The problem of planning for a robot that operates in environments containing a large
number of objects, taking actions to move itself through the world as well as to change the …

Autonomous robots facing a diversity of open environments and performing a variety of tasks
and interactions need explicit deliberation in order to fulfill their missions. Deliberation is …

We generalise the interval-based relaxation to sequential numeric planning problems with
non-linear conditions and effects, and cyclic dependencies. This effectively removes all the …

Planning is motivated by acting. Most of the existing work on automated planning
underestimates the reasoning and deliberation needed for acting; it is instead biased …

Planning in hybrid systems is important for dealing with real-world applications. PDDL+
supports this representation of domains with mixed discrete and continuous dynamics, and …

This paper studies novel subgoaling relaxations for automated planning with propositional
and numeric state variables. Subgoaling relaxations address one source of complexity of the …

Modern robots are increasingly capable of performing “basic” activities such as localization,
navigation, and motion planning. However, for a robot to be considered intelligent, we would …

PDDL+ planning involves reasoning about mixed discrete-continuous change over time.
Nearly all PDDL+ planners assume that continuous change is linear. We present a new …

Planning with hybrid domains modelled in PDDL+ has been gaining research interest in the
Automated Planning community in recent years. Hybrid domain models capture a more …

This paper presents a model-based planner called the Probabilistic Sulu Planner or the p-
Sulu Planner, which controls stochastic systems in a goal directed manner within user …