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Ten years ago, Berners-Lee et al. (2001) unveiled a nascent vision of the Semantic Web: a highly interconnected network of data that could be easily accessed and understood by any software agent. Those intelligent software agents would head out on the Internet and automatically perform tasks like booking flights and hotels, updating medical records and giving a single, customized answer to a particular question without the human user having to search for information and pore through results. Software agents have the promise to help users in dealing with information overload and perform tasks automatically in a flexible way. After a decade of developments in both Semantic Web and Software Agent technology, this vision has not yet come true. The challenge for researchers is the need to develop mechanisms to enable connections, which are between the web content and its end-users (Hendler & Berners-Lee, 2010). This implies the need to connect the Semantic Web providing the content and agents who are acting on behalf of the end-users.
From a technology perspective, the Semantic Web can be regarded as a group of technologies to allow machines to understand the meaning of information on the Web. Among those technologies the key ones related to knowledge representation and exchanges are the Resource Description Framework (RDF), the Web Ontology Language (OWL) and the Rule Interchange Format (RIF). The term agent, or software agent, has been widely used and has found its way into a number of technologies. In artificial intelligence, an agent is an entity which can observe and act upon an environment and directs its activity towards achieving goals (Russell & Norvig, 2003). In agent research, the belief-desire-intention (BDI) architecture style is regarded as the predominant approach to the implementation of intelligent or rational agents (Wooldridge, 2000). There are many agent languages and tools supporting this architecture, e.g., PRS, dMARS, and AgentSpeak(L) (Mascardi, Demergasso, & Ancona, 2005).
Nowadays the ideas for integrating Semantic Web and Agent Programming techniques are blooming (Klapiscak & Bordini, 2009). Both domains can benefit from each other by connecting with the other. One of the advantages is that groups of intercommunicating agents are available to simulate complex actions on behalf of the end-users of the Web in real-life applications. (Kravari, Kastori, Bassiliades, & Governatori, 2010). In this way, the development on the Web and its performance can be tested by the agents. Another advantage is to allow the agent system to use existing resources (e.g., an ontology) in the Semantic Web. This integration refers to the interchange and use of rules to regulate the behaviors of agents or share knowledge among collaborating agents. The key to achieve this was indicated as “agents can only flourish when standards are well established” (Shadbolt, Hall, & Berners-Lee, 2006, p. 96). The Web standards for expressing a shared meaning have progressed steadily over the past years. The prospering of OWL in the academic community establishes the foundation of knowledge sharing by giving an open representation of the knowledge. This lets the Semantic Web technologies open up new applications to rule-based technology including agents. To enable the interoperation among rule-based applications on the Web, standards are needed to represent rules. Responding to these circumstances RIF has recently been promoted as a W3C standard for exchanging rules among different systems and developing intelligent rule-based applications for the Semantic Web.