Despite their success in transferring the powerful human faculty of
causal reasoning to a mathematical and computational form, causal
models have not been widely used in the context of core AI
applications such as robotics. In this paper, we argue that this
discrepancy is due to the static, propositional nature of existing
causality formalisms that make them difficult to apply in dynamic
real-world situations where the variables of interest are not
necessarily known a priori. We define Causal Logic Models (CLMs), a
new probabilistic, first-order representation which uses causality as
a fundamental building block. Rather than merely converting causal
rules to first-order logic as various methods in Statistical
Relational Learning have done, we treat the causal rules as basic
primitives which cannot be altered without changing the system. We
provide sketches of algorithms for causal reasoning using CLMs,
preliminary results for causal explanation, and explore the
significant differences between causal reasoning in CLMs and fixed
causal graphs, including the non-locality of manipulation and the
non-commutability between observation and manipulation.
[ Paper ]