The design of a semantic web compatible content language for agent communication

Abstract

Current standards on agent communication languages explain communication by changes in the mental states of agents. It is rather a striking issue that none of the popular agent middleware adheres to this semantics in a formal way. Therefore, this paper tries to close the gap between agent communication theory and practice by implementing a logic that is capable of representing agents' beliefs and intentions. An interesting property of this language, named ALC BI , is that it is a hybrid of modal and description logics. Due to its description logic foundation, it can also be used as an ontology definition language. Thus, it is well suited for agents working on the semantic web. In this paper, a sound and complete decision algorithm as well as other reasoning services for the logic are presented. Finally, the application of the language in a multi-agent development framework is discussed.

Distributed Data and Information Fusion in Visual Sensor Networks

“…The main drawback of SL is that it is undecidable in its general form; i.e., it is not guaranteed that all the inferences are computable in a finite time. Therefore, there is a growing interest in using formal ontologies as content languages (Hendler 2001, Schiemann and Schreiber 2006, Erdur and Seylan 2008, since they have appropriate computational properties and several supporting tools.…”

Integrating computer vision with web‐based knowledge for medical diagnostic assistance

“…In addition, a method has been proposed for creating web annotations in a controlled natural language to express domain‐specific ontological knowledge about that website in an unambiguous subset of English (Schwitter & Tilbrook, 2008). Previous work also included an ontology definition language for agents working on the semantic web (Erdur & Seylan, 2008).…”

Communication in distributed tracking systems: an ontology‐based approach to improve cooperation

“…An advantage of ontologies over classical multi‐agent content languages, such as FIPA Semantic Language, is that the latter are undecidable in their general form – i.e., it is not guaranteed that all the inferences are computable in a finite time, whereas the former are decidable and are supported by current tools (APIs, reasoners, etc.) Hence, ontologies have been proposed to be the knowledge representation of agent systems (Hendler, 2001; Schiemann & Schreiber, 2006; Erdur & Seylan, 2008).…”