ABSTRACT. This industry track invites you to discuss the interaction between researchers, industry, AI and rules. We see that industry often need more simple solutions than offered by researchers but they need to be full proofed and that is much harder to achieve than a first prototype. But some processes in the industry are too difficult for people. People need to guess or 'don’t know': in those cases even very small improvements that increase forecasts from random (50%) to anything above 50% may already be a huge time and money saver for the industry. Researchers are often after bigger improvements. To apply a technology one needs common sense, domain expertise and a deep understanding of its working (benefits and limitations). When we talk about AI (the technology is an algorithm) and todays hype market, we expect to see many failures due to a lack of this insight. There is a challenge for researchers in the ruleML community to make sure people understand how AI works.

ABSTRACT. An example of a simple solutions that need to be full proofed is the rule language that is developed by the Dutch tax administration to express the rules for the income tax calcultation. Lawyers are being used to unconstrained natural and programmers are used to a procedural rule language. To process to get from one to the other was long and error prone. A new declarative language is replacing that process and allows the customer to gradually modernize their legacy calculation engines. This case shows again that the development of the rule language is just a very small thing in all steps for an organization to accept a new way of working.

ABSTRACT. An example of a simple solutions that need to be full proofed is the rule language that is developed by Isis Paparus to configure the workflow in their document management software. The business user defines general constraints in a controlled natural lanugage, such as "a customer must have an account manager" and "a claim must be assessed" to guide the workflow. The process of defining the language, limit the scope and supporting it in the user interface and training the customers shows the complexity of bringing a first prototype to production. Isis Papyrus also uses reinformcement learning to teach the software and the user what 'the next best step' in the process is based on past performance. This simple solution already works for years and is very popular.

ABSTRACT. Being a professional in IT, authority in business rules and specialist in artificial intelligence,  I have seen many initiatives in the industry to create  decision support systems and rule languages. Unfortunately many of them failed. This is not because the technology failed, but because there need to be many other business factors pointing in the right direction for a change initiative to succeed. We will discuss what research is needed to increase the succes factor of rule languages.