Featured Posts: AI

TrustyAI meets Kogito: decision monitoring

In this article, we introduce the metrics monitoring add-on for Kogito. This add-on is part of the TrustyAI initiative already introduced in the previous article https://blog.kie.org/2020/06/trusty-ai-introduction.html . Like Quarkus extensions, the Kogito add-ons are modules that can be imported as dependencies and add capabilities to the application. For example, another add-on is the infinispan-persistence-addon thatRead more →

Trusty AI Aspects

As mentioned in the previous blog post we have three aspects that we are implementing: explainability, runtime and accountability. However we need to see how these are connected with the use cases and personas. The first aspect we will consider is runtime tracing and monitoring. The term monitoring refers to the system overseeing performance orRead more →

Trusty AI Introduction

Have you ever used a machine learning (ML) algorithm and been confused by its predictions? How did it make this decision? AI-infused systems are increasingly being used within businesses, but how do you know you can trust them?  We can trust a system if we have confidence that it will make critical business decisions accurately.Read more →

Introducing jBPM’s Human Task recommendation API

In this post, we’ll introduce a new jBPM API which allows for predictive models to be trained with Human Tasks (HT) data and for HT to incorporate model predictions as outputs and complete HT without user interaction. This API will allow you to add Machine Learning capabilities to your jBPM project by being able toRead more →

Webinar: Re-imagining business automation: Convergence of decisions, workflow, AI/ML, RPA — vision and futures

WEBINAR  Title: Re-imagining business automation: Convergence of decisions, workflow, AI/ML, RPA—vision and futures Time: June 20, 2019, 5:00 p.m. BST (UTC+ 1) Registration https://www.redhat.com/en/events/webinar/re-imagining-business-automation-convergence-decisions-workflow-aiml-rpa%E2%80%94vision-and-futures

drools.js: Towards a Polyglot Drools on GraalVM (with Bonus Tech-Lead Prank)

Image courtesy of Massimiliano Dessì You can find the full source code for this blog post in the submarine-examplesrepository. Different programming languages are better for different purposes. Imagine how hard would it be to query a database using an imperative language: luckily, we use SQL for that. Now, imagine how useless would a rule engineRead more →

New feature overview : PMML

Today, I’ll introduce a new 6.1 experimental feature, just being released from our incubator: Drools-PMML. I’ll spend the next days trying to describe this new module from the pages of this blog. Some of you, early adopters of Drools-Scorecards, will probably have heard the name. Thanks to the great work done by Vinod Kiran, PMMLRead more →

Scorecards and PMML4.1 support for Drools 5.5

Thanks to our super star community contributor, Vinod Kiran, score cards are coming to Drools 5.5. Initially the PMML4.1 standard is embedded for the Scorecards module. We have a full standalone PMML implementation coming for 6.0, being worked on by Dr Davide Sottara. I hope that Vinod will write a full tutorial in this blogRead more →

Mythic Game Project Addition Artificial Intelligence and Quest System Components (Christopher Alan Ballinger)

Google Alerts brought this extensive masters paper to my attention, by Christopher Alan Ballinger. The paper explains what expert systems are and has a lot of DRL examples to follow.http://www.csci.csusb.edu/turner/690/example_projects/chris_ballinger_masters_project.pdf I’d be interested to see this code published online for others to play with. “In this project, we describe the design decisions and principles behindRead more →