A Spontaneous Topic Change of Dialogue for Conversational Agent Based on Human Cognition and Memory

Humans have a remarkable capacity for coordination. Our ability to interact and act jointly in groups is crucial to our success as a species. Joint Action (JA) research has often concerned itself with simplistic behaviors in highly constrained laboratory tasks. But there has been a growing interest in understanding complex coordination in more open-ended contexts. In this regard, collective music improvisation has emerged as a fascinating model domain for studying basic JA mechanisms in an unconstrained and highly sophisticated setting. A number of empirical studies have begun to elucidate coordination mechanisms underlying joint musical improvisation, but these empirical findings have yet to be cached out in a working computational model. The present work fills this gap by presenting TonalEmergence, an idealized agent-based model of improvised musical coordination. TonalEmergence models the coordination of notes played by improvisers to generate harmony (i.e., tonality), by simulating agents that stochastically generate notes biased towards maximizing harmonic consonance given their partner's previous notes. The model replicates an interesting empirical result from a previous study of professional jazz pianists: that feedback loops of mutual adaptation between interacting agents support the production of consonant harmony. The model is further explored to show how complex tonal dynamics, such as the production and dissolution of stable tonal centers, are supported by agents that are characterized by 1) a tendency to strive toward consonance, 2) stochasticity, and 3) a limited memory for previously played notes. TonalEmergence thus provides a grounded computational model to simulate and probe the coordination mechanisms underpinning one of the more remarkable feats of human cognition: collective music improvisation.

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User Experience and Potential Health Effects of a Conversational Agent-Based Electronic Health Intervention: An Observational Cohort Study in the Netherlands

SSRN Electronic Journal ◽

10.2139/ssrn.3787465 ◽

2021 ◽

Author(s):

Marian Z.M. Hurmuz ◽

Stephanie M. Jansen-Kosterink ◽

Tessa Beinema ◽

Katrien Fischer ◽

Harm op den Akker ◽

...

Keyword(s):

Cohort Study ◽

The Netherlands ◽

Health Effects ◽

User Experience ◽

Health Intervention ◽

Observational Cohort Study ◽

Conversational Agent ◽

Potential Health ◽

Agent Based ◽

Observational Cohort

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AutoTutor

Applied Natural Language Processing ◽

10.4018/978-1-60960-741-8.ch010 ◽

2012 ◽

pp. 169-187 ◽

Cited By ~ 19

Author(s):

Arthur C. Graesser ◽

Sidney D’Mello ◽

Xiangen Hu ◽

Zhiqiang Cai ◽

Andrew Olney ◽

...

Keyword(s):

Learning Environments ◽

Speech Act ◽

Intelligent Tutoring ◽

Intelligent Tutoring System ◽

Conversational Agent ◽

Learning Gains ◽

Tutoring System ◽

Agent Based ◽

Electronic Documents ◽

Dialogue Management

AutoTutor is an intelligent tutoring system that helps students learn science, technology, and other technical subject matters by holding conversations with the student in natural language. AutoTutor’s dialogues are organized around difficult questions and problems that require reasoning and explanations in the answers. The major components of AutoTutor include an animated conversational agent, dialogue management, speech act classification, a curriculum script, semantic evaluation of student contributions, and electronic documents (e.g., textbook and glossary). This chapter describes the computational components of AutoTutor, the similarity of these components to human tutors, and some challenges in handling smooth dialogue. We describe some ways that AutoTutor has been evaluated with respect to learning gains, conversation quality, and learner impressions. AutoTutor is sufficiently modular that the content and dialogue mechanisms can be modified with authoring tools. AutoTutor has spawned a number of other agent-based learning environments, such as AutoTutor-lite, Operation Aries!, and Guru.

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