Symbol grounding problem as answer to np hard algorithms

 Before its possible to describe grounded language there is a need to explain who artificial intelligence was imagined until the year 1990. It was treated similar to computer programming in the sense that there is a CPU which executes a program and its up to the programmer to make the algorithm as intelligent as possible. Artificial intelligence was thought as a very advanced computer programmed which is executed by a computer.

In other terms, the computer was seen as a problem solving machine and the only detail problem was which sort of algorithm is needed to solve a certain problem. For example motion planning in robotics was solved with motion planning algorithms while computer chess was solved with alpha beta prunning algorithms. Most of these AI related algorithms were designed as search algorithms. The computer was used to traverse the state space of the domain and this allowed the computer to find the optimal action.

The symbol grounding problem formulated by Stevan Harnad questions this algorithm oriented paradigm. This might explain why even today grounded language is a niche topic within computer science. Because computer science and algorithms were often treated as the same thing, it was outside of the scope how to program a computer without an algorithm.

Let us listen closely how Harnad, Brooks and Steels are arguing about grounded language. The core element is the sensory perception of a robot. The assumption is that the perception is transmitted to the computer. There is no need to calculate something but the focus on the data transfer. A light sensor detects light and the information from the sensor is send over a cable to the computer. The symbol grouding problem doesn't focus on the computer itself, but on the cable between a sensor and a computer, very similar to a computer network. Computer networks are different from a turing machine, they are never running algorithms, but a computer network communicates data often organized in a protocol layer.

The paradigm shift from algorithm centric computers towards protocol oriented data transmission is the core element of the symbol grounding problem. Artificial Intelligence isn't explained as processing or program executation, but Artificical Intelligence is imaged as the air gap between two hosts.

Let us compare the hardware. In classical algorithm oriented AI the basic building block is a central processing unit, which can be a 32bit CPU. The CPU is built with transistors on a chip and gets controlled by Assembly language. In contrast, the symbol grounding problem assumes that there is a Cat5 copper cable which delivers packets. Its up to the network engineer to define the protocol of the packets.

The paradigm shift can be explained for np hard problems. NP hard is a certain category of problems related to artificial intelligence which can't be solved with a computer. Nearly all robotics motion planning problems like the piano movers problem or model predictive control are np hard. The term np hard is referencing to the runtime of an algorithm executed on a cpu. In other words, even a modern 64bit CPU can't solve these problems because the hardware is too slow.

The holy grail in computer science is how to solve np hard problems. The answer was given by Stevan Harnad in his famous 1990 paper. He didn't mentioned np hard problems, but its possible to solve np hard problem with grounded language. Instead of using a CPU to calculate a mathematical problem, a copper cable is used to solve a data transmission problem. This new perspective is powerful enought to solve motion planning problems in robotics.