CS205: Building with Artificial Intelligence

Hill climbing can be applied to any problem where the current state allows for an accurate evaluation function. For example, the travelling salesman problem, the eight-queens problem, circuit design, and a variety of other real-world problems. Hill Climbing has been used in inductive learning models. One such example is PALO, a probabilistic hill climbing system which models inductive and speed-up learning. Some applications of this system have been fit into "explanation-based learning systems", and "utility analysis" models.

Hill Climbing has also been used in robotics to manage multiple-robot teams. One such example is the Parish algorithm, which allows for scalable and efficient coordination in multi-robot systems. The group of researchers designed "a team of robots [that] must coordinate their actions so as to guarantee location of a skilled evader".

Their algorithm allows robots to choose whether to work alone or in teams by using hill-climbing. Robots executing Parish are therefore "collectively hill-climbing according to local progress gradients, but stochastically make lateral or downward moves to help the system escape from local maxima".