PMML revisited

Hi folks! The beginning of this year brings with it the initiative to re-design the Drools PMML module. In this post I will describe how we are going to approach it, what’s the current status, ideas for future development, etc. etc so… stay tuned! Background PMML is a standard whose aim is to "provide aRead 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

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. 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 →

Machine learning – and Apache Mahout

Isabel Drost recently contributed some enhancements to the Guided Editor (to allow nested facts, very handy) – quite a clever patch. As if that isn’t enough, she is also a contributor to the Apache Mahout project:Mahout is: (in the projects own words): “Mahout’s goal is to build scalable, Apache licensed machine learning libraries.” The projectRead more →

Drools and Machine Learning

I’m Gizil. I am doing my master thesis in Drools project. I’m working on decision trees. I have made an ID3, C4.5 implementation with rule generation. I’m investigating bagging and boosting algorithm in order to produce better rules. I am using Annotations on object fields to be able to process extra information on the attributes ofRead more →