scholarly journals Mining in Ontology with Multi Agent System in Semantic Web : A Novel Approach

2014 ◽  
Vol 6 (5) ◽  
pp. 45-54 ◽  
Author(s):  
Vishal Jain ◽  
Prasad S. V. A. V
Keyword(s):  
Semantic Web ◽  
Multi Agent System ◽  
Agent System ◽  
Novel Approach ◽  
Multi Agent

INTENT RECOGNITION IN MULTI-AGENT SYSTEMS: COW HERDING

2014 ◽  
Vol 28 (05) ◽  
pp. 1459006
Author(s):  
NAJLA AHMAD ◽  
ARVIN AGAH
Keyword(s):  
Conceptual Framework ◽  
Recognition System ◽  
Plan Recognition ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent System ◽  
Intent Recognition ◽  
Domain Specific ◽  
Novel Approach ◽  
Multi Agent

In a multi-agent system, an agent may utilize its idle time to assist other agents in the system. Intent recognition is proposed to accomplish this with minimal communication. An agent performing recognition observes the tasks other agents are performing and, unlike the much studied field of plan recognition, the overall intent of an agent is recognized instead of a specific plan. The observing agent may use capabilities that it has not observed. A conceptual framework is proposed for intent recognition systems. An implementation of the conceptual framework is tested and evaluated. We hypothesize that using intent recognition in a multi-agent system increases utility (where utility is domain specific) and decreases the amount of communication. We test our hypotheses using the domain of Cow Herding, where agents attempt to herd cow agents into team corrals. A set of metrics, including task time and number of communications, is used to compare the performance of plan recognition and intent recognition. In our results, we find that intent recognition agents communicate fewer times than plan recognition agents. In addition, unlike plan recognition, when agents use the novel approach of intent recognition, they select unobserved actions to perform. Intent recognition agents were also able to outperform plan recognition agents by consistently scoring more points in the Cow Herding domain. This research shows that under certain conditions, an intent recognition system is more efficient than a plan recognition system. The advantage of intent recognition over plan recognition becomes more apparent in complex domains.

Knowledge Technologies-Based Multi-Agent System for Semantic Web Services Environments

2008 ◽  
pp. 1222-1233
Author(s):  
Francisco García-Sánchez ◽  
Rodrigo Martínez-Béjar ◽  
Rafael Valencia-García ◽  
Jesualdo T. Fernández-Breis
Keyword(s):  
Web Services ◽  
Semantic Web ◽  
Semantic Web Services ◽  
Multi Agent System ◽  
Agent System ◽  
Multi Agent

MASTER: AN INTELLIGENT ONTOLOGY-BASED MULTI-AGENT SYSTEM FOR SIGHTSEER

2009 ◽  
Vol 19 (02) ◽  
pp. 137-157 ◽  
Author(s):  
TOBY H. W. LAM ◽  
JAMES N. K. LIU ◽  
RAYMOND S. T. LEE
Keyword(s):  
Semantic Web ◽  
Structural Information ◽  
Domain Ontology ◽  
Multi Agent System ◽  
Markup Languages ◽  
Agent System ◽  
Web Technologies ◽  
Ontology Language ◽  
Multi Agent ◽  
Tourist Information

Recently, semantic web has received substantial attention from the research community. Semantic web aims to provide a new framework that can enable knowledge sharing and reusing. Semantic web is a collection of web technologies that include a number of markup languages such as RDF, OWL and RDFS. These markup languages are used for modeling a domain ontology. By using ontology to model resources, humans and computers (software agents) can have a consensus on the resource structure. The use of these technologies allows the creation of a more effective web search system. In this paper, we modeled travel domain ontology by using Web Ontology Language (OWL). Instead of inviting an expert to model the ontology, we created the travel ontology by collecting and analyzing the structural information from a number of travel related websites. Besides, we implemented an intelligent ontology-based Multi-Agent System for sighTseER (MASTER), which is constructed by using semantic web technologies. MASTER integrates Global Positioning System (GPS), ontology and agent technologies to support location awareness for providing the precise navigation and classify the tourist information for the users. The system was tested on 30 novice users. 83% of the users felt that the system can help tourists find tourist information in Hong Kong.

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