What types of options are "more" path dependent than others is not well-defined. The cited distinction -- full-path dependence versus single-point dependence -- is somewhat useful in the sense of estimating memory usage of a Monte Carlo pricer, but not so useful beyond that. Even American-exercise options are path-dependent, after all.
What I think is useful is the distinction according to most (asymptotically) efficient pricing scheme, such as
- $O(1)$ A closed-form option pricing formula exists (B-S)
- European
- Geometric-average asian
- Continuous lookback
- $O( N^{-4}) $ A 1-d quadrature (e.g. Simpson's rule) is needed (European stochastic vola)
- $O( N^{-1}) $ A 1-d grid scheme will suffice (American)
- $O( N^{-1} + M^{-1}) $ A 2-d grid scheme is needed (Arithmetic asian, Bermudan swaption)
- $O( N^{-\frac{1}{2}}) $ A 1-d Monte Carlo scheme is needed (Some exotics)
- $O(K N^{-\frac{1}{2}}) $ Multidimensional quadrature / Monte Carlo scheme is needed (Basket, CDO tranche protection)
This leaves out a lot of practitioner tricks. These include control variates, moment-matching for basket options, quasirandom sequences for quadrature, SABR-type approximations for rates, and LSMC for convertibles and tricky exotics.
Note it is very common, even for practitioners, to take advantage of the convenience of Monte Carlo and employ it in cases where it is not the most asymptotically efficient scheme. One can do Monte Carlo in a couple lines of code, but for quasirandom sequences et cetera there is more machinery involved. This is especially true of asian options, where the grid scheme is much more of a pain to write.