The slow emergence of Artificial Intelligence

 AI and robotics was researched since decades. In contrast to other disciplines like computer science or mathematics there was no success available. Even if AI researchers have analyzed the subject from a scientific perspective and discussed the situation at conferences there were not able to identify major problems or offer possible answers. What was happen instead was a long disappointing journey.

Even if AI in the past suffered, lots of subjects were analyzed. Notable examples are: autonomous robotics, model predictive control, genetic algorithms, reinforcement learning, Turing maschines. All these subjects were seen as promising candidates towards the pathway to intelligent machinery. They can be called advanced subjects in computer science and many papers were written. The general idea was to describe intelligence as an optimization problem which can be measured with a score. For example, trajectory optimization tries to reduce the costs, while genetic algorithms are maximing the fitness of candidates. In both cases the computer is a device for solving a mathematical problem.

On the first look, it makes sense to describe robotics movement with model predictve control algorithms. It helps to translate a problem from the reality towards an abstract mathematical equation. The idea is, that artificial intelligence can be realized as a combination of computer science, mathematics and game theory. Most researchers in the past would agree, that such kind of interdisplinary approach is a sign of excellence and allows to discover future robotics algorithms. What the researchers in the 1990s and 2000s didn't know was that the describe workflow is a dead end. None of mentioned techniques lead to artificial intelligence.

Model predictive control is a good example for a dead end in robotics resarch. The subject was researched by multiple researchers independend from each other with a great effort. There is no obvious mistake in the equation nor in a certain paper about the subject. At the same time, the entire model predictive control research has to be called a dead end because it fails to control simple robots.

In the history of artificial intelligence such kind of dead end is not an exception but its default situation. All the other attempts to realize robotics like expert system, neural networks and 5th generation programming languages like Prolog have failed too. It seems that there was a need to explore all the non working principles to get a better understanding what sort of approach won't result into a working robot.

Ai research in the past was realized as an intersection of physics models, mathematical theories and computer science. The hope was that the combination of these powerful disciplines allows to create intelligent machinery. For example trajectory optimiziation has a background in theoretical physics, and can be implemented as an algorithm on a computer. This would allow to plan the movement of a robot.

What was unknown in the past or perhaps it was ignored was, that the state space in robotics is too large to use mathematical optimization problems. Predicting future states of a system is only possible if the system consists of a few variables e.g. in a predator-prey scenario modelled with Lotka–Volterra equations. Such a system can be calculated on a computer and future states can be processed in advanced. The concept fails on robotics domains like dexterous grasping or biped walking. The equations are not known or they are too complicated the calculate. Even if there are realistic physics simulators available like Box2D, its not possible to determine future states of these engines into the future.

Despite this pessimistic situation it makes sense to explore model predtctive control and other mathematical optimization techniques because it allows a better understanding of np hard problems. If its known, that the state space in robotics is too large, its possible to rethink about the situation and explore strategies how to reduce the state space. A state space reduction is the pathway to advanced robotics.