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Online platforms offer forums with rich, real-world illustrations of moral reasoning. Among these, the r/AmITheAsshole (AITA) subreddit has become a prominent resource for computational research. In AITA, a user (author) describes an interpersonal moral scenario, and other users (commenters) provide moral judgments with reasons for who in the scenario is blameworthy. Prior work has focused on predicting moral judgments from AITA posts and comments. This study introduces the concept of moral sparks—key narrative excerpts that commenters highlight as pivotal to their judgments. Thus, sparks represent heightened moral attention, guiding readers to effective rationales. Through 24,676 posts and 175,988 comments, we demonstrate that research in social psychology on moral judgments extends to real-world scenarios. For example, negative traits (rude) amplify moral attention, whereas sympathetic traits (vulnerable) diminish it. Similarly, linguistic features, such as emotionally charged terms (e.g., anger), heighten moral attention, whereas positive or neutral terms (leisure and bio) attenuate it. Moreover, we find that incorporating moral sparks enhances pretrained language models’ performance on predicting moral judgment, achieving gains in F1 scores of up to 5.5%. These results demonstrate that moral sparks, derived directly from AITA narratives, capture key aspects of moral judgment and perform comparably to prior methods that depend on human annotation or large-scale generative modeling. 

Protocols model multiagent systems (MAS) by capturing the communications between its agents. Belief-Desire-Intention (BDI) architectures provide an attractive way for organizing an agent in terms of cognitive concepts. Current BDI approaches, however, lack adequate support for engineering protocol-based agents. We describe Argus, an approach that melds recent advances in flexible, declarative communication protocols with BDI architectures. For concreteness, we adopt Jason as an exemplar of the BDI paradigm and show how to support protocol-based reasoning in it. Specifically, Argus contributes (1) a novel architecture and formal operational semantics combining protocols and BDI; (2) a code generation-based programming model that guides the implementation of agents; and (3) integrity checking for incoming and outgoing messages that help ensure that the agents are well-behaved. The Argus conceptual architecture builds quite naturally on top of Jason. Thus, Argus enables building more flexible multiagent systems while using a BDI architecture than is currently possible. 

An interaction protocol specifies how the member agents of a decentralized multiagent system may communicate to satisfy their respective stakeholders' requirements. We focus on information protocols, which are fully declarative specifications of interaction and support asynchronous communication. We offer Mambo, an approach for protocol design. Mambo identifies common patterns of requirements, provides a notation to express them, and a verification procedure. Mambo incorporates heuristics to generate small internal representations for efficiency. Experimental results demonstrate Mambo's effectiveness on practical protocols. 

Argumentative stance classification plays a key role in identifying authors' viewpoints on specific topics. However, generating diverse pairs of argumentative sentences across various domains is challenging. Existing benchmarks often come from a single domain or focus on a limited set of topics. Additionally, manual annotation for accurate labeling is time-consuming and labor-intensive. To address these challenges, we propose leveraging platform rules, readily available expert-curated content, and large language models to bypass the need for human annotation. Our approach produces a multidomain benchmark comprising 4,498 topical claims and 30,961 arguments from three sources, spanning 21 domains. We benchmark the dataset in fully supervised, zero-shot, and few-shot settings, shedding light on the strengths and limitations of different methodologies. 

Commitments support flexible interactions between agents by capturing the meaning of their interactions. However, commitmentbased reasoning is not adequately supported in agent programming models. We contribute Azorus, a programming model based on declarative specifications centered on commitments and aligned with information protocols. Azorus supports reasoning about goals and commitments and combines modeling of commitments and protocols, thereby uniting three leading declarative approaches to engineering decentralized multiagent systems. Specifically, we realize Azorus over three existing technology suites: (1) Jason, a popular BDI-based programming model; (2) Cupid, a formal language and query-based model for commitments; and (3) BSPL, a language and its associated tools for information protocols, including Jason programming. We implement Azorus and demonstrate how it enables capturing interesting patterns of business logic. 

We demonstrate Orpheus, a novel programming model for engineering BDI agents that communicate on the basis of protocols. In Orpheus, protocols are specified in BSPL and agents are implemented in Jason. Given a protocol, Orpheus tooling generates a Jason adapter that exposes a simple interface for sending messages based on protocol state. Orpheus shines in the implementation of flexible, loosely-coupled agents, long a challenge for BDI-based agent programming approaches. Demonstration video: https://di.unito.it/orpheusvid 

We propose Orpheus, a novel programming model for communicating agents based on information protocols and realized using cognitive programming. Whereas traditional models are focused on reactions to handle incoming messages, Orpheus supports organizing the internal logic of an agent based on its goals. We give an operational semantics for Orpheus and implement this semantics in an adapter to help build agents. We use the adapter to demonstrate how Orpheus simplifies the programming of decentralized multiagent systems compared to the reactive programming model. 

We study (public) microtransit, a type of transportation service wherein a municipality offers point-to-point rides to residents, for a fixed, nominal fare. Microtransit exemplifies practical resource allocation problems that are often over-constrained in that not all ride requests (pickup and dropoff locations at specified times) can be satisfied or satisfied only by violating soft goals such as sustainability, and where economic signals (e.g., surge pricing) are not applicable—they would lead to unethical outcomes by effectively coercing poor people.We posit that instead of taking rider preferences as fixed, shaping them prosocially will lead to improved societal outcomes. Prosociality refers to an attitude or behavior that is intended to benefit others. This paper demonstrates a computational approach to prosociality in the context of a (public) microtransit service for disadvantaged riders. Prosociality appears as a willingness to adjust one’s pickup and dropoff times and locations to accommodate the schedules of others and to enable sharing rides (which increases the number of riders served with the same resources).This paper describes an interdisciplinary study of prosociality in microtransit between a transportation researcher, psychologists, a social scientist, and AI researchers. Our contributions are these: (1) empirical support for the viability of prosociality in microtransit (and constraints on it) through interviews with drivers and focus groups of riders; (2) a prototype mobile app demonstrating how our prosocial intervention can be combined with the transportation backend; (3) a reinforcement learning approach to model a rider and determine the best interventions to persuade that rider toward prosociality; and (4) a cognitive model of rider personas to enable evaluation of alternative interventions.