Embodied agents, such as robots, will need to interact in situated environments where successful communication often depends on reasoning over social norms: shared expectations that constrain what actions are appropriate in context. A key capability in such settings is norm-based reference resolution (NBRR), where interpreting referential expressions requires inferring implicit normative expectations grounded in physical and social context. Yet it remains unclear whether Large Language Models (LLMs) can support this kind of reasoning. In this work, we introduce SNIC (Situated Norms in Context), a human-validated diagnostic testbed designed to probe how well state-of-the-art LLMs can extract and utilize normative principles relevant to NBRR. SNIC emphasizes physically grounded norms that arise in everyday tasks such as cleaning, tidying, and serving. Across a range of controlled evaluations, we find that even the strongest LLMs struggle to consistently identify and apply social norms—particularly when norms are implicit, underspecified, or in conflict. These findings reveal a blind spot in current LLMs and highlight a key challenge for deploying language-based systems in socially situated, embodied settings.

We present IntelliProof, an interactive system for analyzing argumentative essays through LLMs. IntelliProof structures an essay as an argumentation graph, where claims are represented as nodes, supporting evidence is attached as node properties, and edges encode supporting or attacking relations. Unlike existing automated essay scoring systems, IntelliProof emphasizes the user experience: each relation is initially classified and scored by an LLM, then visualized for enhanced understanding. The system provides justifications for classifications and produces quantitative measures for essay coherence. It enables rapid exploration of argumentative quality while retaining human oversight. In addition, IntelliProof provides a set of tools for a better understanding of an argumentative essay and its corresponding graph in natural language, bridging the gap between the structural semantics of argumentative essays and the user's understanding of a given text.

LLMs are being increasingly integrated into embodied robotic systems. A useful capability that the LLMs bring to robots is translating noisy spoken human natural language instructions into executable robot actions. However, these integrations are somewhat ad hoc and understudied as they tend to not consider the gamut of syntactic, semantic, as well as pragmatic aspects of embodied human communication. What is missing is a characterization of the different paradigms for integrating LLMs into robotic architectures as well as a set of evaluation metrics that capture whether an LLM-equipped robot can correctly understand these different aspects of human instruction. In this article, we present a suite of evaluation metrics together with data augmentation techniques for evaluating these architectures, using concepts from the cognitive science and human communication literature. To illustrate an application of these metrics and augmentation techniques, we conduct experiments to compare two integration methods: LLMs as pre-processing components that map human instructions into more constrained versions to be processed by the architecture’s natural language understanding (NLU) subsystem, or LLMs as a wholesale replacement for the NLU’s parser. We provide experimental evaluations and a robotic implementation to show the inherent tradeoffs between the methods. Our results suggest that while they offer increased explainability, traditional parsing tools coupled with LLMs do not perform as well as an LLM that replaces a parser entirely. The proposed evaluation metrics together with the characterization of different LLM integration approaches offer the promise of systematically evaluating LLMs as natural language interfaces to robotic systems as well as tackle the important tradeoff between explainability/verifiability/interpretability and robustness to noisy input and broad language understanding in an open-world embodied setting.

Understanding the implicit values and beliefs of diverse groups and cultures using qualitative texts – such as long-form narratives – and domain-expert interviews is a fundamental goal of social anthropology. This paper builds upon a 2022 study that introduced the NLP task of Recognizing Value Resonance (RVR) for gauging perspective – positive, negative, or neutral – on implicit values and beliefs in textual pairs. This study included a novel hand-annotated dataset, the World Values Corpus (WVC), designed to simulate the task of RVR, and a transformer-based model, Resonance-Tuned RoBERTa, designed to model the task. We extend existing work by refining the task definition and releasing the World Values Corpus (WVC) dataset. We further conduct several validation experiments designed to robustly evaluate the need for task specific modeling, even in the world of LLMs. Finally, we present two additional Resonance-Tuned models trained over extended RVR datasets, designed to improve RVR model versatility and robustness. Our results demonstrate that the Resonance-Tuned models outperform top-performing Recognizing Textual Entailment (RTE) models in recognizing value resonance as well as zero-shot GPT-3.5 under several different prompt structures, emphasizing its practical applicability. Our findings highlight the potential of RVR in capturing cultural values within texts and the importance of task-specific modeling.

