Everybody is familiar with Sudoku: you need to fill a 9×9 grid in a such way that each column, each row, and each of the nine 3×3 boxes (also called blocks) contains the digits from 1 to 9, only one time each. In this post I’ll show how easy to build a Sudoku decision model using the latest OpenRules Rule Solver.
Continue readingIn real-world decision models, our customers deal with complex business glossaries. In particular, it is quite common when one business concept refers to other business concept and it is not immediately clear how to define and use such references. I will explain how it’s supposed to be done in OpenRules. let’s consider a simple example “HelloNestedLocation” with the following glossary:
Iterating over collections (arrays) of decision variables is a frequent need in real-world decision models. For example, the following decision table defines if a company has high-paid employees:
The iteration here is defined by the [for each Employee in Employees] in the first row. The ACTION-BREAK in the column “ActionExecute” will force the iteration process to stop (break) when the very first high-paid employee is found.
Continue readingIn this post we describe an OpenRules decision model that addresses DMCommunity.org Mar-2023 Challenge “Permit Eligibility”. The Challenge asks to implement this rule: “An applicant is eligible for a resident permit if the applicant has lived at an address while married and in that time period, they have shared the same address at least 7 of the last 10 years.” There is already 2 DMN-based solutions published by Bruce Silver who explained that this simply sounding rule requires to address several not so simple considerations. There is also an attempt to create a decision model with ChatGPT, which I analyzed and converted to a working Java code. I asked an OpenRules developer Alex Mirtsyn to look at this problem, and together we came up with a solution described in this post.
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These days only lazy people don’t write about ChatGPT and large language models (LLM). Vendors are trying to be the first to announce a ChatGPT integration even when they don’t have anything serious to show. I’ve also written about it: see “ChatGPT Producing Simple Decision Models” and “LLM and Decision Modeling“. This weekend I decided to help ChatGPT (that is now at GPT-4) to address the Challenge “Permit Eligibility” published by DMCommunity.org. It has a simple rule: “An applicant is eligible for a resident permit if the applicant has lived at an address while married and in that time period, they have shared the same address at least 7 of the last 10 years.” But this rule contains several tricky assumptions – no wonder, DM vendors are not in a hurry to submit a solution.
Continue readingLiving through the ChatGPT boom, it is interesting to read this article “Is ChatGPT Aware?“:
“Polanyi’s paradox, named in honor of the philosopher and polymath Michael Polanyi, states that “we know more than we can tell.” He means that most of our knowledge is tacit and cannot be easily formalized with words. In The Tacit Dimension, Polanyi gives the example of recognizing a face without being able to tell what facial features humans use to make such a distinction.“
It brings back some of my related thoughts on “Business Rules and Tacit Knowledge” from 7 years ago. It described how “Human Learning” and “Decision Modeling” were moving in opposite directions. Will we see a change?
ChatGPT has the public excited, but the experts are reserved in their praise. Thinking about a practical application of the Large Language Models (LLM) to decision modeling this quote from LeCun caught my attention:
When we create practical decision models we usually deal with an even more limited “universe”. A decision model “manipulates the state of the decision variables” within a very specific business domain (insurance, loan origination, claims, medical guidelines, etc.) complemented by generic concepts well covered by such relatively small standards as DMN and SBVR. Decision modeling universe is really “limited, discrete, deterministic, and fully observable”.
So, being cautious about the current ChatGPT’s hype, we may be more optimistic about the next breakthrough in Decision Modeling. I suspect the answers of experts to my DecisionCAMP-2022 question “Are our Rule Engines Smart Enough?” would be different today.
DMCommunity.org offered the Dec-2022 Challenge “Christmas Model” created with ChatGPT. This nice model defines a set of people, a set of gifts, the happiness level, and costs of each gift. The objective is to maximize the total happiness, subject to the budget constraint that the total cost of the gifts must be less than or equal to the budget, and the constraint that each person can only receive one gift. As a holiday exercise, I tried to implement it using a free and open-sourced JavaSolver.com. Here is my solution:
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Our team worked hard to prepare a nice Thanksgiving gift for our customers, and today I am happy to announce general availability of OpenRules-9! This major new release comes with a new implementation of our Graphical Explorer and Debugger as a foundation for the current and future OpenRules Interactive Decision Modeling Environment oriented to business users. Watch new Intro Video, read Release Notes 9.0.0, and evaluate it for free. Happy Thanksgiving!
