# Enhancing Human-Agent Interaction via Artificial Agents That Speculate About the Future
## 1 Introduction  
Overview  
It could be argued that most of what humans talk about is not actions we necessarily do ourselves, but rather _speculation_ about events that happen in the world around us. The weather, the economy, politics, sports, etc. This includes events that have occurred in the past as well as events that may (or may not) occur in the future, i.e., things we imagine as rational possibilities [1]. Natural human conversation is full of this kind of speculation, which may eventually lead to actions but is not yet action in and of itself. In other words, it is what we might refer to as \"anticipatory speech\", a term we coin here to define it. In contrast to the more typical planning approach for artificial agent speech, which is geared towards specific actions the agent will perform, the focus of anticipatory speech is on potential events with unclear probability. Humans are thought to engage in such speech because itallows them to cognitively structure their own thoughts by mentally rehearsing various actions toward multiple potential events [2; 3], while also communicating a sense of \"shared experience\" to other people around them in order to create the social cohesion necessary for surviving in an uncertain world (i.e., \"we're all in this together\" mentality) [4; 5; 6; 7]. The purpose of anticipatory speech in that sense is very different from the purpose of speech planning, in that for the former, we are not really planning actions but rather attempting to engage socially. As such, we would argue that perhaps our social robots and artificial agents should do the same sort of thing, given that they were inherently designed to be social (i.e., unlike service robots or physical machines). Intuitively, that makes sense, but the challenge, of course, is how to accomplish it in practice [8; 9]. After all, it is easy enough to talk about what is happening, or has already happened, or even what we will definitely do in the future, but in contrast, talking about what _might_ happen and what we _might_ do is much more abstract. It is important to clarify the difference between the definition of anticipatory speech given above and other related concepts: joint planning, speech planning, dialogue state trackers, turn-taking prediction, etc. Joint planning, for instance, focuses on the actions that an agent/robot and human will perform towards some specific task (i.e., no speculation involved) in an attempt to create better coordination between them. It can be thought of as an extension of classical planning methods in computer science and AI [10; 11; 12; 13]. Speech planning is a term originally from neuroscience that refers to the human production of speech content (i.e., taking an idea and turning it into words) and is often applied to populations where such speech production is disrupted (e.g., people who stutter, post-stroke patients) [14; 15; 16]. Dialogue state trackers are used in many modern speech systems, but typically focus on what has already been said in order to try to keep the agent/robot \"on-topic\" during the conversation [17; 18; 19]. Turn-taking prediction focuses on determining appropriate times for agent/robot to speak so as not to interrupt a human conversational partner [20; 21]. As one can see from such descriptions, all those other terms have a different meaning and purpose from anticipatory speech, which is focused on _speculating_ about potential events. Presently, many speech systems for human-agent interaction (HAI) and human-robot interaction (HRI) focus on current actions and/or knowledge of prior events (see Section 2), and even large-language model (LLM) systems like ChatGPT can be framed similarly given their corpus-based training methods [22; 23]. To our knowledge, there is limited research on embedding speculation into speech systems for artificial agents.