# Enhancing Human-Agent Interaction via Artificial Agents That Speculate About the Future
## 2 Background
### Experimental Work  
Aside from theoretical work, there has also been prior experimental research on speech systems for artificial agents geared towards future actions, which, although different from the concept of anticipatory speech here, still provides some useful background on how scientists are exploring different types of speech with regard to artificial agents. For instance, in the field of HRI, many researchers have evaluated the effects of speech on trustworthiness and user perception, though not necessarily the effects on actual task performance [13; 38]. It remains an open question whether impacting trust will actually carry through to impact completing some task jointly by a human and artificial agent in the real world, though that is currently a hot area of research [39]. There have also been a few attempts to measure the effects of verbal feedback from a robot/agent prior to human-performed actions during interactions, though the results of such research have been mixed. Some researchers report significant effects, while others report no clear effect; e.g., the human-performed action remains the same after feedback [40; 41; 42; 43]. That may be an issue with how the feedback is being communicated by the robot that limits its impact or have something to do with user perceptions of the robot's intelligence [44]. Suffice it to say, while there is a general consensus in the field that trust and alignment are important factors in HRI, it remains unclear as to what degree. Other researchers have focused on the topic of recovering from moments of miscommunication during speech dialogue in HRI/HAI, which often leads to interaction failures. Typically, that entails some sort of speech about specific future actions on the part of the robot/agent so as to recover from miscommunication by guiding the human user towards a particular behavior [45; 46]. While that sort of scenario is still reactive in the sense that it is a response to a previous interaction failure, it does entail some anticipation about the future, though not necessarily utilizing prediction. Rather, for example, speech in that scenario may involve \"explanations\" of why the current interaction failure may have occurred, so that the human can attempt to fix the issue, e.g., the human performed a task step out of sequence, such as when assembling something [47; 48]. That is obviously quite a bit different from speculating about the future. Regardless, it is still an open debate as to how social robots should best respond to failures during HRI, and thus an active area of research in the field [46; 49]. Another related topic is that of \"fluency\" during HAI and HRI, referring to the artificial agent purposely engaging in more proactive behaviors (in contrast to merely reacting). This idea has been explored in various ways by researchers, generally in terms of behaviors intended to trigger human responses [12; 50; 51; 52]. Some of the early work in that area was done by [PERSON] et al., who defined different kinds of robot anticipation and ways to measure any resulting task efficiency improvements [12]. Interestingly, however, later researchers found that proactive behaviors in artificial agents are not always preferable and that, in fact, some users react negatively to such behavior in certain situations [43]. While some of that research has used human speech cues to trigger proactive robot behavior, a substantial portion has utilized non-verbal cues of humans, such as user intent based on eye gaze [50]. Moreover, much of the focus has also been on enabling the robot to interpret human cues, rather than the robot's own behavior [53]. That said, there has also been some related work where the robot attempts to anticipate the human user's next utterance during dialogue, i.e., guessing what the human will say next [54]. This idea is related to the concepts of turn-taking prediction and dialogue state trackers mentioned in Section 1[21; 22; 23].