We apply two AI methodologies to the needs of trucking. The first approach is what is known as statistical AI, or more popularly as machine learning. It is premised on the idea that a large volume of historical, current, and future data has patterns of importance. The software discovers those patterns, and uses them to predict key outcomes. It is called machine learning because greater experience improves the predictive power of the software.
The second approach is known as AI Planning. Planning does not necessarily require learning from experience (although that can help). Instead of focusing on learning from experience, AI planning is pursued by accurately describing the state of the world, the actions available, and goals we want to achieve. With that, the AI acts as a rational agent trying to achieve goals with allowed decisions and with an expectation of how the world will react to them. Human involvement in AI planning is meant to act as a gatekeeper on final decisions. People are said to be in, on, or out of the decision making loop depending on if they approve decisions, can only halt the process entirely, or are strictly observers.
The diagram above shows how our AI is architected to leverage both methods. The planning side is about producing logically superior ways to use a truck. It ends with what we call “bundles”, which are specific ways to schedule, consolidate, sequence, and therefore execute a combination of transport orders (what we call Cargo Orders). But a rationally good bundle still needs to be qualitatively attractive to the person who decides to accept (or reject) it. These are dispatchers who control the private fleet, or are in another company altogether and deciding what each carrier will do with its trucks. The fact the staff are in different companies mean they have mis-aligned incentives and will not want to disclose all their information about capacity, price sensitivity, and so forth. Also, behavioral economics shows that most people are not aware of their own true preferences and these are only revealed indirectly. Machine learning is much better than rational planning in this area. Machine learning is used to construct tenders for each bundle to a specific carrier. It is only the tender that the carrier can see, not the original cargo or the bundle.
Together, the two AI methods are applied “in the middle”, between the cargo order from the shipper and the tender given to the carrier. If TNX does its work right, the two sides don’t see the complexity of the AI, just the quality of its results.