Akademik Veri Yönetim Sistemi
21st European Conference on Multi-Agent Systems, EUMAS 2024, Dublin, İrlanda, 26 - 28 Ağustos 2024, cilt.15685 LNAI, ss.181-197, (Tam Metin Bildiri)
This research investigates the influence of language warmth on the user adoption of agent recommendations and satisfaction by/of the users in the multi-armed bandit scenarios. Prior work has identified that agent recommendations can, under certain situations, improve user selection of options to maximize utility given a limited number of pulls on a multi-armed bandit problem. We posit that appropriate explanations associated with agent recommendations can further increase user adoption of potentially higher utility arms and thereby increase cumulative utility obtained by the user from a finite number of arm pulls. Furthermore, we investigate the effect of “warmth” of explanations on recommendation adoption by the user and concomitant cumulative utility realized. We design an experimentation platform for the agent-assisted user selection from multi-armed bandits. Experiments are run with workers recruited from an online crowdsourcing platform. The results reveal that explanations do result in improved cumulative utility obtained. Most importantly, we suggest that agent designers should configure the warmth level of agent explanations depending on the interaction type, i.e., warm language can be more effective in some problems while cold language works in others.