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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. 

Current languages for specifying multiagent protocols either over-constrain protocol enactments or complicate capturing their meanings. We propose Langshaw, a declarative protocol language based on (1) sayso, a new construct that captures who has priority over setting each attribute, and (2) nono and nogo, two constructs to capture conflicts between actions. Langshaw combines flexibility with an information model to express meaning. We give a formal semantics for Langshaw, procedures for determining the safety and liveness of a protocol, and a method to generate a message-oriented protocol (embedding needed coordination) suitable for flexible asynchronous enactment. 

Political news is often slanted toward its publisher’s ideology and seeks to influence readers by focusing on selected aspects of contentious social and political issues. We investigate political slants in news and their influence on readers by analyzing election-related news and reader reactions to the news on Twitter. To this end, we collected election-related news from six major US news publishers who covered the 2020 US presidential elections. We computed each publisher’s political slant based on the favorability of its news toward the two major parties’ presidential candidates. We found that the election-related news coverage shows signs of political slant both in news headlines and on Twitter. The difference in news coverage of the two candidates between the left-leaning (LEFT) and right-leaning (RIGHT) news publishers is statistically significant. The effect size is larger for the news on Twitter than for headlines. And, news on Twitter expresses stronger sentiments than the headlines. We identified moral foundations in reader reactions to the news on Twitter based on Moral Foundation Theory. Moral foundations in readers’ reactions to LEFT and RIGHT differ statistically significantly, though the effects are small. Further, these shifts in moral foundations differ across social and political issues. User engagement on Twitter is higher for RIGHT than for LEFT. We posit that an improved understanding of slant and influence can enable better ways to combat online political polarization. 

Moral reasoning reflects how people acquire and apply moral rules in particular situations. With social interactions increasingly happening online, social media provides an unprecedented opportunity to assess in-the-wild moral reasoning. We investigate the commonsense aspects of morality empirically using data from a Reddit subcommunity (i.e., a subreddit), r/AmITheAsshole, where an author describes their behavior in a situation and seeks comments about whether that behavior was appropriate. A commenter judges and provides reasons for whether an author or others’ behaviors were wrong. We focus on the novel problem of understanding the moral reasoning implicit in user comments about the propriety of an author’s behavior. Specifically, we explore associations between the common elements of the indicated rationale and the extractable social factors. Our results suggest that a moral response depends on the author’s gender and the topic of a post. Typical situations and behaviors include expressing anger emotion and using sensible words (e.g., f-ck, hell, and damn) in work-related situations. Moreover, we find that commonly expressed reasons also depend on commenters’ interests. 

This paper describes a newly launched project that will produce a new approach to public microtransit for underserved communities. Public microtransit cannot rely on pricing signals to manage demand, and current approaches face the challenges of simultaneously being underutilized and overextended. This project conceives of the setting as a sociotechnical system. Its main idea is to engage users through AI agents in conjunction with platform constraints to find solutions that purely technical conceptions cannot find. The project was specified over an intense series of discussions with key stakeholders (riders, city government, and nongovernmental agencies) and brings together expertise in the disciplines of AI, Operations Research, Urban Planning, Psychology, and Community Development. The project will culminate in a pilot study, results from which will facilitate the transfer of its technology to additional communities. 

A multiagent system is a society of autonomous agents whose interactions can be regulated via social norms. In general, the norms of a society are not hardcoded but emerge from the agents’ interactions. Specifically, how the agents in a society react to each other’s behavior and respond to the reactions of others determines which norms emerge in the society. We think of these reactions by an agent to the satisfactory or unsatisfactory behaviors of another agent as communications from the first agent to the second agent. Understanding these communications is a kind of social intelligence: these communications provide natural drivers for norm emergence by pushing agents toward certain behaviors, which can become established as norms. Whereas it is well-known that sanctioning can lead to the emergence of norms, we posit that a broader kind of social intelligence can prove more effective in promoting cooperation in a multiagent system. Accordingly, we develop Nest, a framework that models social intelligence via a wider variety of communications and understanding of them than in previous work. To evaluate Nest, we develop a simulated pandemic environment and conduct simulation experiments to compare Nest with baselines considering a combination of three kinds of social communication: sanction, tell, and hint. We find that societies formed of Nest agents achieve norms faster. Moreover, Nest agents effectively avoid undesirable consequences, which are negative sanctions and deviation from goals, and yield higher satisfaction for themselves than baseline agents despite requiring only an equivalent amount of information. 

We model a multiagent system (MAS) in socio-technical terms, combining a social layer consisting of norms with a technical layer consisting of actions that the agents execute. We express stakeholder needs to ensure that a MAS demonstrates resilience, allowing it to recover effectively from failures within a brief timeframe. This extended abstract presents a framework that computes probabilistic and temporal guarantees on whether the underlying requirements are met or, if failed, recovered. An important contribution of the framework is that it shows how the social and technical layers can be modeled jointly to enable the construction of resilient systems of autonomous agents. This paper facilitates specification refinement through methodological guidelines, emphasizing joint modeling of social and technical layers. We demonstrate our framework using a manufacturing scenario with competing public, industrial, and environmental requirements. This is an extended abstract of our JAAMAS paper available online. 

We model a multiagent system (MAS) in socio-technical terms, combining a social layer consisting of norms with a technical layer consisting of actions that the agents execute. This approach emphasizes autonomy, and makes assumptions about both the social and technical layers explicit. Autonomy means that agents may violate norms. In our approach, agents are computational entities, with each representing a different stakeholder. We express stakeholder requirements of the form that a MAS is resilient in that it can recover (sufficiently) from a failure within a (sufficiently short) duration. We present ReNo, a framework that computes probabilistic and temporal guarantees on whether the underlying requirements are met or, if failed, recovered. ReNo supports the refinement of the specification of a socio-technical system through methodological guidelines to meet the stated requirements. An important contribution of ReNo is that it shows how the social and technical layers can be modeled jointly to enable the construction of resilient systems of autonomous agents. We demonstrate ReNo using a manufacturing scenario with competing public, industrial, and environmental requirements. 

Everyone acknowledges the importance of responsible computing but practical advice is hard to come by. Important Internet applications are ways to accomplish business processes. We investigate how they can be geared to support responsibility as illustrated via sustainability. Sustainability is not only urgent and essential but also challenging due to engagement with human and societal concerns, diverse success criteria, and extended temporal and spatial scope. This article introduces a new framework for developing responsible Internet applications that synthesizes the perspectives of Theory of Change, Participatory System Mapping, and Computational Sociotechnical Systems.