Abstract
The major premise of deterministic artificial intelligence (D.A.I.) is to assert deterministic self-awareness statements based in either the physics of the underlying problem or system identification to establish governing differential equations. The key distinction between D.A.I. and ubiquitous stochastic methods for artificial intelligence is the adoption of first principles whenever able (in every instance available). One benefit of applying artificial intelligence principles over ubiquitous methods is the ease of the approach once the re-parameterization is derived, as done here. While the method is deterministic, researchers need only understand linear regression to understand the optimality of both self-awareness and learning. The approach necessitates full (autonomous) expression of a desired trajectory. Inspired by the exponential solution of ordinary differential equations and Euler’s expression of exponential solutions in terms of sinusoidal functions, desired trajectories will be formulated using such functions. Deterministic self-awareness statements, using the autonomous expression of desired trajectories with buoyancy control neglected, are asserted to control underwater vehicles in ideal cases only, while application to real-world deleterious effects is reserved for future study due to the length of this manuscript. In totality, the proposed methodology automates control and learning merely necessitating very simple user inputs, namely desired initial and final states and desired initial and final time, while tuning is eliminated completely.
Highlights
Artificial intelligence is most often expressed in stochastic algorithms that often have no knowledge whatsoever of the underlying problem being learned
Deterministic artificial intelligence (D.A.I.) requires a self-awareness statement derived from the governing physics followed by some type of learning
The assertion of self-awareness statements is validated with several maneuvers, while the validating simulations with optimal learning are expressed in the presentation of the empirically derived relationship between maximum available actuation versus achievable minimum maneuver time
Summary
Artificial intelligence is most often expressed in stochastic algorithms that often have no knowledge whatsoever of the underlying problem being learned (a considerable strength of the methods). The field of non-stochastic or deterministic artificial intelligence breaks from this notion by first asserting the nature of the underlying problem using a self-awareness statement that permits the controlled item to have knowledge of itself, and this assertion allows the controlled item to learn in reaction to the environment. An unmanned vehicle with a deterministic self-awareness and learning can respond to significant damage that removes substantial vehicle parts or instead increases the vehicle’s math models via inelastic collisions (e.g., bird strikes on aircraft, or robotic capture for spacecraft and underwater vehicles). An original Dartmouth summer workshop attendee from MIT, Marvin Minsky, was so enthusiastic to state in writing, “within a generation . An original Dartmouth summer workshop attendee from MIT, Marvin Minsky, was so enthusiastic to state in writing, “within a generation . . . the problem of creating ‘artificial intelligence’ will substantially be solved” [9,11,12], clearly failing to acknowledge some very difficult challenges
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