(Click on the presenters or scroll down to see presentation abstracts and author biographies)

Interactive Panel “Ask an Expert”: Sep 27, 16:00 EST (moderated by James Taylor, Decision Management Solutions) Recording

    • Carlos Serrano-Morales, Sparkling Logic
    • Guilhem Molines, IBM
    • Bob Moore, JETset Business Consulting
    • Alan Fish, FICO
    • Gary Hallmark, Oracle

Presentation Abstracts and Author Biographies

How do you FEEL about low code? by Denis Gagne (Trisotech)

Digital transformation and the accelerated transition to remote work are contributing to a perfect technological storm. This perfect storm is indifferently hitting every industry around us. A particularly challenging vector of this technological storm is the ever-growing need for business automation to achieve digital transformation conflated with an ever-growing shortage of technology professionals and software developers. With most companies turning to technology to transform how they engage with customers; software developers are in high demand and short supply. It is clear that we cannot rely on this small number of specialised workers – software developers – to carry out the massive undertaking of digital transformation in organizations.
One way to weather the storm is to empower non-developers in organizations to automate business logic. Business knowledge workers within organizations have a clear understanding of the logic of the business. They have the best understanding of business workflows and decisions required to deliver and exceed the new and expected digital customer experience. They excel at business decision thinking. Then why not enable these business knowledge workers to become not only the business logic architects, but also the actual construction workforce of your digital transformation?
In this session we discuss the emergence of the low-code paradigm as a required enabler to the timely achievement of the desired digital transformation. We compare the notions of no-code, low-code and pro-code and discuss how business knowledge workers can learn to think more like software developers by adopting a Decision Thinking mindset. Using the Decision Model and Notation (DMN) as the cornerstone of decision thinking and the Friendly Enough Expression Language (FEEL) as the low-code language of choice, we show how business knowledge workers can take business automation to production faster, gain simple and efficient ways of making enhancements, and maintain the deployed automated business logic. FEEL is simple enough for business knowledge workers yet expressive and powerful enough for professional developers. In short, FEEL offers the perfect scaffolding for the automation of business logic. With FEEL as a low code language, business knowledge workers can truly become the artisans of the digital transformation. Keywords: Low-Code, Decision Thinking, Friendly Enough Expression Language (FEEL), Decision Model and Notation (DMN)

Denis Gagné is CEO and CTO of Trisotech, a leading Standard Based Low-Code Intelligent Automation enterprise software vendor. For more than two decades, Denis has been a driving force behind most international BPM standards in use today. Denis is a member of the steering committee of the BPM+ Health Community of Practice, where he also leads the Ambassador program. For the Object Management group (OMG), Denis is Chair of the BPMN Interchange Working Group (BPMN MIWG) and an active contributing member to the Business Process Model and Notation (BPMN), the Case Management Model and Notation (CMMN), and the Decision Model and Notation (DMN) work groups.

Transparent Machine Learning for Data-driven Decision Management by Greg Ottosson (IBM)

As businesses strive to become more “data-driven” and make more precise predictions with Machine Learning, one emerging issue is transparency. For critical decisions, both internal business guidelines and external regulation might require our ML-based predictions to be as transparent as the existing business rules they complement. While black-box ML models can be leveraged – perhaps with post-hoc explainability added – in many cases a more suitable approach might be to train a fully transparent ML model.
Rule Sets and Score Cards have been hand-crafted and used in Decision Management for decades but deriving them automatically from data creates new opportunities and some new challenges. How should these transparent ML models be included in decision models? How should they be expressed, managed, and governed? How should they be invoked and explained in the context of additional policy rules?
In this presentation we discuss the characteristics and use cases of transparent ML models, and how they can be included inside decision models, while enhancing both accuracy and governance of decisions.
Keywords: Machine Learning, Rule Learning, Decision Modeling, Predictive Analytics, Transparency

At IBM, Greger Ottosson leads product strategy for applying AI to improve digital business automation. Current work is focused on using Machine Learning to improve operational decisions, automate workflows and infuse ML in business applications. Greger is an experienced business leader and product manager and has deep experience in advanced analytics, data science and enterprise software. He holds a PhD in Computer Science from Uppsala University in Sweden

Decision Models for Ethical Decision Making: Transparency in the light of the proposed EC Artificial Intelligence Act by Dr. Alan Fish (FICO)

Around the world, consumers are becoming increasingly concerned about the ethicality of automated decisions. Their concerns include the transparency of any decisions made about them. This presentation looks at how transparency is defined in the new proposed EC Artificial Intelligence Act, and suggests ways in which providers of AI systems can use decision models to ensure that their decisions are explainable to consumers and regulators in compliance with this legislation. Keywords: DMN, Decision Model, Decision, Ethics, Explanation, Compliance.

