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Pure exploration in multi-armed bandits has emerged as an important framework for modeling decision making and search under uncertainty. In modern applications however, one is often faced with a tremendously large number of options and even obtaining one observation per option may be too costly rendering traditional pure exploration algorithms ineffective. Fortunately, one often has access to similarity relationships amongst the options that can be leveraged. In this paper, we consider the pure exploration problem in stochastic multi-armed bandits where the similarities between the arms is captured by a graph and the rewards may be represented as a smooth signal on this graph. In particular, we consider the problem of finding the arm with the maximum reward (i.e., the maximizing problem) or one that has sufficiently high reward (i.e., the satisficing problem) under this model. We propose novel algorithms GRUB (GRaph based UcB) and zeta-GRUB for these problems and provide theoretical characterization of their performance which specifically elicits the benefit of the graph side information. We also prove a lower bound on the data requirement that shows a large class of problems where these algorithms are near-optimal. We complement our theory with experimental results that show the benefit of capitalizing on such side information. 

We propose that emotional priming may be an effective approach to scaffold the creation of rich stories. There are relatively few emotion-based approaches to support users to create, instead of consume, rich stories. Emotional priming is the technique of using emotion- related stimuli to affect human’s executive control and affective processing. It has been researched mostly in terms of human’s behaviors and decision making. We conducted a within-subjects study with 12 participants to investigate the effects of emotional priming induced through an interactive application on storytelling quality. Two conditions of priming were compared to a baseline condition of no priming. In the first condition, the application primes participants by having asking them to perceive and recognize varying emotional stimuli (perception-based priming). In the second condition, the application primes participants by having them produce varying emotional facial expressions (production- based priming). Analyses show that emotional priming resulted in richer storytelling than no emotional priming, and that the production-based emotional priming condition resulted in statistically richer stories being told by participants. We discuss the possibility of integrating interactive emotional priming into storytelling applications.