Drawing a real case DMN asset may become a time-consuming activity. In some domains, the possible types involved in DMN logic can explode into dozens or even hundreds of possible involved objects. Although a well-designed UI can support users to define your domain object type in a simpler and faster way possible, other alternative strategiesRead more →
In this blog post we’re going to explore an integration between the Drools DMN Engine and another open source project from IBM: "Open Prediction Service" (OPS). Introduction Integrating symbolic AIs (rule engines, KRR, etc) with Machine Learning predictive models is an effective strategy to achieve pragmatical, and often more eXplainable, AI solutions. We have alsoRead more →
Defeasible reasoning [1] is a field of interest in both Philosophy and Computer Science (the subdiscipline of Artificial Intelligence (AI)). While the philosophical history of the field goes back to Aristotle, AI has only shown interest in it over the last 40 years. What is called nonmonotonic reasoning in AI is roughly the same asRead more →
In this short update, I want to share with you about an experimental feature to leverage the extensibility of the DMN specification to evaluate expressions using a plurality of expression languages, such as JavaScript, Power Fx, and potentially many more! For the running example in this post, let’s use the Body Mass Index (BMI) calculationRead more →
This is a second iteration of a previous post, where we implemented EIP patterns using just Drools and Apache Camel. In this post instead, I want to share with you how to implement a complete, end-to-end Content Based Routing solution using Quarkus as a developer platform, including: Drools DMN Engine, Kogito, Apache Camel, AtlasMap andRead more →
Drools is a hybrid rule engine, allowing both data-driven forward chaining (rules match facts in the working memory producing other facts that in turn activate other rules) and goal-driven backward chaining (queries match facts in the working memory, eventually invoking other queries to retrieve them). This second usage pattern is also available in streaming modeRead more →
Decision Model and Notation (DMN) is designed to catch the crucial business logic which affects decisions. It’s not meant to be a replacement for a general purpose programming language. FEEL is a powerful expression, but it is on purpose designed to be an expression language and not an imperative language to avoid bringing in theRead more →
In this post I want to share an interesting use case of data enrichment, using DMN with BPMN and other open standards. The typical usage pattern for data enrichment is the following: a complex data structure containing several attributes is provided as input; based on some computations and decision results, the original structure is enrichedRead more →
As part of our effort to make Kogito usable in an event-driven fashion, this article introduces a new addon: the event-driven rules addon. It is available since Kogito v1.12.0 and its behavior resembles what the event-driven decisions addon (presented here) already does for decisions. Key concepts The new addon enables the evaluation of DRL ruleRead more →
In my last post I described the principles guiding the design of the new programmatic API. As I promised last time, in this blog post I would like to give an overview of new API capabilities that this new design would enable. One downside of having an API that is tightly-coupled with the implementation ofRead more →
