With the rapid rise of AI technologies such as ChatGPT, understanding and integrating AI into K-12 education has become increasingly important. However, teachers often lack the AI literacy necessary to navigate these tools, which can lead to the perpetuation of misconceptions and biases in the classroom. This study seeks to identify K-12 teachers’ self-identified needs regarding AI education and compare them with existing research on professional development (PD) for AI integration. We surveyed 34 K-12 teachers to assess their knowledge of AI, identify areas where they require further support, and evaluate the relevance of current PD offerings. Our findings reveal a significant disconnect between the top-down assumptions of expert-driven PD initiatives and the practical needs articulated by teachers. Key themes emerged, including a diverse range of AI understanding among educators, a strong preference for hands-on, practical training, and a demand for ongoing institutional support. Additionally, teachers expressed a desire for collaborative learning environments to share strategies and experiences related to AI. This study underscores the importance of tailoring PD programs to address the unique contexts and challenges faced by educators, advocating for a more personalized approach that fosters confidence and competence in AI integration. By aligning PD offerings with teachers’ needs, we aim to enhance their ability to effectively utilize AI tools in the classroom, ultimately enriching the educational experience for students.

This issue of TPLP contains the regular papers of the 41 st

Abstract This paper introduces a framework for assisting policy authors in refining and improving their policies. In particular, we focus on authorization and obligation policies that can be encoded in Gelfond and Lobo’s $\mathscr{AOPL}$ language for policy specification. We propose a framework that detects the statements that make a policy inconsistent, underspecified, or ambiguous with respect to an action being executed in a given state. We also give attention to issues that arise at the intersection of authorization and obligation policies, for instance when the policy requires an unauthorized action to be executed. The framework is encoded in Answer Set Programming.

The Answer Set Programming (ASP) logic programming paradigm was introduced in the late 1990s, based on the answer set semantics of logic programs proposed by Gelfond and Lifschitz a decade earlier. To date, ASP has been applied to a variety of domains and demonstrated its suitability for solving knowledge-intensive tasks and combinatorial search problems, in particular. One direction of ASP research focuses on the development of efficient methods for computing answer sets. Novel techniques were initially adapted from SAT, which led to the introduction of satisfiability modulo theories. More recently, ideas have been derived from the study of the relationship between ASP and other computing paradigms, such as constraint satisfaction, quantified boolean formulas, first-order logic, pseudo-boolean solvers, theorem provers. A second line of work in the ASP community investigates multi-paradigm problem-solving for practical applications, which has resulted so far in the integration of ASP with description logics, constraint satisfaction and external means of computation.

Abstract This paper presents a practical application of Answer Set Programming to the understanding of narratives about restaurants. While this task was investigated in depth by Erik Mueller, exceptional scenarios remained a serious challenge for his script-based story comprehension system. We present a methodology that remedies this issue by modeling characters in a restaurant episode as intentional agents. We focus especially on the refinement of certain components of this methodology in order to increase coverage and performance. We present a restaurant story corpus that we created to design and evaluate our methodology.

Abstract We describe an application of Answer Set Programming to the understanding of narratives about stereotypical activities, demonstrated via question answering. Substantial work in this direction was done by Erik Mueller, who modeled stereotypical activities asscripts. His systems were able to understand a good number of narratives, but could not process texts describing exceptional scenarios. We propose addressing this problem by using a theory of intentions developed by Blount, Gelfond, and Balduccini. We present a methodology in which we substitute scripts byactivities(i.e., hierarchical plans associated with goals) and employ the concept of anintentional agentto reason about both normal and exceptional scenarios. We exemplify the application of this methodology by answering questions about a number of restaurant stories. This paper is under consideration for acceptance in TPLP.

AI developments on smart cities, if not critical, risk making a flawed urban model more efficient. Instead, we suggest that AI should challenge the mainstream techno-optimistic approach to solving urban problems by dialoguing with other academic fields, questioning the dominant urban paradigm, and creating transformative solutions. We claim that doing differently, rather than doing better, may be smarter for cities and the common good. This article is part of the special track on AI and Society.

Software engineering is becoming increasingly model-centric. Engineers are using models more within projects and their models are growing in complexity. A challenge facing the modeling community is evaluation of these models. One technique for software evaluation is detecting instances of established "good" or "bad" solutions in a system, often termed design patterns or antipatterns, respectively. Most approaches require implemented code for detection. However, this precludes early-stage analysis, and the evaluation of purely or mostly model-centric systems. In this position paper, we introduce a detection technique that uses answer set programming to find occurrences of patterns within sets of structural and behavioral models. We represent the patterns as rules and the structural and behavioral system models as facts, requiring both model types since some patterns specify both. We provide an overview of our proposed approach, contrast existing work, and present discussion points on its impact on model evaluation and anticipated challenges.

