CLIPS (C Language Integrated Production System) is a public‑domain software tool designed for building expert systems. Developed at NASA’s Johnson Space Center beginning in 1985, CLIPS provides a cohesive environment for rule‑based, object‑oriented, and procedural programming. Its inference engine, based on the Rete algorithm, allows efficient pattern matching, making it widely used in academic, industrial, and research settings for knowledge representation and reasoning tasks. CLIPS supports forward chaining, backward chaining (via the COOL object‑oriented extension), and modular rule sets, and it can be embedded into larger applications or run as a standalone interpreter.
1 History
1.1 Origins at NASA
CLIPS was created at the NASA Johnson Space Center in 1985 as an in‑house tool to support the development of expert systems for space‑related applications. The project was led by Gary Riley, and its design emphasized portability, efficiency, and ease of integration with existing software. The first version was written in the C programming language, giving the tool its name.
1.2 Version Evolution
1.2.1 CLIPS 6.x and 7.x
The 6.x series (released in the early 1990s) introduced many of the core features that defined modern CLIPS, including the COOL object‑oriented extension and support for modular rule sets with definites and defmodules. Version 7.x brought incremental improvements in performance, debugging tools, and cross‑platform compatibility, solidifying CLIPS as a reliable expert‑system shell.
1.2.2 Transition to CLIPS 8
In 2021, the project transitioned to CLIPS 8, a major re‑implementation that modernized the codebase, improved the integration with C‑based APIs, and enhanced support for modern operating systems. CLIPS 8 remains backward‑compatible with earlier versions while offering a streamlined architecture.
1.3 Open Source Status
CLIPS has been in the public domain since its early releases, allowing unrestricted use, modification, and distribution. The source code is freely available, which has fostered a wide community of users and contributors across academic, commercial, and hobbyist environments.
2 Technical Overview
2.1 Architecture
CLIPS is composed of three main subsystems: the inference engine, the knowledge base, and the user interface.
2.1.1 Inference Engine
The inference engine controls the execution of rules. It operates in a cycle of pattern matching, conflict resolution, and rule firing.
2.1.1.1 Rete Network
The core of the inference engine is the Rete network, a data‑driven algorithm that efficiently matches facts against rule conditions.
###### 2.1.1.1.1 Pattern Matching
When rules are defined, their left‑hand side conditions are compiled into a Rete network. As new facts are asserted or modified, the network incrementally updates the set of rule activations, avoiding redundant recomputation and achieving high performance even with large knowledge bases.
2.1.2 Knowledge Base
The knowledge base stores facts (declarative data) and rules (procedural knowledge). Facts are organized as instances of templates or ordered lists. Rules are maintained in modules, allowing for scoping and separation of concerns.
2.1.3 User Interface
CLIPS provides both a command‑line interface and a graphical IDE. The CLI allows batch execution and scripting, while the IDE offers a visual environment for editing rules, facts, and debugging.
2.2 Programming Paradigms
CLIPS supports three distinct but interoperable paradigms.
2.2.1 Rule‑Based Programming
The primary paradigm. Rules are condition‑action pairs; when all conditions of a rule are satisfied, the rule is placed on the agenda. After conflict resolution, the rule fires, executing its actions (e.g., asserting or retracting facts). This forward‑chaining strategy is the default.
2.2.2 Object‑Oriented Programming (COOL)
The CLIPS Object‑Oriented Language (COOL) extends the system with classes, instances, message passing, and inheritance. COOL can be used alongside rule‑based programming, allowing hybrid knowledge representations.
2.2.3 Procedural Programming
CLIPS also includes procedural elements such as definable functions, loops, and conditional statements. These are useful for computations that do not fit naturally into a rule‑based style.
3 Core Components
3.1 Facts
Facts represent the current state of knowledge. Two primary types exist.
3.1.1 Ordered Facts
An ordered fact is a simple list of values, e.g., (temperature high). They are defined without an explicit schema and are easy to use for quick prototyping.
3.1.2 Template Facts
Template facts are based on a deftemplate construct, which defines named slots with specific types and default values. For example, (person (name "Alice") (age 30)). Template facts provide type checking and structured data.
3.2 Rules
A rule consists of two parts: conditions and actions.
3.2.1 Left‑Hand Side (LHS) Conditions
The LHS contains patterns that facts must match. Patterns can include variable bindings, logical conditions (AND, OR, NOT), and constraints on slot values. The LHS may also contain test functions for complex checks.
3.2.2 Right‑Hand Side (RHS) Actions
The RHS contains actions executed when the rule fires. Common actions include assert (add a fact), retract (remove a fact), modify (change a fact), printout, and calls to user‑defined functions or external C routines.
