DOCSLIB.ORG

Drools Expert User Guide

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Drools Expert User Guide Drools Expert User Guide Version 5.5.0.CR1 by The JBoss Drools team [http://www.jboss.org/drools/team.html] ....................................................................................................................................... vii 1. Introduction ................................................................................................................. 1 1.1. Artificial Intelligence ............................................................................................ 1 1.1.1. A Little History ......................................................................................... 1 1.1.2. Knowledge Representation and Reasoning ................................................ 2 1.1.3. Rule Engines and Production Rule Systems (PRS) .................................... 3 1.1.4. Hybrid Reasoning Systems (HRS) ............................................................ 5 1.1.5. Expert Systems ........................................................................................ 8 1.1.6. Recommended Reading ........................................................................... 9 1.2. Why use a Rule Engine? .................................................................................. 12 1.2.1. Advantages of a Rule Engine ................................................................. 13 1.2.2. When should you use a Rule Engine? ..................................................... 14 1.2.3. When not to use a Rule Engine .............................................................. 15 1.2.4. Scripting or Process Engines .................................................................. 15 1.2.5. Strong and Loose Coupling .................................................................... 16 1.3. Rete Algorithm .................................................................................................. 16 2. User Guide ................................................................................................................. 23 2.1. The Basics ....................................................................................................... 23 2.1.1. Stateless Knowledge Session ................................................................. 23 2.1.2. Stateful Knowledge Session .................................................................... 26 2.1.3. Methods versus Rules ............................................................................ 31 2.1.4. Cross Products ...................................................................................... 32 2.2. Inference .......................................................................................................... 33 2.2.1. Buss Pass Example ............................................................................... 33 2.3. Truth Maintenance with Logical Objects ............................................................. 35 2.3.1. Overview ............................................................................................... 35 2.4. Decision Tables in Spreadsheets ....................................................................... 40 2.4.1. When to Use Decision Tables ................................................................. 41 2.4.2. Overview ............................................................................................... 41 2.4.3. How Decision Tables Work ..................................................................... 43 2.4.4. Spreadsheet Syntax ............................................................................... 46 2.4.5. Creating and integrating Spreadsheet based Decision Tables .................... 55 2.4.6. Managing Business Rules in Decision Tables .......................................... 56 2.4.7. Rule Templates ...................................................................................... 57 2.5. Templates ........................................................................................................ 59 2.5.1. The Rule Template File .......................................................................... 60 2.5.2. Expanding a Template ............................................................................ 61 2.5.3. Example ................................................................................................ 63 2.6. More on building and deploying ......................................................................... 66 2.6.1. Knowledge Base by Configuration Using Changesets ............................... 66 2.6.2. Knowledge Agent ................................................................................... 67 2.7. Logging ............................................................................................................ 70 3. API Reference ............................................................................................................ 73 3.1. Building ............................................................................................................ 73 iii Drools Expert User Guide 3.1.1. Building Using Code ............................................................................... 73 3.1.2. Building Using Configuration and the ChangeSet XML .............................. 78 3.1.3. Changing the Default Build Result Severity .............................................. 81 3.2. Deploying ......................................................................................................... 82 3.2.1. KnowledgePackage and Knowledge Definitions ........................................ 82 3.2.2. KnowledgeBase ..................................................................................... 83 3.2.3. In-Process Building and Deployment ....................................................... 85 3.2.4. Building and Deployment in Separate Processes ...................................... 