Answer Set Programming (ASP) is a logic programming, declarative, paradigm that was introduced in the late 1990s, based on the answer set semantics of logic programs proposed by Gelfond and Lifschitz a decade earlier.To date, ASP has been applied to a variety of domains and demonstrated its suitability for solving reasoning tasks such as knowledge-intensive tasks and combinatorial search problems.One direction of ASP research focuses on the development of efficient methods for computing answer sets.Novel techniques were initially adapted from SAT, then designed on purpose for ASP, e.g. to deal with specific ASP constructs, like aggregates, or to solve different reasoning tasks, such as cautious reasoning.More recently, ideas have been derived from the study of the relationship between ASP and other computing paradigms, such as constraint satisfaction, quantified Boolean formulas, first-order logic, pseudo-Boolean solvers, theorem provers, description logics, and external means of computation.The goal of this direction is to be able to cope more efficiently with practical problems, and to extend the domains that can be modeled and solved via ASP and its extensions.A recent, successful direction is CASP, which integrates ASP and constraint programming to solve problems with mixed discrete-continuous dynamics.The Answer Set Programming and Other Computing Paradigms (ASPOCP) series of workshops aims at fostering the cross-fertilization between ASP and other approaches by providing a venue for discussing advances in theory, solving techniques, and applications.Furthermore, the workshop encourages research that crosses the boundaries of ASP in other original directions, including action languages, probabilistic reasoning and machine learning, multi-agent and multi-context systems, argumentation frameworks and modularity.The ASPOCP workshop series has been held annually since its first edition in 2008 as a co-located event with the International Conference on Logic Programming (ICLP).The workshop has become an established event, as demonstrated by the considerable number of submissions and participants at each edition.The eighth edition of the workshop (ASPOCP 2015 1 ) was held in Cork, Ireland, on August 31st, 2015, as an affiliated event of the 31st ICLP meeting, which was part of "The Year of George Boole."Eleven papers were presented and the authors were invited to submit extended versions to be con-

Abstract This paper presents CoreALMlib, an $\mathscr{ALM}$ library of commonsense knowledge about dynamic domains. The library was obtained by translating part of the Component Library (CLib) into the modular action language $\mathscr{ALM}$ . CLib consists of general reusable and composable commonsense concepts, selected based on a thorough study of ontological and lexical resources. Our translation targets CLibstates (i.e., fluents) and actions. The resulting $\mathscr{ALM}$ library contains the descriptions of 123 action classes grouped into 43 reusable modules that are organized into a hierarchy. It is made available online and of interest to researchers in the action language, answer-set programming, and natural language understanding communities. We believe that our translation has two main advantages over its CLib counterpart: (i) it specifies axioms about actions in a more elaboration tolerant and readable way, and (ii) it can be seamlessly integrated with ASP reasoning algorithms (e.g., for planning and postdiction). In contrast, axioms are described in CLib using STRIPS-like operators, and CLib's inference engine cannot handle planning nor postdiction.

Abstract The paper introduces a new modular action language, ${\mathcal ALM}$ , and illustrates the methodology of its use. It is based on the approach of Gelfond and Lifschitz (1993,Journal of Logic Programming 17, 2–4, 301–321; 1998,Electronic Transactions on AI 3, 16, 193–210) in which a high-level action language is used as a front end for a logic programming system description. The resulting logic programming representation is used to perform various computational tasks. The methodology based on existing action languages works well for small and even medium size systems, but is not meant to deal with larger systems that requirestructuring of knowledge. $\mathcal{ALM}$ is meant to remedy this problem. Structuring of knowledge in ${\mathcal ALM}$ is supported by the concepts ofmodule(a formal description of a specific piece of knowledge packaged as a unit),module hierarchy, andlibrary, and by the division of a system description of ${\mathcal ALM}$ into two parts:theoryandstructure. Atheoryconsists of one or more modules with a common theme, possibly organized into a module hierarchy based on adependency relation. It contains declarations of sorts, attributes, and properties of the domain together with axioms describing them.Structuresare used to describe the domain's objects. These features, together with the means for defining classes of a domain as special cases of previously defined ones, facilitate the stepwise development, testing, and readability of a knowledge base, as well as the creation of knowledge representation libraries.

Somak Aditya, Chitta Baral, Nguyen Ha Vo, Joohyung Lee, Jieping Ye, Zaw Naung, Barry Lumpkin, Jenny Hastings, Richard Scherl, Dawn M. Sweet, Daniela Inclezan. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2015.

Abstract This paper explores the contributions of Answer Set Programming (ASP) to the study of an established theory from the field of Second Language Acquisition:Input Processing. The theory describes default strategies that learners of a second language use in extracting meaning out of a text based on their knowledge of the second language and their background knowledge about the world. We formalized this theory in ASP, and as a result we were able to determine opportunities for refining its natural language description, as well as directions for future theory development. We applied our model to automating the prediction of how learners of English would interpret sentences containing the passive voice. We present a system,PIas, that uses these predictions to assist language instructors in designing teaching materials.

Computer science students graduating in the next decade will face the big energy and environmental challenges of the 21st century. According to current trends, an increasing number of them will be employed in "green jobs" and will contribute to promoting biodiversity, minimizing the consumption of energy and materials, or restoring environmental quality. It is our job to prepare them for the task ahead. While the vast majority of textbooks and materials used in different areas of CS are oblivious to these problems, many resources can be found at the boundaries with other disciplines (e.g., environmental science, architecture, agriculture, etc.). Moreover, new computer applications are created every day for the analysis of current environmental problems and the evaluation of their possible solutions, but these applications are normally not mentioned in CS classes. This is a lost opportunity for engaging our students in the real world challenges we are facing today. Environmental problems can be addressed in the CS classroom in a way that does not impede the learning of the technical content, but rather increases students' ability to think critically about complex systems. This BoF intends to brainstorm innovative resources, examples, activities, and assignments that can be incorporated into the CS classes, in order to raise students' awareness to current ecological problems and, at the same time, illustrate the role computer scientists can play in solving them. For this BoF session, a laptop is optional.

Promoting sustainability, critical thinking, and ethical awareness are goals that appear in the vision statements of the majority of universities in the United States nowadays. One way of achieving these goals is by increasing the ecological literacy (i.e., ecoliteracy) of our students. In order to be effective, ecoliteracy should be taught not only in environmental studies classes, but rather across the institutional curriculum. In this paper we present three activities designed to be used in the first week of class in an introductory Database Systems course. Our activities promote critical thinking, systemic thinking, ethical behavior, and an increased awareness of ecological problems -- building blocks that set up the stage for teaching ecoliteracy.