When solving instances of problem domains that feature a large branching factor, A* may
generate a large number of nodes whose cost is greater than the cost of the optimal solution …

Work in machine learning has grown tremendously in the past years, but has had little to no
impact on optimal search approaches. This paper looks at challenges in using deep …

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 …

A* is often described as beingoptimal', in that it expands the minimum number of unique
nodes. But, A* may generate many extra nodes which are never expanded. This is a …

Heuristic search is a state-of-the-art approach to classical planning. Several heuristic
families were developed over the years to automatically estimate goal distance information …

Bidirectional search algorithms interleave two separate searches, a normal search forward
from the start state, and a search backward from the goal. It is well known that adding a …

Dijkstra's single-source shortest-path algorithm (DA) is one of the well-known, fundamental
algorithms in computer science and related fields. DA is commonly taught in undergraduate …

In a common parking lot, much of the space is devoted not to parking but to lanes in which
cars travel to and from parking spaces. Lanes must not be blocked for one simple reason: a …

Mathematical Selection is a method in which we select a particular choice from a set of such.
It have always been an interesting field of study for mathematicians. Accordingly …

Recent work introduced XDP and XUP priority functions for best-first bounded-suboptimal
search that do not need to perform state re-expansions as long as the search heuristic is …