86 3.2.5. StatefulknowledgeSessions and KnowledgeBase Modifications ................. 87 3.2.6. KnowledgeAgent .................................................................................... 87 3.3. Running ............................................................................................................ 90 3.3.1. KnowledgeBase ..................................................................................... 90 3.3.2. StatefulKnowledgeSession ...................................................................... 91 3.3.3. KnowledgeRuntime ................................................................................. 91 3.3.4. Agenda .................................................................................................. 99 3.3.5. Event Model ......................................................................................... 103 3.3.6. KnowledgeRuntimeLogger .................................................................... 106 3.3.7. StatelessKnowledgeSession .................................................................. 106 3.3.8. Commands and the CommandExecutor ................................................. 110 3.3.9. Marshalling .......................................................................................... 118 3.3.10. Persistence and Transactions .............................................................. 120 3.3.11. Drools Clips ....................................................................................... 122 4. Rule Language Reference ........................................................................................ 125 4.1. Overview ........................................................................................................ 125 4.1.1. A rule file ............................................................................................. 125 4.1.2. What makes a rule ............................................................................... 126 4.2. Keywords ........................................................................................................ 126 4.3. Comments ...................................................................................................... 128 4.3.1. Single line comment ............................................................................. 128 4.3.2. Multi-line comment ............................................................................... 129 4.4. Error Messages .............................................................................................. 129 4.4.1. Message format ................................................................................... 129 4.4.2. Error Messages Description .................................................................. 130 4.4.3. Other Messages ................................................................................... 134 4.5. Package ......................................................................................................... 134 4.5.1. import .................................................................................................. 135 4.5.2. global .................................................................................................. 136 4.6. Function ......................................................................................................... 137 4.7. Type Declaration ............................................................................................. 138 4.7.1. Declaring New Types ........................................................................... 139 4.7.2. Declaring Metadata .............................................................................. 141 4.7.3. Declaring Metadata for Existing
Load more

Recommended publications
  • K-SITE RULES: Integrating Business Rules in the Mainstream Software Engineering Practice
    K-SITE RULES: Integrating Business Rules in the mainstream software engineering practice José L. Martínez-Fernández(2), José C. González(1,2), Pablo Suárez(2) (1) DAEDALUS – Data, Decisions and Language, S.A., Avda. Albufera 321, Madrid, Spain [email protected], [email protected], [email protected] (2) ETSI Telecomunicación, Universidad Politécnica de Madrid Keywords: Business rules, rule engine, rule language, software development lifecycle models. Abstract: The technology for business rule based systems faces two important challenges: standardization and integration within conventional software development lifecycle models and tools. Despite the standardization effort carried out by international organizations, commercial tools incorporate their own flavours in rule languages, making difficult the migration among tools. On the other hand, although some business rules systems vendors incorporate interfaces to encapsulate decision models as web services, it is still difficult integrating business rules in traditional object-oriented analysis and design methodologies. This is the rationale behind the development of K-Site Rules, a tool that facilitates the cooperation of business people and software designers in business applications. 1 INTRODUCTION accessible storage, and this knowledge can be modified, updated and maintained in a quick and A Business Rule (BR) can be seen as an expression simple way. Second, companies need ways to foster used to make explicit the knowledge about the their reaction to new business opportunities, and the business present in an organization. According to the ability to adapt their IT systems to take benefit of Business Rules Group1 two definitions can be given these opportunities becomes a definitive advantage. for the term business rule, depending on the point of Third, from the IT perspective, the use of business view adopted: from the business point of view, rules allows a reduction of the effort needed to adapt where a business rule is “a directive, intended to software systems to new requirements.
