In imitation learning for planning, parameters of heuristic functions are optimized against a
set of solved problem instances. This work revisits the necessary and sufficient conditions of …

Symbolic search has proven to be a competitive approach to cost-optimal planning, as it
compactly represents sets of states by symbolic data structures. While heuristics for symbolic …

In classical planning, the goal is to derive a course of actions that allows an intelligent agent
to move from any situation it finds itself in to one that satisfies its goals. Classical planning is …

In this book the author argues that the basis of what we consider computer intelligence has
algorithmic roots, and he presents this with a holistic view, showing examples and …

ComplementaryPDB is a planner that uses heuristic search via Symbolic Pattern Databases
(PDBs) and uses a greedy pattern selection algorithm–Partial Gamer–combined with pattern …

The difficulty of task planning for robotic agents arises from the stochastic nature of their
environment and the high cost of a failure during execution meaning frequent replanning is …

Pattern database (PDB) is one of the most popular automated heuristic generation
techniques. A PDB maps states in a planning task to abstract states by considering a subset …

Optimization of heuristic functions for the A* algorithm, realized by deep neural networks, is
usually done by minimizing square root loss of estimate of the cost to goal values. This …

Climate change, increase in world population and the war in Ukraine have led nations such
as the UK to put a larger focus on food security, while simultaneously trying to halt declines …

Planning is the field of Artificial Intelligence (AI) tasked with finding a sequence of actions for
achieving a goal from an initial description of the environment. In this thesis, we will look into …