Using Rule-Based Concepts as Foundation for Higher-Level Agent Architectures

Declarative programming using rules has advantages in certain application domains and has been successfully applied in many real world software projects. Besides building rule-based applications, rule concepts also provide a proven basis for the development of higher-level architectures, which enrich the existing production rule metaphor with further abstractions. One especially interesting application domain for this technology is the behavior specification of autonomous software agents, because rule bases help fulfilling key characteristics of agents such as reactivity and proactivity. This chapter details which motivations promote the usage of rule bases for agent behavior control and what kinds of approaches exist. Concretely, these approaches are in the context of four existing agent architectures (pure rule-based, AOP, Soar, BDI) and their implementations (Rule Responder, Agent-0 and successors, Soar, and Jadex). In particular, this chapter emphasizes in which respect these agent architectures make use of rules and with what mechanisms they extend the base functionality. Finally, the approaches are generalized by summarizing their core assumptions and extension mechanisms and possible further application domains besides agent architectures are presented.

SDRule Markup Language

The emergence of ontology based applications, e.g. the Semantic Web, marks the importance of ontologies. Application rules, such as decision making rules, are often committed to an existing domain ontology when a new application needs to be designed and developed. During this process, the semantics of application rules is required to be precisely grounded. In this chapter, we tackle the problems of modeling and interchanging ontological commitments in order to support ontology based decision making. We model and visualize ontological commitments by means of an extension to Object Role Modeling Language (ORM), which was called ORM Plus (ORM+) and is now named Semantic Decision Rule Language (SDRule-L). SDRule-L is a commitment language for modeling dynamic and non-monotonic decision rules. SDRule-L models are further stored in an XML-based markup language called Semantic Rule Markup Language (SDRule ML), which is a hybrid language of Rule Markup Language (Rule-ML) and Object Role Modeling Markup Language (ORM-ML). We also illustrate its supporting tool called SDRule-Lex, which is based on Tiny Lexon Browser (T-Lex). We demonstrate in the field of on-line customer management.

Mining Association Rules

During the last years the amount of data stored in databases has grown very fast. Data mining, also known as knowledge discovery in databases, represents the discovery process of potentially useful hidden knowledge or relations among data from large databases. An important task in the data mining process is the discovery of the association rules. An association rule describes an interesting relationship between different attributes. There are different kinds of association rules: Boolean (crisp) association rules, quantitative association rules, fuzzy association rules, etc. In this chapter, we present the basic concepts of Boolean and the fuzzy association rules, and describe the methods used to discover the association rules by presenting the most important algorithms.

Using the Semantic Web Rule Language in the Development of Ontology-Driven Applications

The Semantic Web Rule Language (SWRL) is an expressive OWL-based rule language. SWRL allows users to write Horn-like rules that can be expressed in terms of OWL concepts to provide more powerful deductive reasoning capabilities than OWL alone. Semantically, SWRL is built on the same description logic foundation as OWL and provides similar strong formal guarantees when performing inference. Due to its description logics foundation, rule-based applications developed using SWRL have a number of relatively novel characteristics. For example, SWRL shares OWL’s open world assumption so certain types of rules that assume a closed world may be difficult or impossible to write in SWRL. In addition, all inference in SWRL is monotonic so deductions cannot be updated or retracted. These formal characteristic have a strong influence on the development and use of SWRL rules in ontology-driven applications. In this chapter, we describe the primary features of SWRL and outline how, despite some limitations, SWRL can be used to dramatically increase amount of knowledge that be represented in OWL ontologies.

Languages and Tools for Rule Modeling

This chapter presents selected practical issues of rule modeling. This field combines both classic artificial intelligence methods and software engineering. The chapter gives a concise presentation of selected relevant methods, and approaches, put in an engineering perspective. The modeling language used in the communication between business analysts and experts for analyzing the system requirements should not be too technical. It should allow for visual rule expressions, which can be understood by experts without an extensive technical training. The main goals of this chapter are: to summarize the formal foundations of rules found in the field of AI, including decision tables and trees; discuss main challenges in practical rule design, and modeling; introduce selected recent research in the field of rule design, focusing on visual modeling; and observe some important future trends in rule design and integration. In the chapter it is argued that efficient visual rule modeling methods are crucial for developing complex rule systems.