    [Show full text]
  • Reactive Rules on the Web
    Reactive Rules on the Web Bruno Berstel1, Philippe Bonnard1,Fran¸cois Bry2,MichaelEckert2, and Paula-Lavinia P˘atrˆanjan2 1 ILOG 9, rue de Verdun, 94250 Gentilly, France {berstel,bonnard}@ilog.fr — http://www.ilog.com 2 University of Munich, Institute for Informatics Oettingenstr. 67, 80538 M¨unchen, Germany {bry,eckert,patranjan}@pms.ifi.lmu.de — http://www.pms.ifi.lmu.de Abstract. Reactive rules are used for programming rule-based, reactive systems, which have the ability to detect events and respond to them au- tomatically in a timely manner. Such systems are needed on the Web for bridging the gap between the existing, passive Web, where data sources can only be accessed to obtain information, and the dynamic Web, where data sources are enriched with reactive behavior. This paper presents two possible approaches to programming rule-based, reactive systems. They are based on different kinds of reactive rules, namely Event-Condition- Action rules and production rules. Concrete reactive languages of both kinds are used to exemplify these programming paradigms. Finally the similarities and differences between these two paradigms are studied. 1 Introduction Reactivity on the Web, the ability to detect events and respond to them automat- ically in a timely manner, is needed for bridging the gap between the existing, passive Web, where data sources can only be accessed to obtain information, and the dynamic Web, where data sources are enriched with reactive behavior. Reactivity is a broad notion that spans Web applications such as e-commerce platforms that react to user input (e.g., putting an item into the shopping bas- ket), Web Services that react to notifications or service requests (e.g., SOAP messages), and distributed Web information systems that react to updates in other systems elsewhere on the Web (e.g., update propagation among biological Web databases).
    [Show full text]
  • Production Systems and Rete Algorithm Formalisation Horatiu Cirstea, Claude Kirchner, Michael Moossen, Pierre-Etienne Moreau
    Production Systems and Rete Algorithm Formalisation Horatiu Cirstea, Claude Kirchner, Michael Moossen, Pierre-Etienne Moreau To cite this version: Horatiu Cirstea, Claude Kirchner, Michael Moossen, Pierre-Etienne Moreau. Production Systems and Rete Algorithm Formalisation. [Research Report] 2004, pp.21. inria-00280938 HAL Id: inria-00280938 https://hal.inria.fr/inria-00280938 Submitted on 20 May 2008 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Meta-compilation´ non intrusive du filtrage par contraintes Non intrusive meta-compilation of matching by constraints Sous Projet 1 RETE et re´ecriture´ Production Systems and Rete Algorithm Formalisation Description : The rete algorithm is a well-known algorithm for efficiently addressing the many patterns/many objects match problem, and it has been widely used and imple- mented in several applications, mainly production systems. But despite of the wide usage of production systems and the rete algorithm, to the best of our knowledge there has been just one proposition for a formal definition of the rete algorithm given by Fages and Lissajoux [FL92], but no attempt to give a formal description of production systems as a whole, giving rise to lots of ambiguities and incompatibilities between the different implementations.
    [Show full text]
  • Business Rules White Paper Version D0.2 June 2012
    NHSIA Business Rules White Paper Version D0.2 June 2012 Prepared for the Administration for Children and Families (ACF) National Human Services Interoperability Architecture Business Rules White Paper DRAFT Version D0.2 June 2012 Prepared by: The Johns Hopkins University Applied Physics Laboratory (JHU/APL) i NHSIA Business Rules White Paper Version D0.2 June 2012 Draft Issue It is important to note that this is a draft document. The document is incomplete and may contain sections that have not been completely reviewed internally. The material presented herein will undergo several iterations of review and comment before a baseline version is published. This document is disseminated in the interest of information exchange. The Johns Hopkins University Applied Physics Laboratory (JHU/APL) assumes no liability for its contents or use thereof. This report does not constitute a standard, specification, or regulation. JHU/APL does not endorse products or manufacturers. Trade and manufacturer’s names appear in this report only because they are considered essential to the object of this document. Note: This document and other NHSIA-related documentation are available for review from the NHSIA SharePoint site. Updates and any additional documents will be published on that site. The URL for the site is https://partners.jhuapl.edu/sites/HSNIA. The version D0.1 and D0.2 documents may be viewed or downloaded from the document library named NHSIA_Drafts. Review and comments to this document are welcome. To comment, either post your feedback in the NHSIA_Drafts_Comments library or send comments to [email protected]. Christine Salamacha The Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Road Laurel, MD 20723 Phone: 240-228-4976 E-Mail: [email protected] ii NHSIA Business Rules White Paper Version D0.2 June 2012 Table of Contents List of Figures ...............................................................................................................
