Conditional AI

  1. The basic for this project is the concept that Intelligence, or rather the decision making part of Intelligence, can be broken down to a simple series of conditions.
  2. These conditions are mostly dependant (condition parameters depend on results of previous condition), meaning that long term memory is also required.
  3. There are many fallback conditions, which are consulted when encountering new situations.
  4. The system must be able to learn new conditions, either by teaching it, or by way of self-learning from the results of the decisions, which requires the system to be able to get feedback about it's decisions.
  5. in order for a decision to be made, a set of given parameters must be passed to the system, which will analyze them according to it's conditions DB.
  6. The parameters can include actions done to the system(someone talking to it, someone hitting it, etc.) and/or events that happened around it (the light went off, someone hit someone else, etc.). they may include information about the surrounding environment, the object interacted with, and any other information.
  7. It is up to the system to take the parameters and search for the most suitable condition that match those parameters, and start following the line of conditions until a decision is made. This is better clarified by an example: