AI as search for a problem

 In the history of AI development it was mostly unclear, what exactly AI is about. In the 1980s a common definition was, that AI has to do with search in the problem space. But this definition fits only to small subsection of AI which is state space search in games. And search algorithms like A* can't be applied to more complex problems.

A possible improved definition is, that AI has to do with search for the problems. This definition shifts the focus away from algorithm centric solving of existing challenges towards an explorative search for new problems.

Example problem might be the 8 puzzle problem, chess puzzles, path finding in a maze, or the VQA problem (visual question answering). The assumption is, that more advanced AI techniques can be unlocked by inventing more advanced puzzles first.

Let me given an example. Suppose the self selected problem to solve is the 8 queens problem. Under such a constraint, the following debate consists of a mathematical problem description, and a comparison of different algorithms which are mostly back tracking algorithms. The decision to solve the 8 queen problem results into a certain bias.

By selecting other problems the discussion space gets modified. Especially by selecting language games like Visual question answering, the debate gets modified drastically. Instead of describe only search based algorithms, new topics have to be discussed like vocabulary definition or grounded language. These new subjects are not available in classic problems like the 8 queen problem.

So we can say, that no hard subjects within computer science like hardware, software, algorithms or programming language are creating the discourse space for Artificial Intelligence, but the debate gets influenced by the preference for certain AI problems. The decision for or against certain problems affects the discovery of possible problem solving techniques. If the attempt is to solve only the 15 puzzle problem, a certain sort of computer program is the result. But if the task is to solve problems with grounded language like the instruction following problem, more advanced algorithms are needed.

The main question which has to answered by the AI community is, what are the advantages and disadvantages of a certain problem category. The traveling salesman problem was popular in the 1980s because it can be described easily and can be converted into an mathematical algorithm. The main disadvantage of this problem is, that even the algorithm has found the shortest route, the same algorithm can't control a real robot because robotics has to be described with different problems.

The holy grail is perhaps an AI problem, which can be described easily but fits to a variety of real world robotics problems. If it matches to the reality, the algorithms used for solving the problem can be adapted to real world scenarios.