An adaptive probabilistic mapping2 or actor interacts with an application program or environment by means of exchanging signals. The input and output signals of the actor can be the arguments of the adaptive probabilistic mapping. The output signals3 of the actor are the outcomes of the adaptive probabilistic mapping.
The actor ordinarily receives the input signals, spur (see Spur-driven Behavior) increments, and time increments and emits the output signals with the goal to maximize spur increment or decrement velocity. To achieve that goal, the actor performs basic forecasting of spur increments resulting from emitting various output signals. The author assumes that by combining multiple actors or applying other approaches it is possible to amplify this forecasting capability to create a system intelligently interacting with an environment of real-world complexity.
|• Event History|
|• Output Signal Selection|
|• Small and Large Actors|
|• Creating an Actor|
|• Repeated Sequence of Operations|
|• Customizing the Relative Probability Function|
|• Specifying Output Signal Weights|
|• Automatic Spur|
|• Switching Adaptive or Random Behavior of an Actor|
|• Revising Action Choice States|
|• Example of Using the Actor API|
The old term for “adaptive probabilistic mapping” used before QSMM 1.17 was “optimal action generation engine”.
A deprecated synonym for the term “output signal” used before QSMM 1.17 was “action signal”.