Any large language processing software relies in its operation on heuristic decisions concerning the strategy of processing. These decisions are usually "hard-wired" int...
Graphplan and heuristic state space planners such as HSP-R and UNPOP are currently two of the most effective approaches for solving classical planning problems. These approaches h...
Batching jobs in a manufacturing system is a very common policy in most industries. Main reasons for batching are avoidance of setups and/or facilitation of material handling. Bat...
Because many real-world problems can be represented and solved as constraint satisfaction problems, the development of effective, efficient constraint solvers is important. A solv...
Heuristic evaluation is a well known discount evaluation technique in HCI but has not been utilized in Information Visualization (InfoVis) to the same extent. While several sets o...
Torre Zuk, Lothar Schlesier, Petra Neumann, Mark S...
Scaling conformant planning is a problem that has received much attention of late. Many planners solve the problem as a search in the space of belief states, and some heuristic gu...
A memory-based heuristic is a heuristic function that is stored in a lookup table. Very accurate heuristics have been created by building very large lookup tables, sometimes calle...
— This paper is at the crossroad of Cognitive Psychology and AI Robotics. It reports a cross-disciplinary project concerned about implementing human heuristics within autonomous ...
Charles Tijus, Elisabetta Zibetti, V. Besson, Nico...
Abstract. A well established heuristic approach for solving various bicriteria optimization problems is to enumerate the set of Pareto optimal solutions, typically using some kind ...
As any other problem solving task that employs search, AI Planning needs heuristics to efficiently guide the problem-space exploration. Machine learning (ML) provides several tec...