I'm currently studying for an AI computer-science course.One thing is difficult for me to grasp and somewhat vaguely explained in my course-material:I understand that with search-methods like e.g. beam search, hill-climbing... you can search based on a certain knowledge about the problem which can tell you in each state an approximation off how close you are to finding the solution. Currently I've reached a chapter about Optimal Search algorithms. An important algorithm in that chapter is Uniform Cost which is based on the lowest accumulated travel-cost, with as desired consequence that the algorithm will reach a solution in a branch with the lowest accumulated travel cost. Now, I don't understand that travel cost and how it differs from a normal heuristic value. Can someone explain the difference and how it effects finding a solution?
Difference between heuristic-based searching and optimal path searching
search algorithms;artificial intelligence;search trees;heuristics
Uniform Cost Search (UCS) does not have a heuristic value and doesn't rely on a heuristic function.  I suspect you're getting it confused with some other algorithm (e.g., A*).  In Uniform Cost Search, there's no heuristic: we know exactly what the distance from the starting point was, and we use that to determine which nodes to expand.