Search algorithms are typically made to optimize a well-defined criterion. Unfortunately, real-world problems are often hard to describe in a handful of variables. It is known from psychology that humans use a variety of heuristics, not only to limit the search space, but also leading to more appropriate solutions than well-defined "optimal" strategies. Herbert Simon has coined the term "satisficing" to describe solutions that are good enough for real problems and can be calculated with little effort.
Approach
We develop a heuristic search paradigm that combines different problem-specific (and possibly problem-unspecific) heuristics to solve a problem without an explicit optimization criterion. The first version, called Greedy Expert Search (GES), was based on a generalized search algorithm. In the next step, a new implementation as a blackboard architecture will be more general and include the option of a loose problem hierarchy.
Hierarchical Knowledge for Heuristic Problem Solving --- A Case Study on the Traveling Salesperson Problem (Alexandra Kirsch), In First Annual Conference on Advances in Cognitive Systems, 2012. [pdf]
Humanlike Problem Solving in the Context of the Traveling Salesperson Problem (Alexandra Kirsch), In AAAI Fall Symposium on Advances in Cognitive Systems, 2011. [pdf]
