# Speech Act Theory as an Evaluation Tool for Human-Agent Communication
## 7 Discussion  
This paper aims to study the plausibility of using SAT concepts to analyse the verbal communication between a human and an agent in a collaborative virtual environment. To go beyond merely exploring the hypothesized relationship between the agent's verbal communication and collaboration performance, the agent's verbal communication was anatomized. In this dissection, each fundamental component that composed the agent's verbal communication was evaluated individually and studied in relation to other components. SAT is a well-known theory for understanding human speech, particularly while achieving a task. Although SAT has been used to explore human speech while accomplishing a mission in collaboration with other humans, it has not previously been utilized to \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Mean** & **SD** & **t** & **df** & **Sig. (2-tailed)** \\ \hline \multicolumn{5}{c}{**Humans’ Perceptions of the Agent’s Illocutionary Acts**} \\ Intention appropriateness of agent’s request & 2.89 & 0.86 & 3.99 & 65 & 0.000 * \\ Intention appropriateness of agent’s reply & 2.75 & 0.78 & 2.83 & 65 & 0.006 * \\ Intention appropriateness of agent’s feedback & 2.82 & 0.80 & 3.43 & 65 & 0.001 * \\ \hline \multicolumn{5}{c}{**Humans’ Perception of the Collaboration Performance**} \\ Humans’ perceptions of the agent’s flow of actions & 2.81 & 0.83 & 27.54 & 65 & 0.000 * \\ Humans’ perceptions of the agent’s reaction to the humans’ flow of actions & 2.62 & 0.87 & 24.39 & 65 & 0.000 * \\ \hline \hline \end{tabular}  
* Significance at the 0.01 level.  
\end{table} 
Table 2: Results of t-tests to show significance differences in the participants’ perception of the appropriateness of the agent’s intention as expressed in the agent’s requests, replies, and feedback. understand an agent's verbal communication and the impact of that communication on the outcome of a collaboration with a human teammate. We claim that this is one of the innovations of this paper. The first research question inquired about how to evaluate the agent's locutionary acts. The results showed that the participants generally had a positive perspective toward the structure of the agent's utterances. We sought to investigate why many found the messages \"awkward\". By reviewing the script of the sentences and the participants' comments, we found that some of agent's utterances seemed to be formal and not like natural everyday conversation. Researchers have found that humans' expectations of the abilities of collaborative virtual agents were lower for robot-like agents rather than human-like agents. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) found that the appearance of a virtual agent can have a strong effect on human evaluations of the agent's capabilities. If the virtual agent looks like a human, humans will expect the virtual agent to have other human capabilities such as natural human speech. Improving the structure of the sentences uttered by the agent needs to be continuously revised to make sure they satisfy human expectations. The second research question inquired about how agent's illocutionary acts can be measured. The result of measuring illocutionary acts stressed the importance of balance in the interlocutors' usage of [PERSON]'s five classes. Effective speech should not have a specific or singular tone, i.e., representative, declarative, etc. The results showed that the five classes were used with different ratios in the dialogue between the agent and the human. The dominant ratio is for the representative class because the nature of the dialogue needs both the human and the agent to reply to each request for feedback. A subjective evaluation measured the extent to which the participants considered the intention of the agent to be clear, as conveyed by the agent's requests, replies, and feedback. Findings in social neuroscience research have demonstrated that understanding the intentions of co-actors is fundamental for successful social collaboration ([PERSON] and [PERSON], 2018).