Rule Engine's History: From AI research to enterprise automation tools
Charles Forgy introduced the first widely adopted rule engine
In this blog, we will trace the story of rule engines and how they came into play. What role is it currently playing and where will it eventually lead us?The History of Expert Systems (1960s–1970s)
The first rule engines were made possible by Stanford University's groundbreaking AI research in the 1960s. Initially, systems that employed rules for decision-making was DENDRAL (1965), which applied rules methodically to identify molecular structures from mass spectrometry data.
MYCIN (1976), a breakthrough was created to identify bacterial infections. By employing simple rule formats that mimic "If-then" functionality, experts, even without any coding background in the healthcare industry, could comprehend and alter without the need for programming knowledge or occupying tech teams. MYCIN introduced the revolutionary idea of separating knowledge (medical rules) from reasoning mechanisms. Although simple rules, they still needed coding and occupied tech teams. MYCIN was, however, still in use only for medical fields; this technology needed to be made industry-agnostic.
Production Systems and the RETE Algorithm (1970s-1980s)
Charles Forgy introduced the RETE algorithm with OPS5 (1977) at Carnegie Mellon, which became the first widely adopted rule engine. RETE solved the performance problem of checking every rule against every fact by creating an incremental network structure that dramatically improved efficiency.
R1/XCON, built with OPS5 at Digital Equipment Corporation, became the first major commercial success. By 1986, it was processing 80% of DEC's (Digital Equipment Corporation) VAX (Virtual Address eXtension) computer configurations, proving that rule engines could handle enterprise-scale problems.
Charles Forgy introduced the first widely adopted rule engine
The future of Rule Engine looks promising
Commercial Growth and the AI Winter (1980s-1990s)The 1980s saw commercial expert system shells like ART (Automatic Reasoning Tool) and KEE (Knowledge Engineering Environment) helping rule engines solve the pain points of businesses. Alas, this growth came to a screeching halt when, in the late 1980s, "AI Winter" brought reduced interest and funding, with many companies failing due to systems that were difficult to maintain and couldn't handle uncertainty. According to Forbes, the market for LISP machines, which were powering these expert systems, crashed overnight, which was the main reason for the AI Winter.
Despite commercial setbacks, research and development continued in areas like fuzzy logic and probabilistic reasoning that would later influence the systems we use today and also affect how we approach a business problem.
Business Rules Management Renaissance (1990s-2000s)
The mid-1990s brought renewed interest driven by enterprise software needs,
object-oriented programming, and internet growth. ILOG Rules (Intelligence
Logicience, which is French for Intelligent Software), which was later rebranded
as IBM ODM (the flagship decision-making software by IBM) in 2009, pioneered
separating business rules from application code, allowing business analysts to
modify logic without programming. The seed of No-Code automation was thus
planted.
Blaze Advisor, developed by FICO (Flair Isaac Corporation), focuses on decision management with statistical analysis and A/B testing capabilities for rule performance optimization.
Open Source Revolution (2000s-2010s)
The following 3 rule engines were the most prominent in this era:
- Drools, the most popular open-source rule engine, implemented RETE-OO for object-oriented environments and provided natural language-like rule authoring.
- Jess brought expert system functionality to Java
- OpenRules made rules accessible through familiar spreadsheet interfaces.
Business Rules Management Systems Era
Vendors began offering complete platforms with rule authoring tools, version control, testing frameworks, and high-performance execution engines. With the AI abandonment of major player such as Texas Instruments and Xerox during the "AI Winter" phase, some of the show stealers in that era were IBM WebSphere ILOG JRules, FICO Blaze Advisor, Oracle Business Rules, and Progress Corticon.
Contemporary Evolution (2010s-Present)
Now the advancements are centred around:
Current Landscape
- Cloud-Native Architecture: Rule engines become conveniently consumable by smart, contemporary applications due to integration of microservices, support for containers, and API-first design.
- AI Integration: Explainable rule-based reasoning and machine learning-based pattern recognition are combined in hybrid systems. In the current situation, model governance is used by rule engines to control machine learning models.
- Low-Code Platforms: Natural language processing (NLP) and visual rule builders facilitated rule development for non-technical users. This is a tremendous development as it took the control away from development teams and gave it to the end users, non-development teams/functional teams. So, eliminating any communication gap and minimizing the project timelines.
- Real-Time Decisions: In-memory execution and complex event processing provide sub-millisecond decision-making for high-frequency applications.
The current market offerings include:
What does the future hold?
- Enterprise solutions: IBM ODM, FICO Decision Management, Pega Platform
- Open source: Drools (now part of KIE suite), Easy Rules, OpenL Tablets
- Cloud platforms: AWS Business Rules, Azure Logic Apps, Google Cloud Workflows
The future holds a lot of opportunities for the rule engines. The following are some
of them. The list is not exhaustive:
- Explainable AI: Rule engines make AI decision-making transparent
- Natural language authoring: Converting business needs into executable rules
- Autonomous systems: Logical frameworks for robotics and driverless cars
- Real-time compliance: Automated regulatory monitoring and reporting
The future of Rule Engine looks promising
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