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Computational Foundations for Mixed-Motive Human-Machine Dialogue
Computational Foundations for Mixed-Motive Human-Machine Dialogue
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151059
- ISBN
- 9798381975888
- DDC
- 004
- 저자명
- Chawla, Kushal.
- 서명/저자
- Computational Foundations for Mixed-Motive Human-Machine Dialogue
- 발행사항
- [Sl] : University of Southern California, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 241 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-10, Section: B.
- 주기사항
- Advisor: Lucas, Gale.
- 학위논문주기
- Thesis (Ph.D.)--University of Southern California, 2024.
- 초록/해제
- 요약Social interactions often involve a mixture of motives. People seek to maximize their own interests without undermining the needs of others. Success in these interactions, referred to as mixed-motive interactions, demands a balance between self-serving and other-serving motives. For instance, in a typical negotiation, a player must balance maximizing their own goals with the goals of their partner so as to come to an agreement. If the player asks for too much, this can push the partner to walk away without an agreement, hence hurting the outcomes for all the parties involved. Such interactions are highly prevalent in everyday life, from deciding who performs household chores to customer support and high-stakes business deals. Consequently, automated systems capable of comprehending and participating in these strategic environments with human players find broad downstream applications. This includes advancing conversational assistants and the development of tools that make everyday social interactions more effective and efficient (e.g., by acting as a content moderator or a coach). Additionally, these systems hold a huge potential to transform pedagogical practices by dramatically reducing costs and scaling up social skills training.Most efforts for automation focus on agent-agent interactions, where thousands of offers are exchanged between the players. These interactions are fundamentally different from human-agent conversations, which are much shorter and naturally involve human subjectivity, which in fact, has been a subject matter of research for decades across several disciplines, including Psychology, Affective Computing, and Economics. Hence, in order to simplify the design, most efforts in human-agent negotiations involve restrictive menu-driven communication interfaces that are based on button clicks and structured APIs for interaction between the human and the machine. This concreteness reduces the design complexity, but it comes at a cost -- such interfaces hinder the study and incorporation of several aspects of real-world negotiations, such as complex strategies and emotion expression. Going beyond such constrained designs, it is desirable to incorporate more realistic modes of communication, such as natural language, for their utility in better serving the downstream applications -- our work aims to fill this gap.In this dissertation, we present our foundational work for enabling mixed-motive human-machine dialogue, with a focus on bilateral chat-based negotiation interactions. We discuss our progress in three key areas: 1) The design of a novel task and dataset of grounded human-human negotiations that fueled our investigations into the role of emotion expression and linguistic strategies, 2) Techniques for dialogue systems capable of engaging in mixed-motive interactions by learning to strike a balance between self and partner interests, and 3) Defining a research space encompassing such strategic dialogue interactions to promote a research community for dedicated efforts and discussion in this area.
- 일반주제명
- Computer science
- 일반주제명
- Psychology
- 일반주제명
- Personality psychology
- 키워드
- Chatbots
- 키워드
- Dialogue systems
- 키워드
- Negotiations
- 기타저자
- University of Southern California Computer Science
- 기본자료저록
- Dissertations Abstracts International. 85-10B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211151059
■006m o d
■007cr#unu||||||||
■020 ▼a9798381975888
■035 ▼a(MiAaPQ)AAI31142746
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aChawla, Kushal.
■24510▼aComputational Foundations for Mixed-Motive Human-Machine Dialogue
■260 ▼a[Sl]▼bUniversity of Southern California▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a241 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-10, Section: B.
■500 ▼aAdvisor: Lucas, Gale.
■5021 ▼aThesis (Ph.D.)--University of Southern California, 2024.
■520 ▼aSocial interactions often involve a mixture of motives. People seek to maximize their own interests without undermining the needs of others. Success in these interactions, referred to as mixed-motive interactions, demands a balance between self-serving and other-serving motives. For instance, in a typical negotiation, a player must balance maximizing their own goals with the goals of their partner so as to come to an agreement. If the player asks for too much, this can push the partner to walk away without an agreement, hence hurting the outcomes for all the parties involved. Such interactions are highly prevalent in everyday life, from deciding who performs household chores to customer support and high-stakes business deals. Consequently, automated systems capable of comprehending and participating in these strategic environments with human players find broad downstream applications. This includes advancing conversational assistants and the development of tools that make everyday social interactions more effective and efficient (e.g., by acting as a content moderator or a coach). Additionally, these systems hold a huge potential to transform pedagogical practices by dramatically reducing costs and scaling up social skills training.Most efforts for automation focus on agent-agent interactions, where thousands of offers are exchanged between the players. These interactions are fundamentally different from human-agent conversations, which are much shorter and naturally involve human subjectivity, which in fact, has been a subject matter of research for decades across several disciplines, including Psychology, Affective Computing, and Economics. Hence, in order to simplify the design, most efforts in human-agent negotiations involve restrictive menu-driven communication interfaces that are based on button clicks and structured APIs for interaction between the human and the machine. This concreteness reduces the design complexity, but it comes at a cost -- such interfaces hinder the study and incorporation of several aspects of real-world negotiations, such as complex strategies and emotion expression. Going beyond such constrained designs, it is desirable to incorporate more realistic modes of communication, such as natural language, for their utility in better serving the downstream applications -- our work aims to fill this gap.In this dissertation, we present our foundational work for enabling mixed-motive human-machine dialogue, with a focus on bilateral chat-based negotiation interactions. We discuss our progress in three key areas: 1) The design of a novel task and dataset of grounded human-human negotiations that fueled our investigations into the role of emotion expression and linguistic strategies, 2) Techniques for dialogue systems capable of engaging in mixed-motive interactions by learning to strike a balance between self and partner interests, and 3) Defining a research space encompassing such strategic dialogue interactions to promote a research community for dedicated efforts and discussion in this area.
■590 ▼aSchool code: 0208.
■650 4▼aComputer science
■650 4▼aPsychology
■650 4▼aPersonality psychology
■653 ▼aChatbots
■653 ▼aDialogue systems
■653 ▼aEmotion expression
■653 ▼aNatural language processing
■653 ▼aNegotiations
■690 ▼a0984
■690 ▼a0800
■690 ▼a0621
■690 ▼a0625
■71020▼aUniversity of Southern California▼bComputer Science.
■7730 ▼tDissertations Abstracts International▼g85-10B.
■790 ▼a0208
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160678▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
