Neural planners for RDDL MDPs produce deep reactive policies in an offline fashion. These
scale well with large domains, but are sample inefficient and time-consuming to train from …

Supporting state-dependent action costs in planning admits a more compact representation
of many tasks. We generalize the additive heuristic and compute it by embedding decision …

A Markov Decision Process (MDP) policy presents, for each state, an action, which
preferably maximizes the expected utility accrual over time. In this article, we present a novel …

Abstract The Planning Domain Definition Language (PDDL) is the state-of-the-art language
for specifying planning problems in artificial intelligence research. Writing and maintaining …

Recent work has begun exploring the value of domain abstractions in Monte-Carlo Tree
Search (MCTS) algorithms for probabilistic planning. These algorithms automatically …

Sample-based tree search (SBTS) is an approach to solving Markov decision problems
based on constructing a lookahead search tree using random samples from a generative …

Replanning methods that determinize a stochastic planning problem and replan at each
action step have long been known to provide strong baseline (and even competition …

Real world robotics often operates in uncertain and dynamic environments where
generalisation over different scenarios is of practical interest. In the absence of a model …

Enabling an intuitive, bidirectional communication with real-time feedback to convey
intentions and goals is essential in human-robot collaboration (HRC). In this paper, we …

In real world environments the state is almost never completely known. Exploration is often
expensive. The application of planning in these environments is consequently more difficult …