Cognitive-Inspired Conversational-Strategy Reasoner for Socially-Aware Agents
Authors: Oscar J. Romero, Ran Zhao, Justine Cassell
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experimentation and Results Our experiments focused on evaluating three aspects of our work: 1) determining whether social reasoning can increase rapport and raise engagement; 2) evaluating the degree of effectiveness and accuracy of the Social Reasoner after the data-driven tuning process; and 3) evaluating the performance of the Social Reasoner during interaction with users. |
| Researcher Affiliation | Academia | Oscar J. Romero*, Ran Zhao+, Justine Cassell+ *Machine Learning Department, Carnegie Mellon University +Human-Computer Interaction Institute, Carnegie Mellon University {oscarr, rzhao1}@andrew.cmu.edu, justine@cs.cmu.edu |
| Pseudocode | No | The paper describes a 'procedure for decision-making' in numbered steps within paragraph text, but it does not present it as structured pseudocode or a formally labeled algorithm block. |
| Open Source Code | No | The paper refers to a project website (http://articulab.hcii.cs.cmu.edu/projects/sara/) but does not contain an explicit statement about releasing the source code for the methodology described in this paper, nor a direct link to a code repository. |
| Open Datasets | No | The paper mentions collecting data from a 'Wizard-of-Oz study' ('WOZ study dataset') but does not provide any specific link, DOI, repository name, or formal citation for public access to this dataset. |
| Dataset Splits | No | The paper mentions using a 'WOZ study dataset of 228 sessions' but does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or a citation to predefined splits). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | Following the guidelines proposed by [Romero, 2011; Romero and de Antonio, 2012] and through empirical analysis, we determined that the best configuration of the spreading activation parameters is as follows: 1. To keep the balance between deliberation and reactivity, φ > γ, so φ = 68 and γ = 42. 2. To keep the balance between bias towards ongoing plan vs. adaptivity, π > γ π < φ, so φ = 50. 3. To preserve sensitivity to goal conflict, δ > γ, so δ = 75. |