The General Problem Solver GPS solves problems such as the “Missionaries and Cannibals” puzzle, a task set within a monotonic world where a few static sets of rules and bits of knowledge are needed and the best or next solution can be calculated within a limited world (the puzzle or the game). It would probably fare well with chess too. GPS is symbolic in that it has subprograms which change its current state and rules which encode the constraints. Thus, the system walks through several scenarios, always trying to change its current state to make it more similar to the desired state, always checking its coded subprograms and rules (Mitchell, p. 7). Its symbolic nature is also evident in that to GPS it does not matter what is contained in its strings of code, any string of nonsense can be processed (Mitchell, p. 8).
To Dennett, GPS avoids the frame problem precisely because it takes the shortcut to solving a problem; it merely defines a limited world and installs the required knowledge and rules which are needed to solve this specific task (Dennett, p. 198). A similar approach could be taken with chess or any other task where the knowledge and the rules needed are limited.
However, this approach of installing all that is needed in a machine runs into trouble when the knowledge and rules needed for a task are not as clear. In the end, interacting with the world as humans do cannot be framed as easily as a chess game or a maths or logic puzzle. The frame problem addresses exactly the problem of defining what knowledge is needed in any situation, how it is to be used and how it must change in an everyday situation, like making a sandwich in the middle of the night. An intelligent agent to Dennet must engage in swift information-sensitive “planning ” which has the effect of producing reliable but not foolproof expectations of the effects of its actions (Dennett, p. 193).
The semantic problem of systems like GPS is just what information must be installed in order to fulfill a certain task. In chess or the cannibal puzzle, the rules and knowledge for solving the puzzle of playing the game are limited and do not change, i.e. they are framed. However, in everyday situations, like getting a midnight snack, enormous amounts of detailed information are required. Furthermore, an agent could believe all that it needs to believe in an empirical matter and still be unable to represent it in the right way/to make use of it (Dennett, p. 194). Hence, what must be known is also relative to the situation at hand. We thus must also on the fly be able to use and update the information in a changing and dynamic world. This hints at the question of relevance, which is an issue both for which information needs to be called upon and just as importantly which information is irrelevant to the situation, but also what information needs be updated: if I move around in the room, my position and relation to my surroundings change, but which of these changes are relevant is exactly the frame problem. The syntactic problem then regards the logic of how information is stored. This means we run into problems both with regards to storing all those bits of information, but also how a system calls upon only the relevant bits of data in a fitting order, without the processing taking forever. GPS would calculate the best next move on a game board or which logical move will move it close to a desired state, but we run into the problem of relevance and/or machine processing power if the system is to calculate all possible moves and results in a world of limitless possibilities.
Dennett, Daniel C. (1984) “Cognitive Wheels: The Frame Problem of AI” in: C. Hookway (ed.) Minds, Machines and Evolution. Cambridge University Press.
Mitchell, Melanie. (2019) Artificial Intelligence. Farrar Strauss & Giroux. Chapter 1: “The Roots of Artificial Intelligence”
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