We need to split “search” into more fine-grained concepts.
For example, “model has representation of the world and simulates counterfactual futures depending of its actions and selects action with the highest score over the future” is a one notion of search.
The other notion can be like this: imagine possible futures as a directed tree graph. This graph has set of axioms and derived theorems describing it. Some of the axioms/theorems are encoded in model. When model gets sensory input, it makes 2-3 inferences from combination of encoded theorems + input and selects action depending on the result of inference. While logically this situation is equivalent to some search over tree graph, mechanistically it looks like “bag of heuristics”.
Chess tree looks like classical example. Each node is a boardstate, edges are allowed moves. Working heuristics in move evaluators can be understood as sort of theorem “if such-n-such algorithm recognizes this state, it’s an evidence in favor of white winning 1.5:1”. Note that it’s possible to build powerful NN-player without explicit search.
We need to split “search” into more fine-grained concepts.
For example, “model has representation of the world and simulates counterfactual futures depending of its actions and selects action with the highest score over the future” is a one notion of search.
The other notion can be like this: imagine possible futures as a directed tree graph. This graph has set of axioms and derived theorems describing it. Some of the axioms/theorems are encoded in model. When model gets sensory input, it makes 2-3 inferences from combination of encoded theorems + input and selects action depending on the result of inference. While logically this situation is equivalent to some search over tree graph, mechanistically it looks like “bag of heuristics”.
That’s interesting! What would be some examples of axioms and theorems that describe a directed tree?
Chess tree looks like classical example. Each node is a boardstate, edges are allowed moves. Working heuristics in move evaluators can be understood as sort of theorem “if such-n-such algorithm recognizes this state, it’s an evidence in favor of white winning 1.5:1”. Note that it’s possible to build powerful NN-player without explicit search.