    [Show full text]
  • Fuzzy Dynamic Discrimination Algorithms for Distributed Knowledge Management Systems
    Annals of “Dunarea de Jos” University of Galati Fascicle I – 2010. Economics and Applied Informatics. Years XVI – no 2 - ISSN 1584-0409 Fuzzy Dynamic Discrimination Algorithms for Distributed Knowledge Management Systems Vasile MAZILESCU Dunarea de Jos University of Galati [email protected] Abstract A reduction of the algorithmic complexity of the fuzzy inference engine has the following property: the inputs (the fuzzy rules and the fuzzy facts) can be divided in two parts, one being relatively constant for a long a time (the fuzzy rule or the knowledge model) when it is compared to the second part (the fuzzy facts) for every inference cycle. The occurrence of certain transformations over the constant part makes sense, in order to decrease the solution procurement time, in the case that the second part varies, but it is known at certain moments in time. The transformations attained in advance are called pre-processing or knowledge compilation. The use of variables in a Business Rule Management System knowledge representation allows factorising knowledge, like in classical knowledge based systems. The language of the first-degree predicates facilitates the formulation of complex knowledge in a rigorous way, imposing appropriate reasoning techniques. It is, thus, necessary to define the description method of fuzzy knowledge, to justify the knowledge exploiting efficiency when the compiling technique is used, to present the inference engine and highlight the functional features of the pattern matching and the state space processes. This paper presents the main results of our project PR356 for designing a compiler for fuzzy knowledge, like Rete compiler, that comprises two main components: a static fuzzy discrimination structure (Fuzzy Unification Tree) and the Fuzzy Variables Linking Network.
    [Show full text]
  • Using Cloud Computing with RETE Algorithms in a Platform As a Service (Paas) for Business Systems Development
    Using Cloud Computing with RETE Algorithms in a Platform as a Service (PaaS) for Business Systems Development Richard Poblete 1, David de la Fuente 2, and Margarita Alonso 3 1Department of Business Administration, Cantabria University, Santander, Spain [email protected] 2Department of Business Administration, Oviedo University, Gijón, Asturias, Spain [email protected] 1Department of Business Administration, Cantabria University, Santander, Spain [email protected] this technology is the Virtualization and its model of operative Abstract - Given that companies operate in an environment of business is varied and can include: Pay per Use Models; Free continuous evolution it requires information systems to have Models; Fee for Service Models and Free Use Models, greater adaptation, flexibility, scalability and reliability of sharing utilities. incorporation of these changes. Cloud Computing can be regarded as a paradigm and key element of recent computing in business management solutions. A business solution arises 1.1 Cloud Computing in the cloud; this solution includes both the environmental Cloud Computing is defined by the way in which the dynamics and the changes needed in the business logic service provider and the consumer of that service are related. assuring speed of response, efficiency in the solution and In this context it is very common that the consumers are not maintenance of the application. This article shows a design the owners of the infrastructure, they do not incur CAPEX 1, model of Saas applications based on a platform (PaaS) which since they are under a modality of rent and use from a has the business logic as central element and is subdivided in provider.