Dr. Alan Fish is a thought-leader in Decision Modelling and Decision Management. He invented the Decision Requirements Diagram (DRD) which exposes the structure of a domain of decision-making, and developed Decision Requirements Analysis (DRA): a methodology for building and using such decision models. Alan is the author of “Knowledge Automation: How To Implement Decision Management in Business Processes” (Wiley), which has been translated into Chinese. He is editor and co-author of the OMG specification Decision Model and Notation (DMN), and co-chairs the OMG DMN task force. He continues to develop notations, methodologies and ontologies to provide the conceptual environment for business users of FICO Platform.

Outside work Alan is a musician: singer-songwriter, guitarist and saxophonist. He is a keen member of several musical ensembles and has released an album of his own songs: Yes Why Not.

Managing the Business Data in Business Decisions by Bob Moore (JETset Business Consulting Ltd)

Making decisions involves three distinct bits of knowledge: the case data which identifies the specific situation we are making a decision on; the logic behind how we make decisions; and the internal ‘business data’ items which set the thresholds and constraints the logic applies to the case data. Such business data appears in the cells of our decision tables and in expressions in our rules and functional logic.

Often the number of business data items involved may be small, only a few dozen if that. But in some cases, there may be hundreds, thousands, even millions of such items. Some of this data may also be only partially under business control, being provided by third party regulatory or standards bodies. Even with only a few data items to manage, problems can arise. How do we efficiently represent how the data drives the decisions? Will our decision systems give timely results if we potentially have scan thousands or even millions of data items? How is maintenance performed as the business data changes over time?

There is no one size fits all solution to these issues, but there are many strategies one can employ to incorporate data into decisions. The presentation will explore a small subset of these mainly using techniques explored in some of the Challenges set by the Decision Management Community in recent years, but ones which have also been exploited in real world applications, in finance, insurance and manufacturing among other industry sectors. Keywords: Business Data, Decision Management, Implementation Strategies

Dr. Bob Moore started work on automated decision systems in 1989 and spent the following three decades or so building rule-based systems for a range of clients in finance, insurance, manufacturing and government, using several different technologies. He is largely retired now but has recently got himself involved in educating the next generation of IT professionals as an Associate Lecturer in the School of Computing and Communications at the Open University.

Framework Agnostic, Rule-Driven Dynamic Forms by Seth Meldon (Progress)

For enterprises that fulfill critical functions for individuals’ day to day lives (e.g. healthcare providers or payers, banks, insurers, payment handlers), investments in better end user experiences do not inherently result in improved user experiences. For a consumer to accomplish most any objective within these realms (requesting a healthcare claim reimbursement, freezing your credit, applying for a loan, registering to vote…) will, however, inherently require a multitude of sub-tasks to fully complete, and these sub tasks are unlikely to be common across all objectives. With the proliferation of chatbot software on the market today, digital experience executives researching ways to improve adoption of self-service tools will inevitably be led to consider these solutions as the solution.

This Decision Camp session will serve to counter the effectiveness of this approach and present an alternative: business rule driven dynamic forms. Drawing from a 2022 case study of a successful insurance industry use case, we’ll demonstrate how Corticon.js business rules enable business analysts to define dynamic claims form logic. The rules, transpired into a JavaScript bundle with no external dependencies, instructs the front end browser client which relevant user prompts to display, based upon existing policy data and data accrued from the form. Because all data is retained within the browser session, not the rules, the rules are adaptable such that common form prompts may be easily reused across end user objectives, interspersed with objective-specific user prompts. User experience is maximized by tailoring each step of the form experience to the user’s objective.

Seth Meldon is the senior solution engineer supporting the Corticon business rules engine in North America at Progress, supporting new and existing users throughout all phases of designing rule-intensive applications. Melding this professional experience with his areas of personal research interest, he has designed and written about solutions leveraging the FHIR standard to improve patient access to their medical data, implementing decision logic for incentive-based community adoption of renewable energy sources, and running decision services as cloud-based functions and in mobile applications. His independent, serial investigative articles and longform environmental history report, The Watershed, are accessible from

Always look at the bright side by Silvie Spreeuwenberg (Simacan)

As an advocate of business rules engine technology and entrepreneur in the software industry, with a background in Artificial Intelligence, I ended up recently as CTO of a tech scale-up. With 7 year old high performing, high code software the stack of backlog features is getting higher every day and the pullboard size seems to become smaller every iteration. We follow the Agile Methodology based on SCRUM and some aspects of SAFE but agility is far away. Sounds familiar?