Continue readingDMN is a modeling language and notation for the precise specification of business decisions and business rules using well-defined tabular formats. This popular standard even includes an interchangeable XML format. However, contrary to XML (eXtensible Markup Language), DMN can hardly be called extensible. Today’s DMN allows a user to define complex problem-specific decision modeling constructs using powerful FEEL boxed expressions (that still are being criticized as too close to programming). But DMN doesn’t specify means for building simple domain-specific decision tables that look very natural for business users and hide complex implementation details that actually may require programming.
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OpenRules empowers business users with an ability to assemble new decision services by orchestrating existing decision services independently of how they were built and deployed. The service orchestration logic is a business logic too, so it’s only natural to apply the decision modeling approach to orchestration. OpenRules allows business analysts to create a special orchestration decision model that describes under which conditions such services should be invoked and how to react to their execution results. Link
The ultimate objective of Business Decision Modeling:
A business analyst (subject matter expert) defines a business problem, and a smart decision engine solves the problem by finding the best possible decision.
Declarative decision modeling assumes that a business user specifies WHAT TO DO and a decision engine figures out HOW TO DO it. This is quite opposite to the Procedural approach frequently used in traditional programming.
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Decision tables is the most popular mechanism for representation of business logic – no wonder they play the major role in the DMN standard. However, when it comes to analyzing large amount of data, standard decision tables may not be the best way to do it. In this post, I’ll describe a much better approach implemented in OpenRules.
Continue readingIn this post I describe an OpenRules solution to the puzzle offered as DMCommunity.org June-2021 Challenge:
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Whether you like it or not, your business decision models will contain bugs like regular software always does. They will fail to produce expected results for certain inputs and under different unforeseen circumstances. As a result, their authors, who are usually business people, will have troubles to understand why certain rules were not executed while others did. For real-world business models even the best explanation reports and execution traces could be difficult to follow. When complex logical errors occur, decision model authors discover that their business rules and decision management environment is only as good as the rule debugging tools it offers! Continue reading
We will run a webinar series about OpenRules products starting on Feb 5, 2021:
“Decision Modeling with OpenRules Explorer – Introduction” – Free Registration
Watch Recording
Next Webinars: Continue reading
New OpenRules Decision Manager Release 8.3.0 comes with a Graphical Integrated Decision Modeling Environment called “Decision Model Explorer“. It automatically builds decision model diagrams, opens Excel files associated with different nodes, and allows business people to modify, test, and deploy their decision models from one place. Watch the video and read more. Join our free webinar on Feb 5, 2021 – register here. Continue reading
The new OpenRules Decision Manager 8.3.0 along with “Decision Model Explorer“ includes an ability to define some decision variables by using formulas directly in the Glossary. For instance, in the standard sample “PatientTherapy” we can modify the glossary as follows:
Many popular languages (e.g. Angular or Mustache) use text interpolation to incorporate dynamic string values into the text. OpenRules Decision Manager Release 8.2.1 gives our customers an option to use text interpolation by putting text decision variables in the double curly braces {{ and }}. For example, see how it’s being used in the standard sample project “PatientTherapy”:
OpenRules provides business users with abilities to build and deploy operational decision microservices. Now we empowered business users with an ability to assemble new decision services by orchestrating existing decision services independently of how they were built and deployed. The service orchestration logic is a business logic too, so it’s only natural to apply the decision modeling approach to orchestration. In this post I will explain how to orchestrate different services by creating a special orchestration decision model that describes under which conditions such services should be invoked and how to react to their execution results.
