Today, Adam DeJans Jr. wrote: “Every business operates with tolerance: delivery delays, budget overruns, staffing gaps. Good models price those instead of eliminating them.” Two weeks ago, I posted the related article “Decision Model Tolerance” on LinkedIn.

DMCommunity has already received five solutions for its Oct-2025 Challenge “Decision with two objectives” which is looking for decision models that help a web designer to select certain features while satisfying budget and value constraints. It is quite a simple problem for most linear or constraint solvers, so it is no wonder that the first 3 solutions utilized different solvers: Seeker, Pymoo, and OPL CPLEX. What makes this problem more interesting is that it involves two conflicting objectives: total value and total cost. Our decision models are supposed to devise a rational way to trade them against each other.

Today, four days before the start of DecisionCAMP-2025, we announced the public availability of the Advanced Rules Learner, a Machine Learning (ML) product designed for business users and well-integrated into the OpenRules Decision Intelligence Platform. Now it can generate working decision models based only on a set of problem examples. I will demo the latest capabilities during my presentation at DecisionCAMP on Sep 22 at noon EDT. You may preview my slides ahead of time. Why did we decide to enhance Rule Learner?

Almost all programming languages support regular expressions. Many search languages allowed simple wildcards, for example “*” to match any sequence of characters, and “?” to match a single character. Non-programmers may use regular expressions in many situations just as well. In particular, business rules may need to define conditions that use regular expressions. Examples of such rules were specified in the DMCommunity July-2025 Challenge. Below is a possible OpenRules solution.

The new release 11.0.0 of the OpenRules Decision Intelligence Platform (http://OpenRules.com) simplifies the installation process, and the standard installation now includes various OpenRules samples and documentation. We also introduced advanced features to Rule Learner, Rule Solver, and Rule DB. The website OpenRules.com has been unified and now includes product-specific sidebars.

DMCommunity.org published the April 2025 Challenge “Case Assignment”. Here is an OpenRules solution. First, we created a pure business decision model that defined all feasible case-analyst assignments. However, to find a solution that minimizes the total overqualification, we need to use an optimization engine. As usual with OpenRules, we prefer not just to solve the challenge but to build a working decision service that works for different datasets and is available from any remote cloud. The implementation based on Rule Solver is described below.

The new book is now available at Amazon: https://lnkd.in/ef-ygpJg. This book is oriented to subject matter experts who want to build operational decision models for their business environments. No programming skills are required. The objective is to help readers learn quickly how to apply the decision modeling approach to building real-world decision models. This book will guide readers through practical examples, starting with simple business problems and moving to complex ones. After learning how to create decision models, readers may also test, debug, modify, and analyze them using freely downloadable OpenRules software and familiar tools such as Microsoft Excel (http://OpenRules.com).

We’ve updated the list of publications and presentations that demonstrate the practical use of Decision Intelligence software including OpenRules Rule Engine, Rule Learner, and Rule Solver. It contains about 100 items with the latest being shown first. Link

Today ResearchGate confirmed that people continue to read my 2019 presentation “Creating Intelligent Perpetually Running Applications with Business Rules” at the BBC conference. I reread its abstract: “In the AI era, many business applications which consider themselves “intelligent” cannot simply execute a complex rules-based transaction and wait for the next one. To become really “intelligent” applications, they should be able to learn from already executed transactions, accept new facts as they become available, and, when necessary, they should make changes in their own execution logic. This presentation describes a practical architecture that supports the creation and continuing development of such intelligent, perpetually running decision-making applications. This proposed architecture utilizes the modern pub/sub tools with continuous data streams and state machines, allowing subject matter experts to define and maintain behavioral and decisioning rules. It’s been demonstrated by using several real-world scenarios.” Then I went through my presentation with a new interest. If you are also interested in long-running decision services you may read about Stateful Loan Approval and watch Demo of a long-running decision service.

This June-2024 Challenge deals with the famous stable marriage problem formulated as follows:

“Given n men and n women, where each person has ranked all members of the opposite sex in order of preference, marry the men and women together such that there are no two people of opposite sex who would both rather have each other than their current partners. When there are no such pairs of people, the set of marriages is deemed stable.”

A very good analysis of the problem is provided in the recent presentation given by Dr. Guido Tack. My solution is based on OpenRules Rule Solver. It includes two different implementation approaches described in this document. The complete decision model has been added to RuleSolver samples.