Planning under uncertainty is a central problem in the study of automated sequential
decision making, and has been addressed by researchers in many different fields, including …

An in-depth understanding of uncertainty is the first step to making effective decisions under
uncertainty. Machine/deep learning (ML/DL) has been hugely leveraged to solve complex …

Many stochastic planning problems can be represented using Markov Decision Processes
(MDPs). A difficulty with using these MDP representations is that the common algorithms for …

Recent developments have clarified the process of generating partially ordered, partially
specified sequences of actions whose execution will achieve an agent's goal. This article …

Markov decision processes (MDPs) have proven to be popular models for decision-theoretic
planning, but standard dynamic programming algorithms for solving MDPs rely on explicit …

Markov decisions processes (MDPs) are becoming increasing popular as models of
decision theoretic planning. While traditional dynamic programming methods perform well …

We define the probabilistic planning problem in terms of a probability distribution over initial
world states, a boolean combination of propositions representing the goal, a probability …

The methodological underpinnings of AI are slowly changing. Benchmarks, test beds, and
controlled experimentation are becoming more common. Although we are optimistic that this …

This collection represents the primary reference work for researchers and students in the
area of Temporal Reasoning in Artificial Intelligence. Temporal reasoning has a vital role to …

Recent research in artificial intelligence has developed computational theories of agents'
involvements in their environments. Although inspired by a great diversity of formalisms and …