain knowledge. It has been used as a fundamental tool and is beco...

Causal Reasoning About Air raft A idents