Monday, April 4, 2011

Intelligent Computer Chess, Part VIII



I apologize for taking so long to post this new installment. In Part VII, I explained how the memory builder builds permanent seven-node sequences in memory. Every sequence can itself be a node in a higher level sequence in the tree of knowledge. In this post, I explain the difference between short and long-term memory and the mechanism of motor behavior in Animal.

Memory as Recording Medium

Memory is a neural medium for recording signal sequences. Unless there is a pre-built neural structure that is ready to record a specific sequence of events, it cannot be recorded.
A five-node sequence
This is the reason that a written word (e.g., apple), shown to someone who cannot read English, will not be remembered as a word. The reason is that the high-level neural structures that are needed to recognize and record the word have not yet been constructed. Thus the memory system must anticipate potential event sequences and build the corresponding neural structures, otherwise there can be no learning and no remembering. It can do this by forming as many possible sequences as it can at every level of the memory hierarchy. Obviously, this can quickly become prohibitive, as the number of possible sequences is astronomical. There has to be a way of restricting the number of potential sequences in memory without the system running the risk of missing something important. This is a major problem, one that explains, in my opinion, why animals are not as intelligent as humans. I'll get back to this topic in a future article.

Short-Term Memory vs. Long-Term Memory

Short-term memory (aka attention or working memory) is whatever branch of the tree of knowledge is currently active. It is called short term because, unless it is reinforced by new sensory inputs, the branch can only remain active for a short period, about twelve seconds in the human brain.

The active branch has an initially high attention strength that slowly diminishes. Eventually its strength weakens to the point where the branch is deactivated and is replaced by another branch. However, an active branch can be preempted by another branch even before it has run its course. The reason for this has to do with survival. For example, you don't want your brain fixated on the little kitty on your lap while a lion is roaming nearby. The roar or the sight of the lion should wake up an appropriate branch in your tree of knowledge and put the kitty branch to sleep, otherwise you run the risk of being killed and that would be a bummer.


Having a robot with the ability to remember things is great but unless it can use this ability to interact with its environment, it's not going to do it much good. Sooner or later, we must ask ourselves, how do we use the tree of knowledge to move our muscles? Although the solution may turn out to be easy, this is not an easy question to answer. First of all, before we can even think of a proper way to generate motor signals, we need to understand how the motor system works. I found out over the years that complementarity (the use of opposites) is the key to solving every hard problem in intelligence research.

On the one hand, we have positive and negative sensors that detect the onset or offset of sensed (input) phenomena; and on the other, we have positive and negative effectors that trigger (or effect) the beginning and end of motor (output) phenomena. An effector is a special neuron that controls an actuator (e.g., a muscle or a valve). An actuator has two modes; it can be either activated (started) or deactivated (stopped). A positive effector starts an actuator while a negative effector stops it. For example, in Animal, there are separate effectors that commands the Eye to move to the left, right, up and down. Other effectors move the gripper in various directions and still others cause it to grab or release a chess piece.

Complementarity and Motor Coordination

An actuator must have at least two control effectors, a starter and a stopper. However, since actuators are limited in number, they must be shared by different parts of the intelligent system. As a result, an actuator may receive command signals from an indefinite number of effectors. The problem is that we run the risk of having conflicting motor signals if the system attempts to accomplish different tasks at the same time. We obviously do not want different parts of the brain to try to use the same set of effectors at the same time. It turns out that complementarity is the key to properly design a well-coordinated motor system. The rules of motor coordination are:
  1. An actuator must not receive more than one command signal at a time.
  2. An actuator must not receive a start command if it is already activated.
  3. An actuator must not receive a stop command if is already deactivated.
These amazingly simple rules are the basis of intelligent motor behavior and motor learning in the brain.
Note: Let me take this opportunity to remind my readers that I developed my understanding of the brain's memory and motor systems almost entirely from my interpretation of certain symbolic (occult) passages in the Bible, especially the metaphorical letters to the seven churches of Asia in the book of Revelation and Zechariah's vision of the seven-lamp golden lampstand, the branch and the two olive trees. Again, if you have a problem with my Biblical research or if you believe that I'm some kind of nut, then my blog is not for you. I write only for kindred spirits, sorry.
Coming Up: Connecting the Tree of Knowledge to the Motor System

Now that we know what the motor system expects from us, we have enough understanding to figure out a way to use the tree of knowledge to generate intelligent motor behavior. I will cover this topic in Part IX.

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