That makes much more sense. If your system consists of special-purpose subsystems (call them agents or whatever), then for some of them multi-dimensional space may be the best KR framework. I guess for the sensorimotor part this may be the case, as the works of Brooks and Albus show.
In this design, the tough job is to make the agents working together to cover all kinds of tasks, and for this part, I'm afraid that the multi-dimensional space representation won't help much. Also, we haven't seen much work on high-level cognition in that framework. Pei On 11/26/06, J. Storrs Hall, PhD. <[EMAIL PROTECTED]> wrote:
My best ideas at the moment don't have one big space where everything sits, but something more like a Society of Mind where each agent has its own space. New agents are being tried all the time by some heuristic search process, and will come with new dimensions if that does them any good. Equally important is to collapse dimensions in higher-level agents, forming abstractions. Let's say I'm playing tennis. I want to hit a backhand. I have an agent for each joint in my arm that reads proprioceptive info and sends motor signals. Each one of these knows a lot about the amount of effort necessary to get what angle or acceleration at that joint as a function of the existing position, tiredness of the muscle, etc, etc. This info is essentially an interpolation of memories. I have a higher-level agent that knows how to do a backhand drive using these lower-level ones, and it has a much more abbreviated notion of what's going on at each joint, but it does know a lot about sequencing, timing, and how far the ball will go -- also based on memory. I also have a forehand agent using the same lower-level ones, and so forth. It probably has a space very similar to the backhand one, but the warp and woof of the remembered trajectories in the space will be all different. At higher levels I have within-the-point strategy agents that decide which strokes to use and where to hit to in the opposite court. The spaces for these agents may have subspaces that map recognizeably to a 2-d tennis court, perhaps. Higher up I have an agent that knows how the game scoring works, in which most the dimensions are binary -- I win the point or my opponent does. Such a space boils down to a finite state machine. Chances are that in real life, I've been in a tennis game at every possible score, but I didn't have to -- I didn't build the state space for that agent purely from memory, indicating a more sophisticated form of interpolation. So the basic idea is like Minsky's or Brooks' or Albus' modular architectures but with interpolating n-space trajectory memories as each agent or module. I don't understand Hugo's architecture of Hopfield nets well enough to say whether it's equivalent or not; it could certainly match the performance but I couldn't say whether it could match the learning. --Josh On Sunday 26 November 2006 10:15, Pei Wang wrote: > On 11/26/06, Ben Goertzel <[EMAIL PROTECTED]> wrote: > > HI, > > > > > Therefore, the problem of using an n-space representation for AGI is > > > not its theoretical possibility (it is possible), but its practical > > > feasibility. I have no doubt that for many limited application, > > > n-space representation is the most natural and efficient choice. > > > However, for a general purpose system, the situation is very > > > different. I'm afraid for AGI we may have to need millions (if not > > > more) dimensions, and it won't be easy to decide in advance what > > > dimensions are necessary. > > > > I see no problem with using a sparse representation of dimensions in > > an n-vector K-rep approach, actually... > > It is not about the time-space cost, but how to choose the dimensions > (if they are determined in advance and remain constant), or how to > maintain consistency (if they are dynamically added and deleted). > > Pei > > ----- > 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/?list_id=303 ----- 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/?list_id=303
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