Greedy Best First Search (GBFS) is a powerful algorithm at the heart of many state-of-the-art satisficing planners. The Greedy Best First Search with Local Search (GBFS-LS) algorithm adds exploration using a local GBFS to a global GBFS. This substantially improves performance for domains that contain large uninformative heuristic regions (UHR), such as plateaus or local minima. This paper analy...

Greedy best-first search (GBFS) is a prominent search algorithm for satisficing planning – finding good enough solutions to a planning task in reasonable time. GBFS selects the next node to consider based on the most promising node estimated by a heuristic function. However, this behaviour makes GBFS heavily depend on the quality of the heuristic estimator. Inaccurate heuristics can lead GBFS i...

It has been shown recently that the performance of greedy best-first search (GBFS) for computing plans that are not necessarily optimal can be improved by adding forms of exploration when reaching heuristic plateaus: from random walks to local GBFS searches. In this work, we address this problem but using structural exploration methods resulting from the ideas of width-based search. Width-based...

LAMA-2011 (Richter and Westphal 2010) is the clear winner of the sequential satisficing track in the latest International Planning Competition (IPC-2011). It finds a first solution by Greedy Best-First Search (GBFS), and then continues to improve solutions using restarting weighted A* (Richter, Thayer, and Ruml 2010). Diverse Anytime Search (DAS) (Xie, Valenzano, and Müller 2013) is a metaalgor...

An earlier review of the toxicity of glyphosate and the original Roundup™-branded formulation concluded that neither glyphosate nor the formulation poses a risk for the production of heritable/somatic mutations in humans. The present review of subsequent genotoxicity publications and regulatory studies of glyphosate and glyphosate-based formulations (GBFs) incorporates all of the findings into ...

A classical result in optimal search shows that A* with an admissible and consistent heuristic expands every state whose f -value is below the optimal solution cost and no state whose f -value is above the optimal solution cost. For satisficing search algorithms, a similarly clear understanding is currently lacking. We examine the search behaviour of greedy bestfirst search (GBFS) in order to m...

Fly ash and granulated blast furnace slag (GBFS) are major by-products of thermal and steel plants, respectively. These materials often cause disposal problems and environmental pollution. Detailed laboratory investigations were carried out on cement stabilized fly ash-(GBFS) mixes in order to find out its suitability for road embankments, and for base and sub-base courses of highway pavements....

Utilizing multiple queues in Greedy Best-First Search (GBFS) has been proven to be a very effective approach to satisficing planning. Successful techniques include extra queues based on Helpful Actions (or Preferred Operators), as well as using Multiple Heuristics. One weakness of all standard GBFS algorithms is their lack of exploration. All queues used in these methods work as priority queues...

While greedy best-first search (GBFS) is a popular algorithm for solving automated planning tasks, it can exhibit poor performance if the heuristic in use mistakenly identifies a region of the search space as promising. In such cases, the way the algorithm greedily trusts the heuristic can cause the algorithm to get stuck performing a lot of unnecessary work. In this report, we consider simple ...

Greedy best-first search (GBFS) is a popular and effective algorithm in satisficing planning and is incorporated into high-performance planners. GBFS in planning decides its search direction with automatically generated heuristic functions. However, if the heuristic functions evaluate nodes inaccurately, GBFS may be misled into a valueless search direction, thus resulting in performance degrada...