Rule-based AI is lagging behind
The distinction between rule-based AI and corpus-based AI makes sense in several respects since the two systems work in completely different ways. This does not only mean that their challenges are completely different, it also means that as a consequence, their development trajectories are not parallel in terms of time.
In my view, the only reason for this is that rule-based AI has reached a dead end from which it will only be able to extricate itself once it has correctly identified its challenges. This is why these challenges will be described in more detail below.
Overview of the challenges
In the preceding post, I listed four challenges for rule-based AI. Basically, the first two cannot be remedied: it takes experts to draw up the rules, and these must be experts both in abstract logic and in the specialist field concerned. There is not much that can be changed about this. The second challenge will also remain: finding such experts will remain a problem.
The situation is better for challenges three and four, namely the large number of rules required, and their complexity. Although it is precisely these two that represent seemingly unalterable obstacles of considerable size, the necessary insights may well take the edge off them. However, both challenges must be tackled consistently, and this means that we will have to jettison some cherished old habits and patterns of thought. Let’s have a closer look at this.
The rules require a space and a calculus
Rule-based AI consists of two things:
- rules which describe a domain (specialist field) in a certain format, and
- an algorithm which determines which rules are executed at what time.
In order to build the rules, we require a space which specifies the elements which the rules may consist of and thus the very nature of the statements that can be made within the system. Such a space does not exist of its own accord but has to be deliberately created. Secondly, we require a calculus, i.e. an algorithm which determines how the rules thus established are applied. Of course, both the space and the calculus can be created in completely different ways, and these differences “make the difference”, i.e. they enable a crucial improvement of rule-based AI, albeit at the price of jettisoning some cherished old habits.
Three innovations
In the 1990s, we therefore invested in both the fundamental configuration of the concept space and the calculus. We established our rule-based system on the basis of the following three innovations:
- data elements: we consistently use composite data elements (concept molecules);
- space: we arrange concepts in a multidimensional-multifocal architecture;
- calculus: we rely on non-monotonic reasoning (NMR).
These three elements interact and enable us to capture a greater number of situations more accurately with fewer data elements and rules. The multifocal architecture enables us to create better models, i.e. models which are more appropriate to their situations and contain more details. Since the number of elements and rules decreases at the same time, we succeed in going beyond the boundaries which previously constrained rule-based systems with regard to extent, precision and maintainability.
In the next post, we will investigate how the three above-mentioned innovations work.
This is a post about artificial intelligence.
Translation: Tony Häfliger and Vivien Blandford