Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/293513
Title: | Multiagent system for cooperative decision making in academic environment |
Researcher: | Modgil, Puneet |
Guide(s): | Devi, M. Syamala |
Keywords: | Artificial Intelligence Decision Making Fuzzy Logic Multiagent Ontology |
University: | Panjab University |
Completed Date: | 2019 |
Abstract: | Multiagent System for Cooperative Decision Making in Academic Environment (MASCDAE) is a collection of four subsystems where each subsystem contains one or more agents that coordinate with each other to facilitate cooperative decision making in Academic Environment. The four subsystems of newlineMASCDAE are Admission Scrutiny subsystem, Meeting Scheduling subsystem, Meeting Facilitation subsystem, and Research Asset Management subsystem. newlineIn the Admission Scrutiny subsystem, the main knowledge-intensive tasks of admissions are scrutinising admission forms filled by admission seekers. Scrutiny is performed by capturing the knowledge from the web and relevant sources and scrutinising the information. This work is assigned to three agents, namely Form agent, Record agent and Scrutiny agent. newlineMeeting Scheduling subsystem involves finding a suitable slot for an academic meeting. Coordinator schedules meeting considering the preferences of participants along with their significance. Meeting newlineScheduling subsystem aids coordinator in solving this problem by proposing a meeting schedule which takes into consideration both, i.e. significance and preferences of various participants. This subsystem newlineuses fuzzy logic inferencing embedded in Fuzzy Inference agent to achieve the objective. newlineMeeting Facilitation subsystem involves aiding facilitator/coordinator in processing opinions and views of various participants in the meeting. This subsystem attempts to find the polarities of opinions on newlinevarious agenda points by participants. In order to achieve this objective, this subsystem uses the classification model implemented using machine learning algorithm of logistic regression. This model is newlinetrained with sixteen lakh labelled tuples. newlineAll the subsystems are tested separately first and then after integrating all of them into a single system on the web platform. Results were found as desired. |
Pagination: | ix, 165p. |
URI: | http://hdl.handle.net/10603/293513 |
Appears in Departments: | Department of Computer Science and Application |
Files in This Item:
File | Description | Size | Format | |
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01_title_page.pdf | Attached File | 34.8 kB | Adobe PDF | View/Open |
02_certificate.pdf | 58.28 kB | Adobe PDF | View/Open | |
03_acknowledgement .pdf | 48.46 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 159.58 kB | Adobe PDF | View/Open | |
05_list of figures.pdf | 68.01 kB | Adobe PDF | View/Open | |
06_list of tables.pdf | 67.79 kB | Adobe PDF | View/Open | |
07_abbreviations.pdf | 51.91 kB | Adobe PDF | View/Open | |
08_contents.pdf | 58.2 kB | Adobe PDF | View/Open | |
09_chapter1.pdf | 110.67 kB | Adobe PDF | View/Open | |
10_chapter2.pdf | 147.79 kB | Adobe PDF | View/Open | |
11_chapter3.pdf | 479.41 kB | Adobe PDF | View/Open | |
12_chapter4.pdf | 324.56 kB | Adobe PDF | View/Open | |
13_chapter5.pdf | 1.91 MB | Adobe PDF | View/Open | |
14_chapter6.pdf | 724.68 kB | Adobe PDF | View/Open | |
15_chapter7.pdf | 631.46 kB | Adobe PDF | View/Open | |
16_chapter8.pdf | 101.88 kB | Adobe PDF | View/Open | |
17_annexures.pdf | 338.76 kB | Adobe PDF | View/Open | |
19_bibliography.pdf | 97.59 kB | Adobe PDF | View/Open | |
21_student_approval_form.pdf | 165.64 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 101.88 kB | Adobe PDF | View/Open |
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