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1. Logic-based Software Modeling with FOML

   https://doi.org/10.5381/jot.2020.19.3.a19  

   Mira Balaban, Igal Khitron (October 2020, Journal of object technology)

   Models are at the heart of the emerging Model-Based Systems Engineering (MBSE) approach. MBSE is motivated by the growing complexity of software, which requires multiple levels of abstraction that programming languages do not support.In MBSE, models play a central role in the software evolution process. Rich model management must rely on a unifying underlying formal framework that can support, integrate, and mediate powerful modeling services. This paper describes FOML, a Framework for Object Modeling with Logic, its realization in a modeling tool, proves the correctness of class modeling in FOML, illustrates the process of software modeling with the tool, and presents the main features of the system. The FOML framework for software modeling is compact yet powerful, formal, and is based on an underlying logic rule language called PathLP. The combination of class-based conceptualization with a formal logical base enables clean mediation and integration of a wide range of modeling activities and provides a provably correct formulation of class models. Our implementation of FOML features seamless integration of multiple modeling services that simultaneously support multiple models and provide reasoning,meta-reasoning, validation, testing, and evolution services. 

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2. Mediating Students’ Scientific Argumentation to Support Model Revision

   Zhou, J. Hmelo-Silver; C. E.; Danish, J.; Ryan, Z.; Cruz Gonzalez, C.; Duncan, R. G.; Chinn, C. A.; Murphy D. (July 2022, Computersupported collaborative learning)

   A. Weinberger; W. Chen; D.Hernández-Leo; D., B. Chen (Ed.)

   Scientific argumentation and modeling are both core practices in learning and doing science. However, they are challenging for students. Although there is considerable literature about scientific argumentation or modeling practice in K-12 science, there are limited studies on how engaging students in modeling and scientific argumentation might be mutually supportive. This study aims to explore how 5th graders can be supported by our designed mediators as they engage in argumentation and modeling, in particular, model revision. We implemented a virtual afterschool science club to examine how our modeling tool – MEME (Model and Evidence Mapping Environment), provided evidence, peer comments, and other mediators influenced students in learning about aquatic ecosystems through developing a model. While both groups that we examined constructed strong arguments and developed good models, we show how the mediators played different roles in helping them be successful. 

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3. A minimalist dataset for systematic generalization of perception, syntax, and semantics

   Li, Q.; Huang, S.; Hong, Y.; Zhu, Y.; Wu, Y. N.; Zhu, S. C. (May 2023, International Conference on Learning Representations (ICLR 2023))

   Inspired by humans’ exceptional ability to master arithmetic and generalize to new problems, we present a new dataset, Handwritten arithmetic with INTegers (HINT), to examine machines’ capability of learning generalizable concepts at three levels: perception, syntax, and semantics. In HINT, machines are tasked with learning how concepts are perceived from raw signals such as images (i.e., perception), how multiple concepts are structurally combined to form a valid expression (i.e., syntax), and how concepts are realized to afford various reasoning tasks (i.e., semantics), all in a weakly supervised manner. Focusing on systematic generalization, we carefully design a five-fold test set to evaluate both the interpolation and the extrapolation of learned concepts w.r.t. the three levels. Further, we design a few-shot learning split to determine whether or not models can rapidly learn new concepts and generalize them to more complex scenarios. To comprehend existing models’ limitations, we undertake extensive experiments with various sequence-to-sequence models, including RNNs, Transformers, and GPT-3 (with the chain of thought prompting). The results indicate that current models struggle to extrapolate to long-range syntactic dependency and semantics. Models exhibit a considerable gap toward human-level generalization when evaluated with new concepts in a few-shot setting. Moreover, we discover that it is infeasible to solve HINT by merely scaling up the dataset and the model size; this strategy contributes little to the extrapolation of syntax and semantics. Finally, in zero-shot GPT-3 experiments, the chain of thought prompting exhibits impressive results and significantly boosts the test accuracy. We believe the HINT dataset and the experimental findings are of great interest to the learning community on systematic generalization. 

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4. The Report of AAAI 2025 Workshop 11: Cooperative Multi-Agent Systems Decision-Making and Learning: Human-Multi-Agent Cognitive Fusion

   Yang, Qin; Beltrame, Giovanni; Li, Alberto Quattrini; Amato, Christopher (April 2025, Interactive AI Magazine)

   Many domains of AI and its effects are established, which mainly rely on their integration modeling cognition of human and AI agents, collecting and representing knowledge using them at the human level, and maintaining decision-making processes towards physical action eligible to and in cooperation with humans. Especially in human-robot interaction, many AI and robotics technologies are focused on human- robot cognitive modeling, from visual processing to symbolic reasoning and from reactive control to action recognition and learning, which will support human-multi-agent cooperative achieving tasks. However, the main challenge is efficiently combining human motivations and AI agents’ purposes in a sharing architecture and reaching a consensus in complex environments and missions. To fill this gap, this workshop brings together researchers from different communities inter- ested in multi-agent systems (MAS) and human-robot interaction (HRI) to explore potential approaches, future research directions, and domains in human-multi-agent cognitive fusion. 

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5. We Move Up Levels Together: Dignity, Transformative Marketing, and the Repurposing of Racial Capitalism

   https://doi.org/10.18357/anthropologica65220232620  

   Ellison, Susan (February 2024, Anthropologica)

   Indigenous Bolivians, especially women, are climbing the ranks of global multilevel marketing (MLM) companies like Herbalife, Omnilife, and Hinode, seeking to join Bolivia’s purportedly rising Indigenous middle class. Through MLMs, Indigenous direct sales distributors pursue a dignified life materialized in better homes, smart dressing, international travel, and the respect they receive at recruitment events. In their recruitment and sales pitches to potential buyers and downline vendors, Indigenous distributors fashion testimonials about their successes that explicitly critique existing avenues of class mobility and their racialization in two ways. First, these testimonials counter the skepticism that multilevel marketing companies face by citing a litany of false promises offered by higher education, salaried employment, and public sector jobs—avenues long heralded as the stepping stones to entwined racial and class mobility in Bolivia. They further voice their frustrations with perceived status hierarchies and organizational barriers among Indigenous merchants, highlighting their own sense of alienation from the connections and protections that have enabled the financial success of other Indigenous entrepreneurs. Second, while lodging these critiques, distributors repurpose racialization toward their own recruitment ends. As MLM distributors pursue their visions of the good life, the testimonials that Indigenous MLM recruiters craft to enable their ascent expose, rely on, and rework the bounds of racial capitalism. Ultimately, their critiques reveal how the racial partitioning that enables capitalist extraction operates through the work of direct sales. 

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