To solve difficult problems heuristically, requires detailed attention to computational efficiency. This paper describes how a heuristic problem solving system, HPA, attempts to find a near optimal solution to the traveling salesman problem. A critical innovation over previous search algorithms is an explicit dynamic weighting of the heuristic information. The heuristic information is weighted inversely proportional to its depth in the search tree -- in consequence it produces a narrower depth first search than traditional weightings. At the same time, dynamic weighting retains the catastrophe protection of ordinary branch and bound algorithms.

An initial version of the program has been implemented in LISP on a PDP-10 and is being used in conjunction with robot research at SRI. STRIPS is a member of the class of problem solvers that search a space of "world models" to ind one in w hich a given goal is achieved. For any world model, we assume that there exists a set of appllcable ope rators, each of w hi eh transforms the world model to some other world model. The task of the problem solver is to find some composl11on of ope rat ors that trans forms a given initial worId mode] into one t hat satisfies some stated goa1 condltion. This f rarnewo rk for probl em so 1 v i ng has l een cen t ra 1 to much of t he research I n artificial Intel licence (1). Ou r p nmary interest he re is in the class of p robJ ems faced by a robot in rea rranging ob]ec t s and in navigatlng, l.e.