We perform a corpus analysis to develop a representation of the knowledge and reasoning used to interpret indirect speech acts.An indirect speech act (ISA) is an utterance whose intended meaning is different from its literal meaning.We focus on those speech acts in which slight changes in situational or contextual information can switch the dominant intended meaning of an utterance from direct to indirect or vice-versa.We computationalize how various contextual features can influence a speaker's beliefs, and how these beliefs can influence the intended meaning and choice of the surface form of an utterance.We axiomatize the domain-general patterns of reasoning involved, and implement a proof-of-concept architecture using Answer Set Programming.Our model is presented as a contribution to cognitive science and psycholinguistics, so representational decisions are justified by existing theoretical work.

Resolving Indirect Speech Acts (ISAs), in which the intended meaning of an utterance is not identical to its literal meaning, is essential to enabling the participation of intelligent systems in peoples’ everyday lives. Especially challenging are those cases in which the interpretation of such ISAs depends on context. To test a system’s ability to perform ISA resolution we need a corpus, but developing such a corpus is difficult, especialy given the contex-dependent requirement. This paper addresses the difficult problems of constructing a corpus of ISAs, taking inspiration from relevant work in using corpora for reasoning tasks. We present a formal representation of ISA Schemas required for such testing, including a measure of the difficulty of a particular schema. We develop an approach to authoring these schemas using corpus analysis and crowdsourcing, to maximize realism and minimize the amount of expert authoring needed. Finally, we describe several characteristics of collected data, and potential future work.

While various traditions under the 'virtue ethics' umbrella have been studied extensively and advocated by ethicists, it has not been clear that there exists a version of virtue ethics rigorous enough to be a target for machine ethics (which we take to include the engineering of an ethical sensibility in a machine or robot itself, not only the study of ethics in the humans who might create artificial agents). We begin to address this by presenting an embryonic formalization of a key part of any virtue-ethics theory: namely, the learning of virtue by a focus on exemplars of moral virtue. Our work is based in part on a computational formal logic previously used to formally model other ethical theories and principles therein, and to implement these models in artificial agents.

Norms and conventions play a central role in maintaining social order in multi-agent societies [2, 5]. I study the problem of how these norms and conventions can be learned from observation of heterogeneous sources, under conditions of uncertainty. This is necessary as it is not enough to simply hard code a set of norms into a new agent prior to entering society because norms can evolve over time as agents enter and leave the society [9].

HRI researchers have made major strides in developing robotic architectures that are capable of reading a limited set of social cues and producing behaviors that enhance their likeability and feeling of comfort amongst humans. However, the cues in these models are fairly direct and the interactions largely dyadic. To capture the normative qualities of interaction more robustly, we propose “consent” as a distinct, critical area for HRI research. Convening important insights in existing HRI work around topics like touch, proxemics, gaze, and moral norms, the notion of consent reveals key expectations that can shape how a robot acts in social spaces. Consent need not be limited to just an explicit permission given in ethically charged or normatively risky scenarios. Instead, it is a richer notion, one that covers even implicit acquiescence in scenarios that otherwise seem normatively neutral. By sorting various kinds of consent through social and legal doctrine, we delineate empirical and technical questions to meet consent challenges faced in major application domains and robotic roles. Attention to consent could show, for example, how extraordinary, norm-violating actions can be justified by agents and accepted by those around them. We argue that operationalizing ideas from legal scholarship can better guide how robotic systems might cultivate and sustain proper forms of consent.

Helper robots will be critical in many sectors: helping our elderly and disabled in assisted living facilities, conducting search-and-rescue missions in unforgiving terrain to save human lives, assisting our astronauts on the space station, or even monitoring our surroundings to keep us safe from national security threats. The ultimate goal of our research is to endow robots in these critical sectors with the ability to find creative ways to use and manipulate objects, especially when there is minimal and uncertain information. We have taken the first steps towards this goal and proposed a novel approach based on Dempster-Shafer (DS) theory for inferring object affordances.