So far our approach to solving problems has been characterized by state space search. We are in a given state, and we have a desired or goal state. We have a set of moves available to us which allow us to navigate from one state to another. We search through the possible moves, and we employ a heuristic function to explore the space in an informed manner. In this chapter we study two different approaches to problem solving.

One, with emphasis on knowledge that we can acquire from domain experts. We look at mechanisms to harness and exploit such knowledge. In the last century in the 1980s, an approach to express knowledge in the form of if–then rules gained momentum, and many systems were developed under the umbrella of expert systems. Although only a few lived up to expert level expectations, the technology matured into an approach to allow human users to impart their knowledge into systems. The key to this approach was the Rete algorithm that allowed an inference engine to efficiently match rules with data.

The other looks at problem solving from a teleological perspective. That is, we look at a goal based approach which investigates what needs to be done to achieve a goal. In that sense, it is reasoning backwards from the goal. We look at how problems can be formulated as goal trees, and an algorithm AO* to solve them.