Today I presented “Rule Learner: Self-Learning Decision Models” at the DecisionCAMP Monthly Meeting. It’s about an integrated use of Machine Learning (ML) and Business Rules (BR) within Digital Decisioning Platforms. It describes how “RuleLearner.com” can help business analysts to discover business rules from large historical data sets. Without forcing business analysts to become experts in data science or programming, Rule Learner discovers business rules by naturally incorporating ML algorithms into Business Decision Models. The session focuses on practical aspects of rules generation by developing ever-learning decision-making applications. Video, Slides
While DMN-like decision tables are powerful enough to represent business logic for many practical problems, in real-world our customers frequently define new types of decision tables that are specific for their business domains. For example, years ago OpenRules was chosen for a large project that in particular dealt with spatial business rules. Their customers,, suppliers, and operations vary by region, and distance between then affected their decisions. They already used a Geospatial Information System (GIS) in order to explore spatial relationships that leveraged the industry standard Java Topology Suite (JTS) with a powerful Java API.
However, they wanted their business (!) users to natively define and maintain complex spatial rules without becoming experts in specific Java API. This 2014 presentation describes how OpenRules helped this customer to create a Spatial Decision Table template allowing stakeholders with no GIS training to use plain English in familiar Decision Model spreadsheets to define spatially aware business rules without any additional software. Continue reading
At DecisionCAMP-2020 we had a lot of discussions about the orchestration of the decision services and how to invoke different decision services when states of the business objects change over time. One of the most popular orchestration technique is a Pub/Sub architecture with State machines. The Challenge “Dynamic Loan Approval” is an example of perpetually running decision-making applications which should be able to learn from already executed transactions and evaluate new facts as they become available. This post describes how we implemented this application utilizing OpenRules-based GUI connected to AWS Lambda, EventBridge, SNS, and SQS. Continue reading
There are already 3 solutions to DMCommunity May-2020 Challenge. I especially like how Carol-Ann Berlioz created her solution using a test-driven approach when test cases prompt her which rules should be added. So, I’ve tried to reproduce her solution but instead of a product specific GUI I decided to use OpenRules decision tables defined in Excel. So, here are our test-cases with input data and expected results: Continue reading
For years OpenRules was among the fastest rule engines. When last year we moved from run-time interpretation to design-time code generation, we, like our colleagues at Red Hat Drools, managed to further improve the overall performance and provide support for practical decision microservices. As a result, we dramatically minimized start-up time, went from 50-100 milliseconds per transaction to 5-10 milliseconds, made memory footprint small. These are really good results needed by modern enterprise decision-making systems.
However, I knew that we have multi-year customers that use really big (!) decision tables with 10 and even 30 thousands of rules. How to improve their performance? Continue reading
Last week I created a “Worker Planner” that has a nice GUI deployed on Apache Tomcat and it works in sync with a remote Scheduling Decision Service deployed as AWS Lambda. The scheduling service was implemented with JavaSolver and a constraint solver included into JSR331. My objective was to demonstrate that these days with cloud-based deployment it is not so difficult to create an end-to-end full-scale decision optimization service. I also wanted to show how to apply powerful Linear Solvers to crack traditionally complex production scheduling problems. So, two days ago I took a well-known problem that is described in this example: Continue reading
There are already several good responses to the DMCommunity’s April-2020 Challenge “Doctor Planning”. Below I am describing how I tried to use this challenge to create a complete decision optimization service. I ended up with a working Worker Scheduler that shows a solution for this particular challenge in Fig. 1 (click to open):
OpenRules offers two open source products to support an integrated use of “Business Decision Modeling with Rule Engines and CP/LP Solvers“:
In this post I will describe how you can deploy decision optimization models as AWS Decision Microservices without programming or complex configuration. Continue reading
In these uncertain times, to do our part we want to help US businesses, especially health organizations to quickly make important operational decisions. OpenRules offers immediate assistance in creation and deployment of your decisions services on-cloud or on-premise.
Contact us at support@openrules.com and we will setup a call to discuss how we can be in help. As the CTO at OpenRules, I’d allocate my personal time to help you with your urgent decision automation problems for free – email me directly at jacobfeldman@openrules.com.
