In this survey paper, we characterize the learning problem in cognitive radios (CRs) and
state the importance of artificial intelligence in achieving real cognitive communications …

An intelligent agent interacting with the real world will encounter individual people, courses,
test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of …

Planning is the model-based approach to autonomous behavior where the agent behavior is
derived automatically from a model of the actions, sensors, and goals. The main challenges …

State abstraction is necessary for better task transfer in complex reinforcement learning
environments. Inspired by the benefit of state abstraction in MAXQ and building upon hybrid …

Generalized planning is concerned with the computation of general policies that solve
multiple instances of a planning domain all at once. It has been recently shown that these …

Abstract A Relational Markov Decision Process (RMDP) is a first-order representation to
express all instances of a single probabilistic planning domain with possibly unbounded …

Generalized planning is concerned with the computation of plans that solve not one but
multiple instances of a planning domain. Recently, it has been shown that generalized plans …

Many AI problems arising in a wide variety of fields such as machine learning, semantic
web, network communication, computer vision, and robotics can elegantly be encoded and …

Partially-observable Markov decision processes (POMDPs) provide a powerful model for
sequential decision-making problems with partially-observed state and are known to have …

It has been observed that many classical planning domains with atomic goals can be solved
by means of a simple polynomial exploration procedure, called IW, that runs in time …