3.3 Deftemplate Constructs
The deftemplate construct defines a fact template with slots, types, default values, and cardinality. For instance:
(deftemplate person (slot name (type STRING)) (slot age (type INTEGER)))Templates are essential for structured knowledge representation.
3.4 Defrule and Defmodule
Rules are defined using defrule. Each rule has a name, an optional comment, and the LHS/RHS pair. Modules are defined with defmodule to group rules and facts into separate namespaces, promoting modularity and preventing name collisions. Rules can explicitly reference facts in other modules.
4 Object‑Oriented Extension (COOL)
4.1 Classes and Instances
COOL introduces classes defined with defclass. Each class is a template for objects (instances) with slots. Instances are created with make-instance and can be manipulated similarly to facts.
4.2 Message Passing
COOL uses a message‑passing model. Handlers (methods) are defined for classes and respond to messages sent to instances. For example, a class animal may have a handler for the speak message.
4.3 Inheritance and Polymorphism
Classes support single and multiple inheritance. A subclass can override or extend methods from its parent(s). Polymorphism allows code to operate on objects of different classes through the same message interface.
5 Development Tools
5.1 CLIPS IDE (Windows)
The CLIPS IDE provides a graphical environment for Windows users. It includes a text editor with syntax highlighting, a fact inspector showing the current knowledge base, an agenda viewer listing pending rule activations, and a trace window for debugging execution steps.
5.2 CLI and Batch Usage
On all platforms, the CLIPS executable can be run in command‑line mode. Commands can be typed interactively or loaded from batch files. This mode is favored for scripting, automated testing, and deployment on servers or embedded systems.
5.3 Integration with C/Java via APIs
CLIPS exposes a C API (found in clips.h) that allows embedding the inference engine directly into C programs. Data can be exchanged by calling CLIPS functions from the host code or by using callback functions. Similarly, a Java API (JCLIPS) enables integration with Java applications, though the primary C API remains the most widely used.
6 Applications
6.1 Expert Systems
6.1.1 Diagnostic Systems
CLIPS has been used to build diagnostic expert systems in fields such as medicine, electronics, and aerospace. Rules encode expert knowledge to identify faults from observed symptoms.
6.1.2 Configuration Advisors
Configuration systems (e.g., for computer hardware or industrial equipment) rely on CLIPS to enforce constraints and recommend valid component combinations. The Rete algorithm efficiently handles large sets of constraint rules.
6.2 Education and Training
CLIPS is widely adopted in university courses on artificial intelligence and expert systems. Its simple syntax and public‑domain status make it ideal for teaching rule‑based reasoning. Many textbooks use CLIPS as the primary demonstration platform.
6.3 Research in Artificial Intelligence
Researchers in knowledge representation, reasoning under uncertainty, and hybrid intelligent systems have used CLIPS as a baseline or prototyping tool. Its modularity and extendibility allow experimentation with custom inference strategies.
7 Comparison with Other Rule Engines
7.1 CLIPS vs. Jess
Jess (Java Expert System Shell) is a rule engine for the Java platform that was inspired by CLIPS. Jess uses a similar syntax and Rete algorithm but is not open‑source and is oriented toward Java integration. CLIPS remains the preferred choice for environments requiring a C‑based embedding or public‑domain licensing.
7.2 CLIPS vs. Drools
Drools is a modern, enterprise‑grade rule engine written in Java, featuring a rich domain‑specific language, support for decision tables, and integration with business process management. CLIPS is more lightweight and simpler to learn, while Drools offers more advanced tooling and scalability for large business rule systems.
7.3 CLIPS vs. OPS5
OPS5 (Official Production System 5) is an earlier rule‑based language that also uses the Rete algorithm. CLIPS was directly influenced by OPS5 but improved upon it with an integrated procedural and object‑oriented paradigm, a more user‑friendly syntax, and a public‑domain license.
8 Community and Resources
8.1 Official Documentation
The primary source of documentation is the *CLIPS Reference Manual* and *CLIPS User’s Guide*, both available on the official CLIPS web site. These cover syntax, semantics, and API details.
8.2 User Forums and Mailing Lists
The CLIPS community is active on the SourceForge project page and the Google Groups mailing list “CLIPSESG”. Users discuss technical issues, share code, and provide support for new adopters.
8.3 Example Code Libraries
Numerous example expert systems and code libraries are distributed with the CLIPS package, including the classic “monkey and bananas” problem, a “house‑expert” system, and sample COOL programs. Online repositories, such as GitHub, contain additional community‑contributed projects.