Suboptimal heuristic search algorithms such as greedy best-first search allow us to find
solutions when constraints of either time, memory, or both prevent the application of optimal …

Search in general, and heuristic search in particular, is at the heart of many Artificial
Intelligence algorithms and applications. There is now a growing and active community …

ArvandHerd is a sequential satisficing planner that uses a portfolio consisting of LAMA and
Arvand. This planner won the multi-core track of the 2011 International Planning …

Graphical processing units (GPUs) have become ubiquitous because they offer the ability to
perform cost and energy efficient massively parallel computation. We investigate forward …

Real-world planning problems often feature complex temporal and numeric characteristics.
These include concurrent activities and also effects that involve continuous change. This …

State space search is one of the most important and proverbial techniques for planning. At
the core of state space search, heuristic function largely determines the search efficiency. In …

This thesis introduces random walk (RW) planning as a new search paradigm for satisficing
planning by studying its theory, its practical relevance, and applications. We develop a …

This thesis applies the Monte Carlo Random Walk method (MRW) to motion planning. We
explore different global and local restart strategies to improve the performance. Several new …

It is known that seemingly small details such as tie-breaking among nodes with the same f-
cost can significantly affect the performance of a best-first search algorithm on many …

Random walks are a relatively new component used in several state of the art satisficing
planners. Empirical results have been mixed: while the approach clearly outperforms more …