Introducing a business rule engine to externalize the management of business logic and decrease the demand for coding, is a major step in such environment. A smaller step, such as a low code platform for green field features, is just as challenging. I share with you the obstacles I encountered and we brainstorm solutions.
Take aways from this session:
1) High coders don’t do business rules or low code; 2) Budget for people and budget for tools are unrelated resulting in an abverse incentive; 3) Productivity enhancement tools have a bad reputation or are unknown.
Keywords:  Business Rules, Low Code, No Code, Agile software development, Case Study, Lessons Learned, Operational business decision services

Silvie Spreeuwenberg is an experienced entrepreneur. She combines the ability to be a holistic thinker while, at the same time, she has detailed knowledge about artificial intelligence, compliance and software development. With her company LibRT she provided consultancy, training and auditing services for rule intensive software automation such as airport planning, traffic management, financial products, tax administration and social security services. With her company RuleArts she delivered a software tool for business users to manage their business rules and definitions with standardized semantics. Currently she works as CIO for Simacan, a dutch scale-up delivering a SAAS platform for transport execution monitoring.

Good Beginnings – Sustainable Automation by Chris Berg (inrule)

Somewhere in the life of an automation project a transition takes place from surviving to thriving. The first go-live will feel much different after two hundred promotions to production. Now the organization must live with the solution. Living with automation requires the same rigor and careful execution as the initial phases of the project. What’s in the backlog after go-live? We find a list of important deferred features, bugs, and technical debt. Many tasks remain manual and while the finish line approached, the team expended little toward automating the lifecycle or tracking data for business impact. Long-term success requires a broad view of the investment across teams and activities making sure the effort doesn’t drift or get mired in manual activities. Most importantly the team must finish the work required to confirm the business case.
Keywords: team building, core values, design thinking, strategy, practice

Chris Berg is the Director of Digital Acceleration at InRule. He has a deep history with the decision management, digital process automation and analytics having spent many years with ILOG and IBM as a product architect, product manager and designer. He is a member of the “DMN On-ramp Group” with advocating for DMN adoption. He has published multiple papers and articles on automation the most recent with National Mortgage News, “Five problems lenders have adopting automation and how to fix them.” His multiple points-of-view and many years listening to customers provide a wide-angle lens by which to understand enterprise (hyper) automation from business goals to outcomes.

Engineering Decisions for Decision Engineering by Rob Parker (AngleFinance)

In this presentation we discuss our experiences whilst implementing a credit policy from text based policy rules to a Decision Model Notation (DMN) implementation. In particular, we focus on the engineering decisions or implementation design decisions necessary to implement a transparent, maintainable decision automation solution. Engineering design choices include; the granularity of decisions; when to choose between FEEL expressions versus decision tables; when to use compound business objects (json structures) versus when to use primitive types as inputs to decisions; how much FEEL is too much FEEL. In addition we discuss design choices such as the impact the user experience may have on the structure and sequencing of decisions and vice versa. Keywords: DMN, FEEL, Decision Automation, Credit Policy, Engineering

Rob Parker is an enterprise architect with more than thirty years of experience within the information technology industry. Rob has worked in industries from Government utilities, telecommunications through to banking and finance. Rob has a passion for process and automation, and currently heads up the Engineering and Architecture functions for AngleFinance.

You built a decision model! Now what? by Jan Vanthienen (KU Leuven)
DMN offers a business-friendly representation of business decision knowledge and current tools use this executable specification to make a decision based on this knowledge. DMN even allows checking the model for consistency and completeness. However, there is more than straightforward processing of input cases.
DMN also allows a number of other functionalities that go beyond simple (or complex) decisioning:
– flexible reasoning, explanation, answering decision questions like How, Why, What-If, How-To (e.g. in the form of a chatbot), as presented at last year’s DecisionCamp
– decision analysis: what are the most important criteria?, what is the average cost of a decision?, what are the most frequent cases? Who is doing what?
– fairness: does the knowledge correspond to what one would expect in terms of changes in information item values. E.g. does a higher income always lead to a higher eligibility?
– compliance: is the decision knowledge compliant with existing rules and regulations?
– monitoring: how many cases actually obtained a specific decision outcome?
– evaluation of the decision policy based on historical data
– simulation and prediction: What would be the total outcome of a policy change?
This presentation provides an overview of opportunities for decision modeling and DMN by looking into some common business challenges.
Keywords: Decision Model and Notation, DMN, decision modeling, decision analysis

Prof. Jan Vanthienen received his PhD degree in Applied Economics from KU Leuven, Belgium. He is a full professor of Information Systems at the Department of Decision Sciences and Information Management, KU Leuven and (co-)authored more than 200 full papers in international journals and conference proceedings. His research interests include information and knowledge management, business rules, decisions and processes, and business analysis and analytics. He received an IBM Faculty Award on smart decisions, and the Belgian Francqui Chair at FUNDP. Currently he is department chair at the Department of Decision Sciences and Information Management of KU Leuven.

