Search results
Results From The WOW.Com Content Network
Classic combinatorial search problems include solving the eight queens puzzle or evaluating moves in games with a large game tree, such as reversi or chess. A study of computational complexity theory helps to motivate combinatorial search. Combinatorial search algorithms are typically concerned with problems that are NP-hard. Such problems are ...
Vacuum World, a shortest path problem in which the goal is to vacuum up all the pieces of dirt. In scientific disciplines, a toy problem [1] [2] or a puzzlelike problem [3] is a problem that is not of immediate scientific interest, yet is used as an expository device to illustrate a trait that may be shared by other, more complicated, instances of the problem, or as a way to explain a ...
The missionaries and cannibals problem, and the closely related jealous husbands problem, are classic river-crossing logic puzzles. [1] The missionaries and cannibals problem is a well-known toy problem in artificial intelligence , where it was used by Saul Amarel as an example of problem representation.
Constraint satisfaction problems (CSPs) are mathematical questions defined as a set of objects whose state must satisfy a number of constraints or limitations. CSPs represent the entities in a problem as a homogeneous collection of finite constraints over variables , which is solved by constraint satisfaction methods.
Upward planarity testing [8] Hospitals-and-residents problem with couples; Knot genus [38] Latin square completion (the problem of determining if a partially filled square can be completed) Maximum 2-satisfiability [3]: LO5 Maximum volume submatrix – Problem of selecting the best conditioned subset of a larger matrix
Toy problems were invented with the aim to program an AI which can solve it. The blocks world domain is an example for a toy problem. Its major advantage over more realistic AI applications is, that many algorithms and software programs are available which can handle the situation. [2] This allows to compare different theories against each other.
The main lesson of thirty-five years of AI research is that the hard problems are easy and the easy problems are hard. The mental abilities of a four-year-old that we take for granted – recognizing a face, lifting a pencil, walking across a room, answering a question – in fact solve some of the hardest engineering problems ever conceived...
Explainable AI (XAI), or Interpretable AI, or Explainable Machine Learning (XML), is artificial intelligence (AI) in which humans can understand the decisions or predictions made by the AI. [127] It contrasts with the "black box" concept in machine learning where even its designers cannot explain why an AI arrived at a specific decision. [128]