Mark, I was objecting to the fact that your paper did not even mention, much less
stress, the fact that prediction of previously unseen things is critical to intelligence.
It's true that I don't talk about the prediction of "previously unseen" things. I suppose I just take it for granted. I mean, if I predictor can't generalise from past experience to predict patterns that have not been seen yet, but are in some way related to the past, then it's a pretty poor predictor! Perhaps this illustrates something more important however: One of the problems with defining intelligence is that it seems to involve an endless range of abilities. Some argue that perception is very important. Some that language is key. Others think that mathematical thinking, or probabilistic reasoning, or rational thought are very important. Perhaps planning is very important. Maybe the ability to abstract. Emotions? And so on... The beauty of the approach that we have taken is that we abstract above all this and just talk in terms of goal achieving performance. Thus things such as planning, reasoning or prediction are important to the extent that they enable the agent to achieve goals. If they don't enable the system to work better in some environment, then they aren't a part of intelligence. Not only does this allow us to not have to name what each of these things are, it also means that the intelligence test measures cognitive abilities that may enhance an agent's performance that we have not yet thought of. I agree with the first sentence of your second paragraph entirely but point
out that a "by rote" machine with virtually infinite experience will test as if it had high universal intelligence unless the test manages to hit upon some area where it didn't have experience -- and I feel that this is entirely incorrect.
Actually this is another nice feature of universal intelligence: An agent can't really get a better score by having a large database of prior knowledge. The problem is that if you put some information into the agent's database, "the grass is green and the sky is blue" then this will help the agent in a world where this is true. However, there will be another world with about the same complexity where the sky is green and the grass blue. The agent has no idea which of these two worlds it is going to face. This makes a database of prior experience is useless. The only way that the agent can do well is by quickly learning from its experience and adapting to deal with the uncertainties in its environment. Unfortunately, in the 8 page Benelearn paper there wasn't space to get into many of these interesting aspects of this intelligence test. Cheers Shane ----- 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