Decision Modeling: Good, Bad, Ugly by Dr. Jacob Feldman (OpenRules)
In this presentation I will share different implementations of the decision model for the recent DM Community Challenge “Medical Claim Processing“. I will show how a desire to use already known decision modeling constructs could lead to bad (and even ugly) decision model implementations. We will discuss certain pitfalls, and I will use this example to provide practical advices for building intuitive and manageable decision models for complex business problems.
Keywords: Business Decision Models, Decision Microservice, Invocation Context, Authorized access, Security, CI/CD

Dr. Jacob Feldman is the CTO of OpenRules, Inc., a US corporation that created and maintains the highly popular Business Rules and Decision Management System commonly known as “OpenRules”. He has extensive experience in development of decision-making engines using business rules, optimization, and machine learning technologies for real-world mission-critical applications. Jacob is the DecisionCAMP’s Chair,  the manager of, and an active contributor to BR&DM forums. He is also the Specification Lead for the optimization standard JSR-331. Dr. Feldman is an author of two books “DMN in Action with OpenRules“ and “Goal-Oriented Approach to Decision Modeling“. He has 5 patents and many publications in the decision intelligence domain.

Decision Tables: Could They Communicate Better? by Ronald G. Ross (Business Rule Solutions)

Are decision tables computational devices or communication devices? Yes (both!). What do communities of people need for effective communication? Meaning. Sense-making is a key element of all communication. Decision tables (and decision rules) are no exception. So, the vocabulary used in decision tables should be defined and structured.

And why do many decision tables become so complicated? Perhaps because exceptions and scope are not being handled as well as they could be. There’s a whole world beyond decision tables. Decision tables should make a better effort to communicate with it. This presentation includes real-world examples of good and not-so-good communication to stimulate discussion.

• Understanding the Elements of Vocabulary Structure

• Specifying Applicability Explicitly

• Behavioral Rules as Guardrails

Keywords: Decision Tables, Rules, Vocabulary, Exceptions, Scope, SBVR

Ronald G. Ross is Co-Founder and Principal of Business Rule Solutions, LLC ( BRS provides consulting, training and mentoring in support of policy analysis, business rules, concept modeling, decision analysis, and business knowledge engineering. BRS clients have included many 100s of top businesses and government bodies world-wide. Ron is the author of the 2020 groundbreaking book “Business Knowledge Blueprints: Enabling Your Data to Speak the Language of the Business”, featuring concept models, business vocabularies and disambiguation. It is his 9th professional book.

Ron is Chair of the annual Building Business Capability (BBC), official conference of the IIBA®. He is also Executive Editor of and its flagship on-line publication, Business Rules Journal. Ron has keynoted dozens of conferences and given seminars to many thousands of people worldwide. Ron co-develops the landmark BRS methodology featuring numerous innovative techniques including the popular RuleSpeak® (free on These are the latest offerings in a 45-year career that has consistently featured creative, business-driven solutions. Ron is recognized internationally as the ‘father of business rules.’ In 2017 he was co-author with John Zachman and Roger Burlton of the Business Agility Manifesto ( Mr. Ross holds an M.S. in information science from the Illinois Institute of Technology and a B.A. from Rice University. .

The Killer App for DMN by Bruce Silver (Bruce Silver Associates)

When I introduced DMN to DecisionCamp in 2016, the standard was brand new. It promised something unusual for a standard: Beyond a way to create decision requirements, DMN gave business users the ability to create executable decision logic themselves, without programming, using tabular formats called boxed expressions and a friendly expression language FEEL for the table cells. By 2017, Red Hat had provided an open source runtime for it, but most vendors claiming DMN support still neglected FEEL and boxed expressions.
Fast forward to today: Although the posture of Decision Management vendors is largely unchanged, the world of software has changed. The cloud has transformed it completely. REST APIs are everywhere. And you can’t hire skilled programmers for love or money. As a result, the demand for Low-Code Business Automation is exploding, and incorporating boxed expressions and FEEL into BPMN offers a standards-based platform for that.

In this presentation, we’ll see how that works on the Trisotech platform, where DMN is used for all modeler-defined business logic – not just decisions – and for mapping between process variables and the REST APIs of essentially all process tasks. Now subject matter experts can create complex Business Automation solutions themselves, without programming, and deploy them in one click as cloud REST services.
There are many Low-Code platforms today based on proprietary script. FEEL and boxed expressions are not only non-proprietary but more powerful and more business-friendly. Given the stubborn resistance of DM vendors, the real opportunity for DMN is not Decision Management but Low-Code Business Automation. Keywords: DMN, Low-Code, Business Automation

Bruce Silver is a well-known consultant, industry analyst, and educator specializing in process modeling, decision modeling, and Low-Code Business Automation. He is founder and Principal of and BPMessentials, the world’s leading providers of BPMN and DMN training and certification. Author of BPMN Method and Style and DMN Method and Style, Dr Silver served on the OMG technical committees that developed and revised the BPMN and DMN standards. Previously, he was Vice President in charge of workflow and document management research at the industry analyst firm BIS Strategic Decisions (which became Giga Information Group, now Forrester Research). He holds Physics degrees from Princeton and MIT, and four patents in imaging and workflow.

