My assumption: your system has this agent/guy moving around a home/office environment. [Adjust question accordingly if not] He has a simple task: "Move from A to B or D". But the normal answer "Walk it" is for whatever reason no good, blocked. The simple test is this: how many alternative ways of moving from A to B, will the system (his brain) be able to search for and find? [Sub-questions: how will alternatives be laid out in memory, and how will the system search for them?] That's the basic test of adaptivity : HOW MANY ALTERNATIVE WAYS OF ACHIEVING ANY GOAL CAN A SYSTEM FIND?
Oh, Novamente will find a hell of a lot of different ways of achieving a simple goal like that, even in its current form... Its two main learning modules -- evolutionary learning and probabilistic inference -- are both quite good at diversity generation... But actually, your question bespeaks a certain unfamiliarity with standard GOFAI type technology. Generating a lot of different alternatives and then pruning down the space is pretty standard stuff. What is harder is as follows: if goals G1 and G2 look to be related, and the system has learned a bunch of ways to fulfill G1; then, this latter fact should make it easier for the system to find ways to fulfill G2 (than if it hadn't learned ways to fulfill G1 already). This is known as "transfer learning" and has proved more challenging for AI systems than simply generating diverse plans for the same goal. Because you can generate diverse plans without arriving at a deep understanding of the goal and the space within which it is situated; but transfer learning, except in lucky cases, requires real insight... Ben G ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415&user_secret=fabd7936
