reasoning about approximations in symbolic AI
Frank van Harmelen seems like an interesting person
"Groot, Ten Heije, van Harmelen - Towards a Structured Analysis of Approximate Problem Solving: a Case Study in Classification
The use of approximation as a method for dealing with complex problems is a fundamental research issue in Knowledge Representation. Using approximation in symbolic AI is not straightforward. Since many systems use some form of logic as representation, there is no obvious metric that tells us `how far' an approximate solution is from the correct solution.
This is an issue in the philosophy of science, in particular the issue of how reliable simulations are: how much will errors spread? In terms of inference, I think of a simulation as a large chunk full of deductions with a few (false) auxiliary assumptions thrown in. Ideally, we would use the false assumptions as little as possible, but the reason we make those assumptions in the first place is because analytical solutions are intractable.
"Groot, Ten Heije, van Harmelen - Towards a Structured Analysis of Approximate Problem Solving: a Case Study in Classification
The use of approximation as a method for dealing with complex problems is a fundamental research issue in Knowledge Representation. Using approximation in symbolic AI is not straightforward. Since many systems use some form of logic as representation, there is no obvious metric that tells us `how far' an approximate solution is from the correct solution.
This is an issue in the philosophy of science, in particular the issue of how reliable simulations are: how much will errors spread? In terms of inference, I think of a simulation as a large chunk full of deductions with a few (false) auxiliary assumptions thrown in. Ideally, we would use the false assumptions as little as possible, but the reason we make those assumptions in the first place is because analytical solutions are intractable.