Ariel Felner

Suppose that an intelligent agent accepts as input a complete plan, i.e., a sequence of states (or operators) that should be followed in order to achieve a goal. For some reason, the given plan cannot be followed by the agent, and thus an alternative plan needs to be found - but we would like the alternative plan to be as close as possible to the original. To achieve this, we define a number of distance metrics between paths or plans, and characterize these functions and their respective attributes and advantages. We then develop a general algorithm based on best-first search that helps an agent find the most suitable alternative plan efficiently, and propose a number of heuristics for the cost function of this best-first search algorithm. We then experimentally show that our algorithm is efficient in finding an alternative plan.