An E-negotiation Agent for an E-tourism Platform

The article presents an electronic negotiation agent, integrated within a multiagent system for an electronic tourism platform. The e-negotiation process is based on a winner-winner approach, using a bargaining protocol. However, with the proliferation of services, the task of searching for relevant services becomes more and more difficult. Thus, the authors also propose a search agent to find tourism services corresponding to the client request and profile. The discovery process uses a quantitative similarity measure.

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Developing a Generic Concept of an Electronic Negotiation Process with ebXML

Objects, Components, Architectures, Services, and Applications for a Networked World - Lecture Notes in Computer Science ◽

10.1007/3-540-36557-5_16 ◽

2003 ◽

pp. 199-215

Author(s):

Philipp Thun ◽

Michael Rebstock

Keyword(s):

Negotiation Process ◽

Generic Concept ◽

Electronic Negotiation

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The effects of problem structure on negotiation process and outcome

PsycEXTRA Dataset ◽

10.1037/e722822011-032 ◽

1990 ◽

Author(s):

Jeryl L. Mumpower ◽

Thomas Darling

Keyword(s):

Negotiation Process ◽

Problem Structure ◽

Process And Outcome

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Handling WSD using Hierarchical Clustering Algorithm with sentences

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset1841120 ◽

2018 ◽

pp. 83-88

Author(s):

Mohana Priya K ◽

Pooja Ragavi S ◽

Krishna Priya G

Keyword(s):

Hierarchical Clustering ◽

Similarity Measure ◽

Clustering Algorithm ◽

Clustering Algorithms ◽

Cosine Similarity Measure ◽

Hierarchical Clustering Algorithm ◽

Multiple Levels ◽

Pos Tagger ◽

Sentence Clustering ◽

The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

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