Knowledge Driven Decision Management by Denzil Wasson and Larry Goldberg (Sapiens Decision)

Case study of the use of knowledge and decision management allowing business analysts to provision business solutions. We introduce the concept of a knowledge/decision driven method to model and produce event-based architecture solutions. This approach effectively externalizes business logic and the surrounding use cases to create situationally aware solutions that respond to change in the hands of the business.

Sectors: Banking/Mortgage (Demo use case) – approach and technologies are sector neutral.

Technologies: Sapiens Knowledge, Sapiens SAFr, Sapiens Decision. Keywords: Knowledge, Decision management, Event-based architecture, Situationally aware solutions

Denzil Wasson is responsible for Sapiens Decision Suite technical strategy and delivery. Denzil leverages 30+ years of diverse technology, architecture and implementation experience in banking, insurance, retail, state and federal government to ensure customer success in their technology initiatives

Larry Goldberg is an evangelist for Sapiens DECISION, and as a member of the senior management team is responsible for all products in the Sapiens Decision company. He was Co-founder and Managing Partner of Knowledge Partners International LLC, acquired by Sapiens Decision, and has over forty years of experience in building technology based companies on four continents. Commercial applications in which he played a primary architectural role include such diverse domains as banking, healthcare, supply chain, property & casualty insurance, and enterprise modeling tools. He has been the business lead and/or business sponsor on many major projects in both the public and private sector, and is a trusted adviser to senior executives from major corporations. Larry is a leading international authority on business requirements, and is the co-author of the best-selling book “The Decision Model: A Business Logic Framework Linking Business and Technology” (Auerbach, New York 2009).

Introduction of DMN in projects: A marathon and not a sprint by Daniel Schmitz-Hübsch (Materna)

In the past years the DMN standard has been further developed. Among other things, attention has been paid to improving the user-friendliness of decision modeling. At the same time, the DMN standard was increasingly introduced in customer projects. Especially the balancing between the acceptance of the business side and the technical implementation has been a challenge to break resistance. The presentation will recapitulate how the acceptance of DMN has developed in various real-world projects over the last years and how the adaptations and improvements of the DMN standard have been received. Furthermore, the tools that have proven useful in the requirements engineering will be presented. However, the development of the DMN standard and the tools is not yet complete. At the end of the presentation, concrete suggestions for improvement of the DMN standard as well as the tools based on the project experiences will be discussed to further increase the use and acceptance of DMN. Keywords: DMN, FEEL, Rule Engines

Daniel holds a Master degree in Business Informatics with focus on mobile oriented analysis of business processes. For eight years, he has been involved in the modelling and technical implementation of business process- and decision management systems. As a software developer for an independent IT company, he is responsible for the development of high-availability decision applications using rule engines like IBM Operational Decision Management and RedHat Drools.

Marine Autonomy and Decisions by Guilhem Molines and Don Scott (IBM)

Mayflower Autonomous Ship is a custom-built vessel which has recently crossed the Atlantic ocean with only software and no human at the helm. Designed by UK-based Marine AI, with IBM software, the AI Captain in charge of the navigation combines various AI components, including Machine Learning, Decision and Optimization Models.
This session presents the vision for the project, and will focus on the technology in use to take decisions in this hostile environment with no human intervention. The talk will also give an update on the whereabouts of the ship. Keywords: AI, Decision, Autonomy, Marine

Guilhem Molines, Decision Chief Architect, IBM. With a background in fundamental Computer Science and Artificial Intelligence, Guilhem Molines has been involved with decision technology for more than two decades, in various roles in the field and in the development Lab. Today, he is the Chief Architect of the IBM team building Decision Technology With a special focus on the knowledge modeling and business user experience, Guilhem is always in close contact with users and practitioners and willing to find innovative ways to make the authoring of decisions an easier task for the industry. Since 2020, Guilhem leads the architecture of the next generation of decisioning platform and has also been involved in the navigation system of the unmanned Mayflower Autonomous Ship.

