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The intelligent virtual agent community often works from the assumption that embodiment confers clear benefits to human-machine interaction. However, embodiment has potential drawbacks in highlighting the salience of social stereotypes such as those around race and gender. Indeed, theories of computer-mediated communication highlight that visual anonymity can sometimes enhance team outcomes. Negotiation is one domain where social perceptions can impact outcomes. For example, research suggests women perform worse in negotiations and find them more aversive, particularly when interacting with men opponents. Research with human participants makes it challenging to unpack whether these negative consequences stem from women’s perceptions of their partner or greater toughness on the part of these men opponents. We use a socially intelligent AI negotiation agent to begin to unpack these processes. We manipulate the perceived toughness of the AI by whether or not it expresses anger — a common tactic to extract concessions. Independently, we manipulate the activation of stereotypes by randomly setting whether the interaction has embodiment (as a male opponent) or has only text (where we obscure gender cues). We find a clear interaction between gender and embodiment. Specifically, women perform worse, and men perform better against an apparently male opponent compared to a disembodied agent – as measured by the subjective value they assign to their outcome. This highlights the potential disadvantages of embodiment in negotiation, though future research must rule out alternative mechanisms that might explain these results.
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Design Intention Inference for Virtual Co-Design Agents
We address the challenge of inferring the design intentions of a human by an intelligent virtual agent that collaborates with the human. First, we propose a dynamic Bayesian network model that relates design intentions, objectives, and solutions during a human's exploration of a problem space. We then train the model on design behaviors generated by a search agent and use the model parameters to infer the design intentions in a test set of real human behaviors. We find that our model is able to infer the exact intentions across three objectives associated with a sequence of design outcomes 31.3% of the time. Inference accuracy is 50.9% for the top two predictions and 67.2% for the top three predictions. For any singular intention over an objective, the model's mean F1-score is 0.719. This provides a reasonable foundation for an intelligent virtual agent to infer design intentions purely from design outcomes toward establishing joint intentions with a human designer. These results also shed light on the potential benefits and pitfalls in using simulated data to train a model for human design intentions.
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- Award ID(s):
- 1907542
- PAR ID:
- 10292482
- Date Published:
- Journal Name:
- IVA '20: Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents
- Page Range / eLocation ID:
- 1 to 8
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3383652.3423861