    [Show full text]
  • Evaluation and Implementation of Match Algorithms for Rule-Based Multi-Agent Systems Using the Example of Jadex
    Universität Hamburg Fakultät für Mathematik, Informatik und Naturwissenschaften Evaluation and Implementation of Match Algorithms for Rule-based Multi-Agent Systems using the Example of Jadex Master's Thesis Diplomarbeit Eryk Lagun [email protected] Studiengang Informatik Matrikelnummer: 5318142 Fachsemester: 16 Erstgutachter: Dr. Lars Braubach Zweitgutachter: Dr. Guido Gryczan Contents 1 Introduction.....................................................................................................1 1.1 Motivation and Goals....................................................................................................1 1.2 Context and Scope.........................................................................................................2 1.3 Intended Readers........................................................................................................... 4 1.4 Outline ........................................................................................................................... 4 2 Background......................................................................................................6 2.1 Rule-based Systems....................................................................................................... 6 2.1.1 Definition............................................................................................................ 7 2.1.2 Rule Execution................................................................................................... 8 2.1.3 Recognize-Act Cycle..........................................................................................8
    [Show full text]
  • Eclipse: a C-Based Inference Engine Em6edded in a Lisp Interpreter Brian Beach; Randolph Splitter Software and Systems Laboratory HPL-90-213 December, 1990
    HEWLETT PACKARD Eclipse: A C-based Inference Engine Em6edded in a Lisp Interpreter Brian Beach; Randolph Splitter Software and Systems Laboratory HPL-90-213 December, 1990 expert systems; software Artificial intelligence began on the far-out fringe engineering environment of computer science and has been progressing more and more into the mainstream of software engineering through the use of AI components, such as inference engines, that can be embedded in programs written in more traditional languages. Eclipse takes this progression one step further by allowing the use of Lisp in combination with rules and traditional C programming. Weare currently making use of Eclipse in our work on a team software development environment. Internal Accession Date Only (c)Copyright Hewlett-Packard Company 1990 1 Introduction Eclipse, the subject of this paper, is one component of an object-based software develop­ ment environment (SDE), that is being developed at Hewlett-Packard Labs in Palo Alto. This SDE is designed to support teams of developers building large software systems. In addition to providing a shared object base of project-wide information, the environment provides rule-based support for managing the software development process. These rules create an active database that responds to user actions, checks constraints and automates repetitive tasks. A typical constraint rule might require that a software module pass certain tests before being included in a release. For reasons of efficiency, the rules are attached not to the persistent, project-wide object base but rather to a local cache of objects in the virtual memory of each developer's work­ station.
    [Show full text]
  • Memory Efficient Stream Reasoning on Resource-Limited Devices
    UNIVERSITY OF DUBLIN, TRINITY COLLEGE Memory Efficient Stream Reasoning on Resource-Limited Devices by Dylan Armstrong Supervisor: Declan O'Sullivan Assistant Supervisor: Wei Tai A dissertation submitted in partial fulfillment for the degree of Master in Computer Science Submitted to the University of Dublin, Trinity College, May 2014 Declaration of Authorship I, Dylan Armstrong, declare that the following dissertation, except where otherwise stated, is entirely my own work; that it has not previously been submitted as an exercise for a degree, either in Trinity College Dublin, or in any other University; and that the library may lend or copy it or any part thereof on request. Signed: Date: i Summary The first goal of this research was to determine which of two reasoning algorithms could be adapted for use in a streaming reasoning system with memory constraints. The two algorithms investigated were TREAT and LEAPS. Once achieved, the primary goal was then to determine whether an implementation of the chosen algorithm provided a more efficient option in terms of memory consumption in a resource constrained device in comparison to an existing implementation of the RETE algorithm. This paper first illustrates the motivation for this research by providing context for why and when memory constrained devices are used in reasoning. Standard approaches usually attempt to perform reasoning on a centralised machine, sending data from sensor devices through the web or by other wired or wireless technologies. However, in some conditions this may not be feasible, or may be restrictive. In these cases reasoning can be performed on the sensor device nodes themselves.