Deontic DMN: Representing deontic statement in DMN with bitstring semantics by Wai Wong, Joost Vennekens, Walter Schaeken, Lorenz Demey (KU Leuven)

Decision Model and Notation (DMN) is a standard for modelling operational decisions. It is intended to be used in an industrial setting by domain experts, who may not have a background in logic. In many settings, the decision processes that need to be modelled refer to deontic concepts, such as obligation, prohibition and permission. However, since DMN does not contain such concepts, domain experts must model them implicitly. This may lead to problems, for example, because of an inability to distinguish deontic inconsistency from definitional inconsistency. To solve this issue, we propose to extend DMN with deontic concepts. We use bitstring semantics for this, a recent logical framework developed in logical geometry. In this way, we can nicely incorporate the deontic hexagon, a logical diagram that captures the relations among different deontic concepts, into DMN.

In this talk, we will present this deontic extension of DMN and its advantages and challenges. For example, one advantage is the possibility to combine multiple deontic decisions rendered by different decision tables into a single output column according to deontic logic, as opposed to multiple output columns in traditional DMN. Meanwhile, one example challenge would be that humans interpret deontic terms differently than how it is defined in logic (e.g., a permission is often interpreted as “permitted but not obligated” instead of the logical “permitted and possibly obligated”), a phenomenon known as scalar inference. We will discuss our approaches in solving those challenges and their potential implementations with our reasoning engine e.g. IDP-Z3. Keywords: DMN, Bitstring semantics, Deontic logic, Scalar inference

Wai Wong is a doctoral researcher at KU Leuven. After receiving his educational training in both formal sciences and psychology, he situates his research in the broad field of cognitive science, with the current focus on knowledge engineering in transforming human mental knowledge to A.I. computable knowledge. He mainly works on the interdisciplinary links between psychology and computer science by studying the psychology of human reasoning and the formal representation of knowledge in the computational system.

Joost Vennekens is an associate professor at KU Leuven Campus De Nayer in Sint-Katelijne-Waver, Belgium. His research is concerned with AI technology (both Knowledge Representation and Machine Learning) and its industrial applications. He belongs to the research group EAVISE, which focuses on AI, computer vision and embedded systems, and to the research group DTAI, which studies declarative languages and AI. He is a member of the board of the Benelux Association for Artificial Intelligence and of the board of the Leuven.AI institute.

Walter Schaeken is a full professor at KU Leuven. His research interests are in Cognitive Psychology, Logic and Pragmatics. He is known for his contributions in the areas of deductive reasoning (in particular for his work on propositional and relational reasoning) and pragmatics (in particular for his work on scalar implicatures). He is currently substantiating this work with bitstring semantics, which offers an attractive method (at the intersection between the computational and algorithmic levels) to model, refine and integrate existing experimental data, and even suggests new experimental setups.

Lorenz Demey is a research professor (BOFZAP) at KU Leuven. His research is situated in the broad field of philosophical logic, incl. its various interdisciplinary links (in linguistics, computer science, psychology, etc.) He mainly works on the research program of logical geometry, and is currently heading an ERC Starting Grant (STARTDIALOG) in this area. Demey works at the Center for Logic and Philosophy of Science, and is also affiliated to the KU Leuven Institutes for intellectual history (LECTIO) and artificial intelligence (Leuven.AI).

Explaining Decisions made with both Business Rules and Machine Learning by Greger Ottosson (IBM)

Historically, we’ve considered decision management as intrinsically explainable. Through a “rule trace” we can introspect and understand why a decision model produces a specific outcome. However, while a trace might be sufficient to allow the rule author to debug a model, this procedural understanding doesn’t lend itself well to explain the decision to the end user (such as the recipient of a mortgage approval, product recommendation or fraud alert). It is therefore not uncommon to construct a tailored explanation as a side-effect of the actual decision, such as the “reason codes” common with rules and score cards. While reason codes are helpful, constructing and aggregating them requires work and they don’t directly provide information about what would need to change in order to arrive at a different decision – a so-called “counterfactual” explanation.

While explaining decisions made with rules are hard enough, the inclusion of Machine Learning models adds additional complexity. Unless these ML models are constrained to transparent types – such as smaller rule sets, decision trees or score cards – we’re are now faced with explaining (perhaps multiple) black-box ML models within the context of business rules. While it’s possible to treat the entire decision as a black-box model, ignoring the declarative information present in the business rules can lead to confusing explanations. In this presentation, we will present new research with the long-term goal of providing a unified approach to explaining decisions based on both ML-based predictions and business rules – providing both attributions to input variables and counterfactual explanations.

Keywords: Machine Learning, Explainability, Explainable Decisions

At IBM, Greger Ottosson leads product strategy for applying AI to improve digital business automation. Current work is focused on using Machine Learning to improve operational decisions, automate workflows and infuse ML in business applications. Greger is an experienced business leader and product manager and has deep experience in advanced analytics, data science and enterprise software. He holds a PhD in Computer Science from Uppsala University in Sweden

Data Enriched Knowledge at your fingertips: delivering contextualized Clinical Decision Support by Davide Sottara, Jane Shellum, Adam Bartscher and Deborah Sita (Mayo Clinic)

Most Clinical Decision Support (CDS) Systems used for actual patient care are based on Event-Condition-Action (ECA) Rules. Standards such as SMART on FHIR and CDS Hooks are the de facto way to access the necessary patient data. Various forms of AI, including decisions, rules, and predictive models, formalize when, whether and how to process the data to derive insights and recommendations. Comparatively, there are fewer standard methodologies to deliver the actual inferences back to patients and/or care providers, especially when explanations, background knowledge and contextualization are necessary to build trust and illustrate the proposed course of action.

