Before you can solve general and difficult problems, it is often useful to solve toy versions of the same problems with many of the parameters simplified, or on

Before you can solve general and difficult problems, it is often useful to solve toy versions of the same problems with many of the parameters simplified, or only focusing on individual aspects of the full problem at a time. It would be incredibly difficult to attempt to solve the general problem of having an AI making decisions in the world, including taking into account the goals and mind-states of other humans (and general agents). Much more practical to restrict the problem space very narrowly - can you build something which can learn to strategize and predict future outcomes in a difficult, full-information competitive game? (Go) Okay, can it do something similar without complete information, and with less discrete-looking steps and game states (1v1 Dota) - can it do so whilst cooperating with other similar agents with asymmetric abilities but common knowledge to achieve a common goal? (5v5 Dota) Okay, now can it cooperate with other agents with symmetric abilities but asymmetric knowledge, and both convey necessary knowledge to other agents via its actions, as well as deduce knowledge from the actions of other agents? (Hanobi) These are all necessary steps on the path to creating a general AI - if your AI is not at least able to do the above, it definitely won't be able to navigate a general environment. In the absence of an obvious alternative strategy, it seems to me the most productive approach is to tackle the small, important-but-doable seeming problems, one at a time, until hopefully you are able to unify them or else have a breakthrough of some sort.