Business Rules Management for Business Processes

The power of rule-based solutions has been demonstrated over a wide range of domains and a number of industrial-scale solutions and business rules have now proven their usability in complex real world scenarios. But the use of business rules in conjunction with business process management is still a young research field. Business process management (BPM) is a new paradigm for companies to carry out their value-creating activities. Bringing agility and flexibility to business process management is one of the most pressing challenges we are facing today. In this chapter, we make the case for rule-enabled BPM by motivating the need for introducing business rules in BPM and studying the possible advantages of combining business rule management (BRM) and BPM techniques. A discussion of possible uses of business rules (BRs) in business processes (BPs) is made. Furthermore, we also propose a lifecycle for BPM-oriented business rule management, and illustrate this using a business scenario. Hence, the aim of this chapter is to provide readers with insights into issues conceptual BRM applied to BPM in a business context, not from a formal, but from a methodological point of view.

Automated Interpretation of Key Performance Indicators by Using Rules

Business reporting is an essential task for every enterprise. In order to make appropriate decisions, decision makers need quality reports. Some recent articles suggest that reports generated by BI (Business Intelligence) systems contain mostly data (key performance indicator values) and little or no information. Data has no meaning and must be interpreted in order to become information. Information is, naturally, much more useful because it directly contributes to recipients’ knowledge and can be acted upon. The consequence is that it is left to the decision maker to manually analyze large quantities of data presented in individual reports in order to derive information. A potential solution for automated business data interpretation is presented in this chapter. It proposes using rules to capture and formalize business knowledge and then utilizing these rules to infer information from data automatically.

Sharing Ontologies and Rules Using Model Transformations

Web Ontology Language (OWL), Semantic Web Rule Language (SWRL) and Model-Driven Engineering (MDE) are technologies being developed in parallel, but by different communities. They have common points and issues and can be brought closer together. Many authors have so far stressed this problem and have proposed several solutions. The result of these efforts is the recent OMG’s initiative for defining an ontology development platform. However, the problem of transformation between Semantic Web ontology and rule languages and MDE-based languages has been solved using rather partial and ad hoc solutions, most often by XSLT. In this paper, we relations between the Semantic Web languages and MDE-compliant languages as separate technical spaces. In order to achieve a synergy between these technical spaces, we present ontology and rule languages in terms of MDE standards, recognize relations between the OWL and SWRL langauges and MDE-based ontology languages, and propose mapping techniques. In order to illustrate the approach, we use an MDE-defined architecture that includes the ontology and rule metamodels and ontology UML Profile. We also show how MDE techniques, such as model transformations, can be used to enable sharing rules and ontologies by using REWERSE Rule Markup Language (R2ML), a proposal for a general rule language. The main benefit of this approach is that it keeps the focus on the language concepts (i.e., languages’ abstract syntax - metamodels) rather than on technical issues caused by different concrete syntax. Yet, we also provide transformations that bridge between both languages’ concrete (XML) and abstract (MOF) syntax.

Modeling and Programming by Commitment Rules in Agent Factory

Agent-Oriented Programming (AOP) is a relatively new programming paradigm, proposed by Yoav Shoham, which views software systems as consisting of a set of agents that interact with one another to solve problems beyond their individual capabilities. Since the inception of the paradigm, a number of AOP languages have been proposed. This chapter focuses on one such language, the Agent Factory Agent Programming Language (AFAPL), a practical rule-based language that has been applied to a wide range of problem domains including robotics, virtual and mixed reality environments, and mobile computing. AFAPL is placed in context through a general introduction to the state-of-the-art in AOP. The chapter finishes with a discussion of some future trends for AOP and some concluding remarks.

The Agent Object Relationship Simulation as a Business Process

The use of agent-based simulation models is growing and attracted a lot of attention recently both for researchers and business management. Agent-Object Relationship (AOR) is an agent-based simulation paradigm that uses reaction rules to model agents’ behavior. The goal of this chapter, besides exemplifying the AOR concepts by means of a use case, is to investigate the use of business process modeling notation (BPMN) to model the AOR simulation process. Moreover it discusses aspects of a distributed architecture for an AOR simulation system. The chapter concludes with the fact that BPMN is well suited to model the AOR simulation process.