To this end, we propose an approach based on the contextualization of intelligent content. Editors with clinical expertise curate the background medical knowledge and expert insights as semi-structured, modular content using the Darwin Information Typing Architecture (DITA) standard. Knowledge Engineers augment the content with semantic annotations, to formalize aboutness, applicability and contextualizable parameters. DITA’s built-in pattern matching mechanism allows to assemble, filter, (de)emphasize and/or specialize the content, using features of the patient case, derived using a combination of inference techniques.

We have further established a predicate logic interpretation of the contextualization rules, which supports the implementation of a real-time templating engine, but also enables the use of techniques such as consistency checking and satisfiability to verify the correctness and completeness of the content with respect to the intended scenarios.

We have implemented the framework as part of a cognitive support application, which delivers care guidance for Mayo Clinic patients with cardiac conditions.

Keywords:  Clinical Decision Support, DITA, Intelligent Content, Decision Models

Dr. Sottara obtained his Ph.D. in Computer Science Engineering from the University of Bologna, specializing in applied Artificial Intelligence, working on hybrid intelligent systems, integrating data mining and predictive analytics techniques, logic-based systems, uncertain reasoning and semantic web technologies. He works as the Principle Knowledge Engineer in the Knowledge Management and Delivery (KMD) Section at the Mayo Clinic. Dr. Sottara is also actively involved in Standards Defining Organizations such as HL7 and the Object Management Group, where he focuses on the standardization and exchange of clinical data and knowledge.

Jane Shellum is the former head of the Knowledge Management and Delivery Section at the Mayo Clinic. The section is responsible for the people, processes, and technology used to acquire, catalog, store, and deliver core clinical content, and supports multiple knowledge management and delivery products. Her prior experience includes 6 years as the administrator of the Education Technology Center in the Mayo Clinic College of Medicine, and 10 years as a Systems Analyst. In that role, she helped to implement multiple electronic medical record modules in the inpatient setting, including pharmacy, nurse charting, and orders. She is currently active in the OMG’s BPM+ Health community and is working as a knowledge engineer, modeling clinical pathways. Ms. Shellum has a Bachelor’s Degree in Human Biology from Stanford University, a Master’s Degree in Healthcare Administration from Duke University, and a Master’s Degree in Clinical Informatics from Arizona State University.

Adam Bartscher has worked as a Clinical Knowledge Engineer within the Center for Digital Health at the Mayo Clinic for four and a half years. He works in a cross-functional agile team of engineers and business proponents to deliver Mayo Clinic’s knowledge assets alongside patient data in multiple clinical applications. Mr Bartscher has a Bachelor’s Degree in Kinesiology from the University of Minnesota.

Deborah Sita works as a Clinical Knowledge Engineer modeling clinical pathways in the Knowledge Management and Delivery (KMD) Section at the Mayo Clinic. The KMD section is responsible for the people, processes, and technology used to acquire, catalog, store, and deliver core clinical content, and supports multiple knowledge management and delivery products. She has a Bachelor of Science Degree in Nursing from East Carolina University, and a Master’s Degree in Healthcare Administration from the University of Maryland.

Intelligent Assistance for Knowledge Workers by Denis Gagné (Trisotech) and Tom DeBevoise (Advanced Component Research)

Knowledge Workers, a term coined by Peter Drucker, are workers whose job is to think for a living. Knowledge work can be differentiated from other forms of work by emphasizing continuously evolving non-routine problem-solving based on information. As businesses increase their dependence on information technology via digital transformation, the number of fields in which knowledge workers must operate has expanded dramatically.

Today, much of the knowledge work accomplished involves informal collaborations via emails supported by attached documents (PDFs and others). Fundamentally, knowledge workers spend much of their time acting as human integrators of unstructured information exchanged via unstructured communications and collaborations.

In support of these efforts, Intelligent Document Process (IDP) technologies were introduced by various vendors to transform unstructured and semi-structured information into usable data. The ultimate objective of most IDP capabilities is to integrate with downstream systems such as ERP. They tend to be based on pattern matching supported by Machine Learning (ML) technologies. To become effective, these approaches require varying quantities of representative information being available or supervised learning and labeling techniques that is yet another form of knowledge work. But what if an adequate sample of examples or information are not available for a particular type of knowledge work? And how do we support knowledge workers and their actual flow of work?