    [Show full text]
  • 292 Chapter 9 Inference in First-Order Logic
    292 Chapter 9 Inference in First-Order Logic Rete algorithm The Rete algorithm3 was the first to address this problem. The algorithm preprocesses the set of rules in the knowledge base to construct a dataflow network in which each node is a literal from a rule premise. Variable bindings flow through the network and are filtered out when they fail to match a literal. If two literals in a rule share a variable—for example, Sells(x,y,z)∧Hostile(z) in the crime example—then the bindings from each literal are filtered through an equality node. A variable binding reaching a node for an n-ary literal such as Sells(x,y,z) might have to wait for bindings for the other variables to be established before the process can continue. At any given point, the state of a Rete network captures all the partial matches of the rules, avoiding a great deal of recomputation. Rete networks, and various improvements thereon, have been akeycomponentofso- Production system called production systems,whichwereamongtheearliestforward-chainingsystemsin widespread use.4 The XCON system (originally called R1; McDermott, 1982) was built with a production-system architecture. XCON contained several thousand rules for designing configurations of computer components for customers of the Digital Equipment Corporation. It was one of the first clear commercial successes in the emerging field of expert systems. Many other similar systems have been built with the same underlying technology, which has been implemented in the general-purpose language OPS-5. Cognitive architectures Production systems are also popular in cognitive architectures—that is, models of hu- man reasoning—such as ACT (Anderson, 1983) and SOAR (Laird et al.,1987).Insuchsys- tems, the “working memory” of the system models human short-term memory, and the pro- ductions are part of long-term memory.
    [Show full text]
  • Research of the Rule Engine Based On
    3rd International Conference on Materials Engineering, Manufacturing Technology and Control (ICMEMTC 2016) Research of the Rule Engine based on XML Zhao Ni1, a, Lifang Bai2, b 1 Zhengzhou Institute of Information Science and Technology, Henan, China, 450000, China 2 Zhengzhou Institute of Information Science and Technology, Henan, China, 450000, China aemail: [email protected], bemail:[email protected] Keywords: Pattern matching algorithm; XML; Rule engine; Rete network Abstract. As Studying of the current rule engine implementation mechanism, An XML format rules is put forward for the poor ability in the predicate expansion and the rule reuse of the traditional rule description, which can describe of the detail interface information called by entity class in rules, and the improved rete network corresponding to the rule description is proposed, forming a new pattern matching algorithm based on XML format rules. Introduction Rules engine originated from production system. It can be regarded as a component of the application, which realizes the separation of the business rules or logic in application from the program code, and writes business rules with predefined semantic module [1]. The application field of rule engine is very extensive, including banking, telecommunications billing, game software, business management, etc. At present, an open source business rules engine Drools (JBoss) is the most representative of the rule engine [2]. Pattern matching algorithm is the core algorithm in the rule engine. This paper focuses on Rete pattern matching algorithm, which is the basis for the shell of many production systems, including the CLIPS (C language integrated production system), JESS, Drools and Soar [3].
    [Show full text]
  • Design and Implementation of a Service-Oriented Business Rules Broker
    Design and Implementation of a Service-Oriented Business Rules Broker Florian Rosenberg and Schahram Dustdar Vienna University of Technology Distributed Systems Group, Information Systems Institute 1040 Vienna, Argentinierstrasse 8/184-1, Austria {rosenberg, dustdar}@infosys.tuwien.ac.at Abstract of the business logic, because business rules tend to change quite frequently. Different research efforts exist which try Business rules define or constrain some business [24]. to extract business rules from existing applications [28]. Many different business rules engines exist to capture and The usage of a business rules engine using different manage the rules that comprise ones business. The lack of knowledge bases can greatly improve the maintainability standards in the business rule area leads to different en- in such cases. Such an engine handles the execution and gine implementations and various rule representation for- management of business rules. The administration of rules mats. Furthermore, accessing rule-based knowledge in a should ideally be performed by domain experts or business uniform way from different heterogeneous applications in analysts (depending on the rule language used for author- an enterprise computing environment poses many integra- ing). The necessity of business rules, especially in the elec- tion challenges. In this paper, we propose the design and tronic commerce domain, is stated in [1] by the following implementation of a service-oriented approach on how to two factors: Firstly, an increased competition requires fre- integrate different heterogeneous business rule engines by quent changes in cooperate policies and strategies to remain using our Business Rules Broker architecture. Furthermore, competitive. This means that changes have to be made fast we present a method to automatically expose the rule-based to react to market changes (e.g., changing insurance poli- business knowledge as Web services by generating the Web cies).
    [Show full text]