Planning as heuristic search is a powerful approach to solving domain independent planning problems. In recent years, various successful heuristics and planners like FF, LPG, FAST...
Martin Wehrle, Sebastian Kupferschmid, Andreas Pod...
Planning problems are often formulated as heuristic search. The choice of the heuristic function plays a significant role in the performance of planning systems, but a good heuris...
Heuristic search using algorithms such as A and IDA is the prevalent method for obtaining optimal sequential solutions for classical planning tasks. Theoretical analyses of these ...
Case-based reasoning (CBR) aims at using experience from the past in order to guide future problem solving rather than “starting from scratch” every time. We propose a CBR stra...
Having to cope with memory limitations is an ubiquitous issue in heuristic search. We present theoretical and practical results on new variants for exploring state-space with respe...
Heuristic search effectiveness depends directly upon the quality of heuristic evaluations of states in the search space. We show why ordinal correlation is relevant to heuristic se...
Recent work shows that the memory requirements of bestfirst heuristic search can be reduced substantially by using a divide-and-conquer method of solution reconstruction. We show...
Algorithms for the construction of software interaction test suites have focussed on the special case of pairwise coverage; less is known about efficiently constructing test suite...
This paper contributes to solve effectively stochastic resource allocation problems known to be NP-Complete. To address this complex resource management problem, a Qdecomposition...
— Hyper-heuristics or “heuristics to chose heuristics” are an emergent search methodology that seeks to automate the process of selecting or combining simpler heuristics in o...