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You searched for subject:(Specialized Agent). Showing records 1 – 2 of 2 total matches.

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University of Saskatchewan

1. Emelogu, Adindu Ahurueze. Establishing agent staffing levels in queueing systems with cross-trained and specialized agents.

Degree: 2010, University of Saskatchewan

The determination of the right number of servers in a multi-server queueing system is one of the most important problems in applied queueing theory. The problem becomes more complex in a system that consists of both cross-trained and specialized servers. Such queueing systems are readily found in the call centres (also called contact centres) of financial institutions, telemarketing companies and other organizations that provide services to customers in multiple languages. They are also found in computer network systems where some servers are dedicated and others are flexible enough to handle various clients' requests. Over-staffing of these systems causes increased labour costs for the underutilized pool of agents on duty, while under-staffing results in reduced revenue from lost customers and an increase in queue times. The efficient design and analysis of these systems helps management in making better staffing decisions. This thesis aims to develop models for establishing agent staffing levels in organizations with cross-trained and specialized staff with a view to minimizing cost and maintaining a desirable customer satisfaction. The work investigates the effect of various traffic loads on the number of agents required and the cost. It also considers how using specialized agents, flexible agents and a combination of both categories of agents affects the system. It uses a contact centre that has agents with monolingual, bilingual and trilingual (English, French and Spanish) capabilities to do the study. Advisors/Committee Members: Grassmann, Winfried, Neufeld, Eric, Cheston, Grant, Willoughby, Keith.

Subjects/Keywords: queueing systems; cross-training; service rate factor; agent cost factor; specialized agents; cross-trained agents

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Emelogu, A. A. (2010). Establishing agent staffing levels in queueing systems with cross-trained and specialized agents. (Thesis). University of Saskatchewan. Retrieved from http://hdl.handle.net/10388/etd-06222010-224410

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Emelogu, Adindu Ahurueze. “Establishing agent staffing levels in queueing systems with cross-trained and specialized agents.” 2010. Thesis, University of Saskatchewan. Accessed July 02, 2020. http://hdl.handle.net/10388/etd-06222010-224410.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Emelogu, Adindu Ahurueze. “Establishing agent staffing levels in queueing systems with cross-trained and specialized agents.” 2010. Web. 02 Jul 2020.

Vancouver:

Emelogu AA. Establishing agent staffing levels in queueing systems with cross-trained and specialized agents. [Internet] [Thesis]. University of Saskatchewan; 2010. [cited 2020 Jul 02]. Available from: http://hdl.handle.net/10388/etd-06222010-224410.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Emelogu AA. Establishing agent staffing levels in queueing systems with cross-trained and specialized agents. [Thesis]. University of Saskatchewan; 2010. Available from: http://hdl.handle.net/10388/etd-06222010-224410

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

2. Mooman, Abdelniser. Multi-Agent User-Centric Specialization and Collaboration for Information Retrieval.

Degree: 2012, University of Waterloo

The amount of information on the World Wide Web (WWW) is rapidly growing in pace and topic diversity. This has made it increasingly difficult, and often frustrating, for information seekers to retrieve the content they are looking for as information retrieval systems (e.g., search engines) are unable to decipher the relevance of the retrieved information as it pertains to the information they are searching for. This issue can be decomposed into two aspects: 1) variability of information relevance as it pertains to an information seeker. In other words, different information seekers may enter the same search text, or keywords, but expect completely different results. It is therefore, imperative that information retrieval systems possess an ability to incorporate a model of the information seeker in order to estimate the relevance and context of use of information before presenting results. Of course, in this context, by a model we mean the capture of trends in the information seeker's search behaviour. This is what many researchers refer to as the personalized search. 2) Information diversity. Information available on the World Wide Web today spans multitudes of inherently overlapping topics, and it is difficult for any information retrieval system to decide effectively on the relevance of the information retrieved in response to an information seeker's query. For example, the information seeker who wishes to use WWW to learn about a cure for a certain illness would receive a more relevant answer if the search engine was optimized into such domains of topics. This is what is being referred to in the WWW nomenclature as a 'specialized search'. This thesis maintains that the information seeker's search is not intended to be completely random and therefore tends to portray itself as consistent patterns of behaviour. Nonetheless, this behaviour, despite being consistent, can be quite complex to capture. To accomplish this goal the thesis proposes a Multi-Agent Personalized Information Retrieval with Specialization Ontology (MAPIRSO). MAPIRSO offers a complete learning framework that is able to model the end user's search behaviour and interests and to organize information into categorized domains so as to ensure maximum relevance of its responses as they pertain to the end user queries. Specialization and personalization are accomplished using a group of collaborative agents. Each agent employs a Reinforcement Learning (RL) strategy to capture end user's behaviour and interests. Reinforcement learning allows the agents to evolve their knowledge of the end user behaviour and interests as they function to serve him or her. Furthermore, REL allows each agent to adapt to changes in an end user's behaviour and interests. Specialization is the process by which new information domains are created based on existing information topics, allowing new kinds of content to be built exclusively for information seekers. One of the key characteristics of specialization domains is the seeker centric - which allows intelligent agents…

Subjects/Keywords: Information retrieval; Multi-agent system; Specialized Agent; Reinforcement learning; search engines; specialized domains; personalization; IR system; user's feedback; NLP; WordNet; relevance information and feedback; Topic extractions; semantic web; ontology; clustering; data classifications; data mining

…42 3 Specialized Multi-Agent System for IR: A Novel Framework and Subject Matter of… …Specialized Agent Using RL . . . . . . . . . . 64 Summary… …experimentation . . . . . . . . . . . . . . 113 6.3.1 6.3.2 6.4 Specialized Agent performance using RL… …Specialized Multi-Agent Learning System for IR Framework. . . . . . . . . 47 3.2 Specialized… …Agent Framework. . . . . . . . . . . . . . . . . . . . . . . . . . 59 3.3 Specialized Agent… 

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Mooman, A. (2012). Multi-Agent User-Centric Specialization and Collaboration for Information Retrieval. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/6991

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Mooman, Abdelniser. “Multi-Agent User-Centric Specialization and Collaboration for Information Retrieval.” 2012. Thesis, University of Waterloo. Accessed July 02, 2020. http://hdl.handle.net/10012/6991.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Mooman, Abdelniser. “Multi-Agent User-Centric Specialization and Collaboration for Information Retrieval.” 2012. Web. 02 Jul 2020.

Vancouver:

Mooman A. Multi-Agent User-Centric Specialization and Collaboration for Information Retrieval. [Internet] [Thesis]. University of Waterloo; 2012. [cited 2020 Jul 02]. Available from: http://hdl.handle.net/10012/6991.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mooman A. Multi-Agent User-Centric Specialization and Collaboration for Information Retrieval. [Thesis]. University of Waterloo; 2012. Available from: http://hdl.handle.net/10012/6991

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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