An Overview of the Distributed Integrated Cognition Affect and Reflection DIARC Architecture

Scheutz, Matthias; Williams, Thomas; Krause, Evan; Oosterveld, Bradley; Sarathy, Vasanth; Frasca, Tyler M.

doi:10.1007/978-3-319-97550-4_11

Cited by 37 publications

(29 citation statements)

References 56 publications

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“…As research continues to explore how to develop synthetic teammates that can properly situate language within a given context (e.g., Bonial et al, 2020), recognize and eventually learn new vocabulary (e.g., Scheutz et al, 2019), and more generally become more flexible and human-like in their use of language, it will be important to establish whether or not it will be beneficial for teammates to entrain on particular linguistic structures, and if so, which structures. Our results suggest that information packaging may be a good candidate to consider for synthetic teammate entrainment, though further research is needed.…”

Section: Discussionmentioning

confidence: 99%

How Packaging of Information in Conversation Is Impacted by Communication Medium and Restrictions

2021

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In team-based tasks, successful communication and mutual understanding are essential to facilitate team coordination and performance. It is well-established that an important component of human conversation (whether in speech, text, or any medium) is the maintenance of common ground. Maintaining common ground has a number of associated processes in which conversational participants engage. Many of these processes are lacking in current synthetic teammates, and it is unknown to what extent this lack of capabilities affects their ability to contribute during team-based tasks. We focused our research on how teams package information within a conversation, by which we mean specifically (1) whether information is explicitly mentioned or implied, and (2) how multiple pieces of information are ordered both within single communications and across multiple communications. We re-analyzed data collected from a simulated remotely-piloted aerial system (RPAS) task in which team members had to specify speed, altitude, and radius restrictions. The data came from three experiments: the “speech” experiment, the “text” experiment, and the “evaluation” experiment (which had a condition that included a synthetic teammate). We asked first whether teams settled on a specific routine for communicating the speed, altitude, and radius restrictions, and whether this process was different if the teams communicated in speech compared to text. We then asked how receiving special communication instructions in the evaluation experiment impacted the way the human teammates package information. We found that teams communicating in either speech or text tended to use a particular order for mentioning the speed, altitude, and radius. Different teams also chose different orders from one another. The teams in the evaluation experiment, however, showed unnaturally little variability in their information ordering and were also more likely to explicitly mention all restrictions even when they did not apply. Teams in the speech and text experiments were more likely to leave unnecessary restrictions unmentioned, and were also more likely to convey the restrictions across multiple communications. The option to converge on different packaging routines may have contributed to improved performance in the text experiment compared some of the conditions in the evaluation experiment.

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Section: Discussionmentioning

confidence: 99%

How Packaging of Information in Conversation Is Impacted by Communication Medium and Restrictions

2021

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“…In future work, we also aim to integrate this proposed model into a cognitive robotic architecture, so that it can be leveraged to effectively generate contextually-appropriate robot language. Specifically, we plan to integrate our model into the ADE implementation (Kramer & Scheutz, 2006) of the Distributed, Integrated, Affect, Reflection, Cognition (DI-ARC) architecture (Scheutz et al, 2013(Scheutz et al, , 2019. After completing this integration, we plan to empirically examine the effectiveness of our model in enabling more natural and contextually appropriate human-robot interactions and the learning of relationships between different contexts and the contextual factors explored in this paper, and to assess how this could enable robots to seamlessly adapt their language as they change between different real-world contexts.…”

Section: Discussionmentioning

confidence: 99%

“We Need to Start Thinking Ahead”: The Impact of Social Context on Linguistic Norm Adherence

Lockshin¹,

Williams²

2020

Preprint

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Human dialogue is governed by communicative norms that speakers are expected to follow in order to be viewed as cooperative dialogue partners. Accordingly, for language-capable autonomous agents to be effective human teammates they must be able to understand and generate language that complies with those norms. Moreover, these linguistic norms are highly context sensitive, requiring autonomous agents to be able to model the contextual factors that dictate when and how those norms are applied. In this work, we consider three key linguistic norms (directness, brevity, and politeness), and examine the extent to which adherence to these norms varies under changes to three key contextual factors (potential for harm, interlocutor authority, and time pressure). Our results, based on a human-subject study involving 5,642 human utterances, provide strong evidence that speakers do indeed vary their adherence to these norms under changes to these contextual factors.

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“…In future work, we plan to encode our learned norms within the pragmatic norm base (cf. Williams et al 2015) used by the Distributed Integrated Affect, Recognition and Cognition (DIARC) architecture (Schermerhorn et al 2006;Scheutz et al 2013;2019) and assess the fluidity and successfulness of robots interacting with users under norm systems selected with different thresholds.…”

Section: Discussionmentioning

confidence: 99%

Dempster-Shafer Theoretic Learning of Indirect Speech Act Comprehension Norms

Wen

Siddiqui

Williams

2020

AAAI

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For robots to successfully operate as members of human-robot teams, it is crucial for robots to correctly understand the intentions of their human teammates. This task is particularly difficult due to human sociocultural norms: for reasons of social courtesy (e.g., politeness), people rarely express their intentions directly, instead typically employing polite utterance forms such as Indirect Speech Acts (ISAs). It is thus critical for robots to be capable of inferring the intentions behind their teammates' utterances based on both their interaction context (including, e.g., social roles) and their knowledge of the sociocultural norms that are applicable within that context. This work builds off of previous research on understanding and generation of ISAs using Dempster-Shafer Theoretic Uncertain Logic, by showing how other recent work in Dempster-Shafer Theoretic rule learning can be used to learn appropriate uncertainty intervals for robots' representations of sociocultural politeness norms.

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An Overview of the Distributed Integrated Cognition Affect and Reflection DIARC Architecture

Cited by 37 publications

References 56 publications

How Packaging of Information in Conversation Is Impacted by Communication Medium and Restrictions

How Packaging of Information in Conversation Is Impacted by Communication Medium and Restrictions

“We Need to Start Thinking Ahead”: The Impact of Social Context on Linguistic Norm Adherence

Dempster-Shafer Theoretic Learning of Indirect Speech Act Comprehension Norms

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