Artificial conversational agents are software agents that can interact with humans in the way humans do. Siri Cortana, and Alexa are examples of intelligent agents that can help us with almost all the basic tasks. These agents are smart enough to do the basic tasks, but not as much when it comes to complex tasks, such as analyzing traffic data, reviewing scheduling conflicts, rescheduling meetings while resolving conflicts, and offering suggestions based upon data analyses (e.g. traffic patterns, weather, etc.) The actual potential of dialogue-based task agent potential remains untapped. The reason is the fact agents lack the ability to fully understand human language. Natural Language processing (NLP) makes it possible for those agents to understand humans.
The current research project explores the development of a conversational agent, Schedulio, that can be connected to the user's calendar and assist with the scheduling, deletion, and modification of events using natural language conversation. Additionally, current research explores the implementation of a recommendation agent that provides suggestions based upon time and slot date recommendation, location-specific weather data, and traffic patterns.
","abstract_html":"<p>Artificial conversational agents are software agents that can interact with humans in the way humans do. Siri Cortana, and Alexa are examples of intelligent agents that can help us with almost all the basic tasks. These agents are smart enough to do the basic tasks, but not as much when it comes to complex tasks, such as analyzing traffic data, reviewing scheduling conflicts, rescheduling meetings while resolving conflicts, and offering suggestions based upon data analyses (e.g. traffic patterns, weather, etc.) The actual potential of dialogue-based task agent potential remains untapped. The reason is the fact agents lack the ability to fully understand human language. Natural Language processing (NLP) makes it possible for those agents to understand humans.</p> <p>The current research project explores the development of a conversational agent, Schedulio, that can be connected to the user's calendar and assist with the scheduling, deletion, and modification of events using natural language conversation. Additionally, current research explores the implementation of a recommendation agent that provides suggestions based upon time and slot date recommendation, location-specific weather data, and traffic patterns.</p>","abstract_has_math":false,"creators":["Ahuja, Priyanka"],"institution":null,"degree_name":"Computer Science - Applied Computing Track","degree_level":"Thesis","degree_discipline":"TSYS School of Computer Science","degree_department":null,"school":null,"contributors":["Dr. Rania Hodhod","Dr. Shamim Khan","Dr. Wayne Summers"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-05-01T07:00:00Z","date_published":"2018-05-01T07:00:00Z","updated_at":"2026-07-24T01:45:01Z","subjects":["Natural Language Processing","Natural Language Understanding","Scheduling Agent","AI","Artificial Intelligence and Robotics","Computer Sciences"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://csuepress.columbusstate.edu/theses_dissertations/355","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Rania Hodhod","Dr. Shamim Khan","Dr. Wayne Summers"]},{"key":"dc:creator","label":"Author","values":["Ahuja, Priyanka"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2020-01-30T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["TSYS School of Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Computer Science - Applied Computing Track"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Natural Language Processing","Natural Language Understanding","Scheduling Agent","AI","Artificial Intelligence and Robotics","Computer Sciences"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://csuepress.columbusstate.edu/theses_dissertations/355"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Artificial conversational agents are software agents that can interact with humans in the way humans do. Siri Cortana, and Alexa are examples of intelligent agents that can help us with almost all the basic tasks. These agents are smart enough to do the basic tasks, but not as much when it comes to complex tasks, such as analyzing traffic data, reviewing scheduling conflicts, rescheduling meetings while resolving conflicts, and offering suggestions based upon data analyses (e.g. traffic patterns, weather, etc.) The actual potential of dialogue-based task agent potential remains untapped. The reason is the fact agents lack the ability to fully understand human language. Natural Language processing (NLP) makes it possible for those agents to understand humans.
The current research project explores the development of a conversational agent, Schedulio, that can be connected to the user's calendar and assist with the scheduling, deletion, and modification of events using natural language conversation. Additionally, current research explores the implementation of a recommendation agent that provides suggestions based upon time and slot date recommendation, location-specific weather data, and traffic patterns.
"]},{"key":"dc:title","label":"Title","values":["Al Planning Assistant for Scheduling Daily Activities"]}]}],"canonical_facts":{"dc:contributor":["Dr. Rania Hodhod","Dr. Shamim Khan","Dr. Wayne Summers"],"dc:creator":["Ahuja, Priyanka"],"dc:date.available":["2020-01-30T08:00:00Z"],"dc:description.abstract":["Artificial conversational agents are software agents that can interact with humans in the way humans do. Siri Cortana, and Alexa are examples of intelligent agents that can help us with almost all the basic tasks. These agents are smart enough to do the basic tasks, but not as much when it comes to complex tasks, such as analyzing traffic data, reviewing scheduling conflicts, rescheduling meetings while resolving conflicts, and offering suggestions based upon data analyses (e.g. traffic patterns, weather, etc.) The actual potential of dialogue-based task agent potential remains untapped. The reason is the fact agents lack the ability to fully understand human language. Natural Language processing (NLP) makes it possible for those agents to understand humans.
The current research project explores the development of a conversational agent, Schedulio, that can be connected to the user's calendar and assist with the scheduling, deletion, and modification of events using natural language conversation. Additionally, current research explores the implementation of a recommendation agent that provides suggestions based upon time and slot date recommendation, location-specific weather data, and traffic patterns.
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