Operational business problems can be defined by a set of decision variables and a set of rules that specify relationships between these variables – see the formal definition. This definition considers a decision as a solution of such a problem, but it doesn’t assume anything about ‘HOW’ how decisions will be produced. It means decisions can be found by applying any rule engine, a DMN engine, a constraint or MIP solver, a custom piece of software written in any programming language, a manually provided expert’s decision, or their various combinations. Continue reading
When I learned that the famous Prof. Raymond Smullyan passed away this February at the age of 97, I felt grateful to the man whose books and puzzles my friends and I enjoyed reading as young programmers many years ago. Later on we shared them with our children. I wanted somehow to mark this event and decided to buy his book “The Chess Mysteries of Sherlock Holmes” to read on vacation. Ten days ago I started to read the book during my flight from Newark to Jamaica and… haven’t even noticed as we landed. Continue reading
Question: Should we worry that we’re building systems whose increasingly accurate decisions are based on incomprehensible foundations?
I’ve just posted an article with the same name at the LinkedIn Pulse that addresses this question. It is especially important in the context of rules-based decision making when rules that govern our decisions have been automatically generated using predictive analytics techniques. I shared two examples from OpenRules experience that explain why the automatically generated business rules should be comprehensible. The first one talks about the use of our Rule Learner at IRS. The second example deals with our Rule Compressor.
Contradictory business rules occur in normal business situations, and maintaining rules with exceptions is a very typical example of rule conflicts. Is it possible to automatically resolve such conflicts? Continue reading
“The Sleep of Reason Produces Monsters”, Francisco Goya
Defeasible Logic and Business Rules with Probabilities
Modern rules and decisions management systems provide effective mechanisms for development of good decision models. However, building real-world decision models people always face complex issues related to diagnostic and resolution of rule conflicts. Some systems can effectively verify decision model consistency and diagnose rule conflicts. But there are no practically used Business Rules (BR) products that claim that they may automatically resolve rule conflicts.
In the Part 1 of this series I described how end users can represent their rules in single-hit and multi-hit decision tables while avoiding rule conflicts. But is it possible to automatically resolve rule conflicts? I will discuss this problem below. Continue reading
Representing Contradictory Rules with Single-Hit and Multi-Hit Decision Tables
Modern rules and decisions management systems provide effective mechanisms for development of good decision models. However, building real-world decision models people always face complex issues related to diagnostic and resolution of rule conflicts. Some systems can effectively verify decision model consistency and diagnose rule conflicts. But there are no practically used Business Rules products that claim that they may automatically resolve rule conflicts (at least I am not aware of them). As a result, it becomes a responsibility of users to represent rules in such a way that allows them to avoid conflicts. Continue reading
The integration of different decision making techniques finally is finding its home under the roof of the Decision Management movement. I am glad that an integrated Constraint Programming (CP), Business Rules (BR), and Machine Learning (ML) approach is gaining in popularity as well. An interesting workshop “CoCoMiLe 2013 – Continue reading
On a long flight back to the US I had a few hours to kill. So, I decided to implement one of my favorite modeling tests that I used to give to my students and they always enjoyed it. This time I wanted to try it myself with the newest OpenRules Decision Modeling facilities (see Rule Solver).
Virtual Chess Tournament
Three world champions Fischer, Kasparov, and Karpov played in a virtual chess tournament. Each player played 7 games against two other opponents. Each player received 2 points for a victory, 1 for a draw, and 0 for a loss. We know that Kasparov, known as the most aggressive player, won the most games. Karpov, known as the best defensive player, lost the least games. And Fischer, of course, won the tournament. Continue reading
The famous RETE algorithm was invented by Dr. Charles Forgy more than 30 years ago and it still remains the foundation for most implementations of inferential rule engines. Recently Carole-Ann asked the question: why after all these years there were no practical alternatives to RETE? Continue reading
“Reality is built in wonderful simplicity”, Eliyahu Goldratt “The Choice”
Scheduling and Resource Allocation are traditionally considered as very complex business problems. They are usually out of reach for the most rule engines. I personally learned how to deal with these complex problems during my real-world consulting practice by applying a great product called “ILOG Scheduler” written by Claude LePape and Jean-Francois Puget 20 years ago. I’ve just googled the product name and got this User Manual that has over 600 pages with a lot of C++ code. I used to teach ILOG Solver/Scheduler courses and will reuse some examples borrowed from them. Continue reading
The latest rules conferences RulesFest-2011, BBC-2011, and RuleML-2011 were really great events in general and for OpenRules in particular. We announced a new constraint-based Rule Engine that is the first alternative to Rete-based implementations of a real inferencial rule engine. Continue reading
“OpenRules have the most-aggressive approaches to business-expert authoring and typically requires less developer support than IBM ILOG, FICO Blaze Advisor, and JBoss BRMS.” Continue reading
Being in real-world production environment for many years, OpenRules Engine has a proven record of high efficiency and scalability. Several years ago some of our customers (a major European bank and a large government agency) assigned teams of people to do stress-testing of our product before they decided to use it instead of commercial counter-parts. The results were really good. Continue reading