Investigating Human Response, Behaviour, and Preference in Joint-Task Interaction
This work aims to improve the design of interactive AI agents, particularly those using planning techniques, by incorporating user affective states, which is an incremental step for the human-computer interaction community.
This paper investigates human response, behavior, and preference in joint-task interaction to inform the development of Explainable Planning (XAIP) agents. The authors designed two agents, one reactive and one partially predictive, and conducted an empirical analysis with simulated users to examine their behavior.
Human interaction relies on a wide range of signals, including non-verbal cues. In order to develop effective Explainable Planning (XAIP) agents it is important that we understand the range and utility of these communication channels. Our starting point is existing results from joint task interaction and their study in cognitive science. Our intention is that these lessons can inform the design of interaction agents -- including those using planning techniques -- whose behaviour is conditioned on the user's response, including affective measures of the user (i.e., explicitly incorporating the user's affective state within the planning model). We have identified several concepts at the intersection of plan-based agent behaviour and joint task interaction and have used these to design two agents: one reactive and the other partially predictive. We have designed an experiment in order to examine human behaviour and response as they interact with these agents. In this paper we present the designed study and the key questions that are being investigated. We also present the results from an empirical analysis where we examined the behaviour of the two agents for simulated users.