Last 2 weeks

Transparent ML, integrating Drools with AIX360

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Following up from a previous blog post about integrating Drools with the Open Prediction Service, in this new post we want to share the current results from another exploration work: this time integrating Drools with research on Transparent Machine Learning by IBM. Introduction Transparency is a key requirement in many business sectors, from FSI (FinancialRead more →

a DMN FEEL handbook

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We’re introducing an (experimental) DMN FEEL handbook, an helpful companion for your DMN modeling activities! You can access this new helpful resource at the following URL: https://kiegroup.github.io/dmn-feel-handbook. Key features include: FEEL built-in functions organised by category tested and integrated FEEL examples Responsive design: easily access on Mobile, Tablet and Desktop from your favourite browser! …andRead more →

Last 6 months

Kogito Rules (Drools) with Java Inheritance

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Comparison of a JSON array based approach vs Jackson Inheritance Annotations Introduction: “Kogito is a next generation business automation toolkit that originates from well known Open Source projects Drools (for business rules) and jBPM (for business processes). Kogito aims at providing another approach to business automation where the main message is to expose your businessRead more →

Serverless Drools in 3 steps: Kogito, Quarkus, Kubernetes and Knative!

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This short tutorial walks you through the configuration and deployment of a simple Drools serverless application, including autoscaling with scale to zero, thanks to Kogito, Quarkus, OpenShift Serverless with Kubernetes and Knative! Step 1: Drools app creation with code.quarkus.io To generate the application as shown in the video, you can use this link: https://code.quarkus.io/?e=org.kie.kogito%3Akogito-quarkus-decisions&e=resteasy-jackson&e=kubernetes&e=container-image-jib TheRead more →

Explaining Drools with TrustyAI

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Introduction to TrustyAI and Drools Explainability is a crucial aspect in modern AI and decision services work; recent laws entitle any person subject to automated decisions to explanations of the intuition behind said decisions. Moreover, people are more likely to trust the decisions of explained models compared to unexplained models (Kim et al., 2022). Furthermore,Read more →

Refactoring the Drools Compiler

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In the past few weeks we have been working hard on redesigning the architecture of Drools the rules engine and the rest of our ecosystem of runtime engines. In this blog post I want to focus a bit on the refactoring of the KnowledgeBuilder, a core component of the build infrastructure of the v6-v7 APIs.Read more →

Upgrade Drools version

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We sometimes see questions regarding Drools version upgrade. Most of those questions are about how to change the old API usage in Drools 5 or 6. In this article, I’m going to guide how to change your code to work with the latest Drools version (7.70.0.Final as of now). API If you are using oldRead more →

DMN Types from Java Classes

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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 →

Integrating Drools DMN Engine with IBM Open Prediction Service

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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 →