In this session, we will present a combination of symbolic and non-symbolic reasoning techniques to ease the burden on knowledge workers by offering intelligent just-in-time assistance. This approach is based on open international workflow and decision standards and anchored on the low-code Friendly Enough Expression Language (FEEL) from the Decision Model and Notation (DMN). We use Natural Language Processing (NLP) to enable knowledge-based workflows with channels of intelligent email messages. NLP detection, mediated by decision models of email-created events triggers the flow of knowledge work, detects intermediate business events, route attachments and results for approval or exceptions, and provides useful information to knowledge workers, including calendar events, contacts, and various reports. A Real Estate Closing Process will be used as an example. Keywords: Knowledge Work, Intelligent Document Process (IDP), Natural Language Processing (NLP), Decision Model and Notation (DMN), Friendly Enough Expression Language (FEEL)

Denis Gagné is CEO and CTO of Trisotech, a leading Standard Based Low-Code Intelligent Automation enterprise software vendor. For more than two decades, Denis has been a driving force behind most international BPM standards in use today. Denis is a member of the steering committee of the BPM+ Health Community of Practice, where he also leads the Ambassador program. For the Object Management group (OMG), Denis is Chair of the BPMN Interchange Working Group (BPMN MIWG) and an active contributing member to the Business Process Model and Notation (BPMN), the Case Management Model and Notation (CMMN), and the Decision Model and Notation (DMN) work groups.

Tom Debevoise has extensive experience in Process and decision modeling using BPMN, DMN and FEEL. Tom Debevoise focuses on next-generation IT solutions for business operations as a technology leader and cloud solutions architect. Tom works on the next generation of intelligent, practical, cloud-based services. Tom is developing these with an “intelligent digital assistant”, using Natural Language Processing, APIs to massively integrated services, and a core set of responsive processes, decision making, and analytics. Tom has held various positions at companies such as Oracle, Bosch, and Signavio.

A practical guide to a decision migration project by Carole-Ann Berlioz (Sparkling Logic)

While many green-field projects get built year after year, modernization projects seem to dominate the decision management space. This is no surprise, especially given that Charles Forgy invented and presented his RETE algorithm – which gave birth to this industry – 35 years ago already. Many generations of solutions have been implemented since then, and quite a few are due for an overhaul.

As for any project, companies are faced with a spectrum of options ranging from fully automated to a complete rewrite. However, decision projects differ from IT projects in the sense that business analysts, the intended owners of decision logic, must play a major role, regardless of whether or not they have been historically. With this in mind, considerations and design choices are unique, for the better in fact. Opportunities are lurking in these decision projects awaiting their new form.

In this presentation, Carole-Ann will focus on the pros and cons of several techniques she has personally adopted for streamlining these migration projects. She will cover low-hanging fruits for quick wins and successful projects, as well as long-term guidelines. These best practices are designed to be actionable for any migration project aiming at automating decisions with an emphasis on “business analyst ownership”. They are not product-specific, although they rely on state-of-the-art capabilities not always available out-of-the-box.

Carole-Ann Berlioz is Co-Founder and Chief Product Officer at Sparkling Logic, a leading Decision Management Platform vendor known for its innovation. Over a few decades, she has led product management and strategy for generations of award-winning business rules, predictive analytics, and optimization products. In 2010, she teamed up with Chief Architect and CTO Carlos Serrano-Morales to create Sparkling Logic, a “Cool Vendor” that has gained momentum around the world, uniquely serving Business Analysts with an intuitive yet comprehensive, fully integrated decision manager, SMARTS™. In addition to her visionary role, she also takes pride in building client projects for financial services and insurance companies. Her hands-on expertise fuels her creativity in this industry, recognized with several patents in Decision Management and Adaptive Analytics.

Is Python a good fit for DMN? An exploration of a Python implementation by Russell Mc Donell (C-Cost System Pty Ltd)

This presentation examines the synergies between the Python programming language and FEEL, the expression language defined in the DMN specification. An implementation of DMN in Python is explored, including short comings and deficiencies of a Python implementation. Than an application built in Python, using a Python DMN module, is described. This application creates API access to DMN built rules engines and can be run in Microsoft Azure as a Program As A Service. Keywords: Python, Excel, pyDMNrules, pySFEEL

Russell McDonell is a health informatitian with 50yrs IT experience and has progressed from FORTRAN to BASIC to C to Perl to Python, with several other language detours along the way. Russell develops solutions for IT problems in healthcare, such as clinical costing and auto-coding of pathology reports. His programming language of choice is Python and his decision implementation of choice is DMN. However, as there was no DMN support in any Python module, he decided to create pyDMNrules and share it with fellow Python developers.