An Adaptive Computational Model for Personalized Persuasion

Authors: Yilin Kang, Ah-Hwee Tan, Chunyan Miao

IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Our experimental results show that the MAP-based agent is able to change the others attitudes and behaviors intentionally, interpret individual differences between users, and adapt to user s behavior for effective persuasion. A pilot user study has shown that compared with the single best persuasion strategy method, the MAP-based virtual agent achieved a significantly higher rate of persuasion, higher level of social presence, and lower degree of frustration.
Researcher Affiliation Academia Yilin Kang1, Ah-Hwee Tan1,2, Chunyan Miao1,2 1Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY), Nanyang Technological University, Singapore 2School of Computer Engineering, Nanyang Technological University, Singapore {ylkang,asahtan,ascymiao}@ntu.edu.sg
Pseudocode Yes Algorithm 1: Dynamics of the MAP model
Open Source Code No The paper does not provide any explicit statements or links about open-sourcing the code for the described methodology.
Open Datasets No The paper conducts a user study with 26 recruited subjects but does not specify a publicly available dataset or provide access information for the data collected in their study.
Dataset Splits No The paper describes a user study with 26 subjects but does not refer to traditional training, validation, or test dataset splits.
Hardware Specification No The paper mentions the virtual nurse was 'implemented using the Unity 3-D engine' and uses 'Microsoft Speech Recognition tool', but does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments.
Software Dependencies No The paper mentions 'Unity 3-D engine' and 'Microsoft Speech Recognition tool' but does not provide specific version numbers for these or any other software dependencies.
Experiment Setup No The paper describes the system architecture and the user study procedure, including how internal states are obtained and how the model adapts, but it does not provide specific hyperparameter values for the MAP model (e.g., specific values for α, δ, or ε, beyond 'a small positive decimal between zero and one' for ε).