Computational logic, agents, machine learning, and declarative programming languages.
Draft book: Foundations of Empirical Beliefs
Empirical beliefs are beliefs that are acquired from observations by an agent situated
in an environment. Agents use empirical beliefs to maintain a model of their
environment and select actions to achieve their goals. The empirical belief base of an agent
is a set of empirical beliefs. An empirical belief is a function from some space into the
space of probability measures on another space; this is a conditional empirical belief. A
special case is where an empirical belief is just a probability measure on a space; this is a
nonconditional empirical belief. In a common case, the belief base of an agent is a single
probability measure on a state space. More generally, an agent can have a large number
of conditional and nonconditional empirical beliefs. This book provides a mathematical
theory of empirical beliefs. In particular, it examines in detail the structure of empirical
beliefs, and how to acquire, utilize, and logicize them.
J.W. Lloyd,
"Higher-order Computational Logic",
in Computational Logic: From Logic Programming into the Future,
A. Kakas and F. Sadri (editors),
Springer-Verlag, 2002.