This month OpenRules Decision Manager became the first SaaS Rule Engine available in AWS Marketplace. It gives our customers an opportunity to execute OpenRules-based decision services using a “Pay-As-You-Go” pricing model. You may read the Press Release approved by the AWS Marketplace team. Using your AWS account, you can now subscribe to our AWS SaaS Subscription that allows you to pay a minimal fee for only what you actually use with all charges coming from Amazon AWS Marketplace. Continue reading
2019 has brought lots of innovations and positive developments for OpenRules and its customers. This post summarizes all the new capabilities and user experiences that we have introduced in 2019. Continue reading
There are two major types of decision engines utilized by different Business Rules and Decision Management systems to execute business decision models:
1. Traditional rule engines (RETE-based or Sequential).
2. Constraint-based rule engines Continue reading
Problem
The DMCommunity Challenge Apr-2019 “Recreational Fee” is based on the following problem provided by Ron Ross:
A city has created a decision table to determine appropriate usage fees for its recreational facilities based on length of usage and when the usage occurs:
OpenRules business decision models can be deployed as operational decision services utilizing the Serverless architecture provided by Amazon Web Services (AWS), the most powerful and popular cloud platform in the modern world. When you deploy your decision models as AWS Lambda functions, you don’t even think about servers and pay only for the execution time your services actually consume. Continue reading
Nowadays rules-based business decision models are usually developed and maintained by business analysts or other subject matter experts (not by software developers). And more and more people want to make their business decision models available from cloud as operational decision services. But is it possible for non-technical people to achieve this objective? There are so many new terms and concepts to learn, that it seems doubtful for business people to handle this task. Continue reading
On Aug. 1 we made OpenRules Decision Manager publicly available for the first time – everybody can download and try its free evaluation version. This 3 mins YouTube video provides a very simple introduction to the on-premise version of our new product that now can execute exactly the same decision models as the classic OpenRules BRDMS.
The major advantage of the Decision Manager is the fact that it is well-positioned to be used on cloud utilizing server-based or serverless architectures. It perfectly fits the requirements of the modern microservices and is ready to support decision-making applications which handle the millions of complex rules-based transactions per day when every transaction should be executed within milliseconds. The next upcoming release of OpenRules® Decision Manager will provide a cloud-based UI for analysis, debugging, automatic deployment and execution of decision models as microservices – stay tuned!
The latest OpenRules Release 8.0.0 comes with a new, very simple Java API for incorporation of business decision models created by business people in Excel into Java applications. It corresponds to the simple view of a decision model as “a Business Glossary surrounded by Decision Tables that specify decision logic for Goals and Sub-Goals“. OpenRules 8.0.0 explicitly introduced Java concepts “DecisionModel” and “Goal” to support the Goal-Oriented Decision Modeling approach described in this book. Continue reading
Traditionally, the majority of decision tables list their rules in the top-to-bottom order when different rules are placed in rows. Here is an example of a typical single-hit decision table created in Excel in accordance with the OpenRules format: Continue reading
We a
re aggressively making OpenRules-based services available from cloud environments such as Amazon EC2. In particular, we’ve just re-deployed our Decision Model Analyzer from a 3rd party remote Tomcat to Amazon. It was just a very simple reconfiguration, but the effect is really positive: the Analyzer is now much faster and much more reliable. You may try it yourself without any registration or fee: simply click on the button on the right.
The source code of the Analyzer is included in the OpenRules standard release and can be considered as an example of how to deploy OpenRules web applications created using OpenRules Dialog to cloud. Another example is the game “Nim” that you may play now from the cloud by clicking on the image below: Continue reading
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
DMCommunity Sep-2018 Challenge “Balanced Assignment” gives an example of a complex business problem with a serious optimization component. This problem deals with the assignment of people to different project groups. Usually, such problems require deep knowledge of optimization techniques. My interest was to build a decision model for this problem and to investigate what can be done by business people and where the involvement of optimization experts is necessary. So, I attempted to use a business-friendly approach to represent and to solve this complex problem. It was not a simple journey, and this article describes what I did successfully and where I failed. Link
The latest DMCommunity.org Challenge “Recreational Fee” is very simple, but I still wanted to show how to create the proper decision model using the OpenRules goal-oriented approach. The implementations is described in this PDF document.
Dr. Bob Moore in his DMCommunity.org post “What is a ‘Decision’?” considers different definitions of a “Business Decision” and “Business Decision Logic”, which Bob, Ron Ross, and I have recently been discussing in the context of the March DMC Challenge. Below I will share my point of view.
First of all, it’s important to stress that we are talking not about any ‘decision’ but ‘Operational Business Decision’ which provides a solution for an operational business problem. The majority of such problems can be considered as a special case of the classical Constraint Satisfaction Problem (CSP) defined in the famous book by S.Russell and P.Norvig “Artificial Intelligence: a Modern Approach” [5]. So, I simply applied the Russell/Norvig’s definition to our business problem. Continue reading
In February-2019 I published a new book “Goal-Oriented Decision Modeling with OpenRules” available from Amazon. It explains the goal-oriented approach to business decision modeling introduced by OpenRules in 2018. The book is a practical guide that explains how to create and maintain operational business decision models in a step-by-step easy to understand style. The guide consists of a series of dialog-sessions in which an AUTHOR explains major decision modeling concepts and methods to an inquisitive READER who asks questions and implements the concepts. You will quickly learn how to represent complex decision logic and end up with a deep understanding of practical decision modeling techniques. The book contains only 150 pages, and you may start developing your own decision models after reading first 1-2 chapters.
Our customers frequently build not one but multiple decision models for their business domains like property and casualty insurance, loan origination, medical guidelines, etc. After building several decision models, they already have a quite rich glossary and various decision tables that essentially cover their business domain. So, it gives them a good foundation to build a library of relatively small decision models which can be used to assemble more complex decision models. Sometimes they even add domain-specific decision tables and supporting Java classes. This PDF document uses well-known loan origination problems (described in the Chapter 11 of the DMN specification) to explain how to build and utilize a library of decision models. Link
During the closing discussion at the DecisionCAMP-2018, I promoted a new “model-based” approach to decision modeling that contrary to the commonly used “method-based” approach allows a human decision modeler to concentrate on “WHAT” instead of “HOW”. This approach gives more power to decision modelers allowing a decision engine to come up not just with one of many possible decisions but to find the optimal decision. I wanted to demonstrate this power using a more complex business problem and to do it before the end of this year. So, I decided to apply the model-based approach to the problem “Rebooking Passengers from Cancelled Flights” that is the most challengeable problem among multiple DMCommunity.org Challenges. Continue reading
I provided two solutions for DMCommunity.org Challenge “Vacation Days Advanced”. So far, all decision models submitted as solutions for the old Jan-2016 Challenge and the new challenge were “method-based” meaning they describe exactly how to assign extra days while avoiding possible conflicts. I tried to apply a new “model-based” approach that concentrates on “what” instead of “how” and finds not just one of many possible solutions but the optimal one. The first decision model consists of clearly separated two parts: 1) Business Part presented in Excel decision tables; 2) Technical Part presented in Java using the JSR-331 standard. The second decision model is an attempt to present both parts completely in DMN-like decision tables using Excel only (the supporting Java code is hidden in new Excel templates). You may analyze both solutions here. I’d appreciate comments and suggestions for improvement.
OpenRules was well presented at the major technical business decision management event DecisionCAMP-2018. Here are the presentations directly related to OpenRules:
I also published a paper “Building and Analyzing Goal-Oriented Decision Models” at the Proceedings of the RuleML+RR 2018 Challenge – it explains our goal-oriented approach to decision modeling using a Credit Card Application decision model. In my closing notes I tried to demonstrate how a smarter decision engine can simplify decision modeling using concrete examples from DMCommunity.org challenges. After considering different decision modeling approaches, we will add these capabilities to standard OpenRules features.
OpenRules - Decision Intelligence Software 