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The practice of public parades involving marches has been used historically by political and civic actors as a tool of helping to accomplish recognition of particular agendas or, at the very least, suggest a peaceful proclamation of justice within a democratic framework. Over the past two decades, The pride parade and The flag parade are drawing the attention of radical stakeholders in Israel whilst taking place in Jerusalem—a religiously divided space. Based on EU official reports and data from news reports and social media publications between 2002 and 2022, this paper examines religious extremists' presence in the above parades, in parallel to the appearance of incitement, physical violence and vandalism. All as part of an ongoing Radicalisation process. In addition, it observes the establishment of The flower parade as a civic counter-response initiated by deradicalisation agents. The research reveals that under the values and rule of democracy, the mechanism of public parades can self-trigger a dynamic between participants and opposers, exploited by extreme collectives for violent purposes. It reflects upon a fundamental dispute between different interpretations of the connection between Judaism, nationality and family values in the light of the democratic framework. One is leading to religious-based exclusion agenda on the one hand, while the other is reacting with inclusion activities on the other. [ABSTRACT FROM AUTHOR]

In today's highly interconnected, open-networked computing world, artificial intelligence computer agents increasingly interact in groups with each other and with people both virtually and in the physical world. AI's current core challenges concern determining ways to build AI systems that function effectively and safely for people and the societies in which they live. To incorporate reasoning about people, research in multi-agent systems has engendered paradigmatic shifts in computer-agent design, models, and methods, as well as the development of new representations of information about agents and their environments. These changes have raised technical as well as ethical and societal challenges. This essay describes technical advances in computer-agent representations, decision-making, reasoning, and learning methods and highlights some paramount ethical challenges. [ABSTRACT FROM AUTHOR]

• On-line courses have made educational material widely accessible to students of varying abilities, backgrounds and styles. There is thus a growing need to accommodate for individual differences in e-learning systems. • We present a new algorithm called EduRank for personalizing educational content to students in e-learning systems that combines collaborative filtering with social choice theory. • EduRank constructs a difficulty ranking for each student by aggregating the rankings of similar students using different aspects of their performance on common questions. • The algorithm was tested on two data sets containing thousands of students and a million records and was able to outperform the state-of-the-art ranking approaches as well as a domain expert. • EduRank was embedded in a real classroom. It was shown to lead students to solve more difficult questions than an ordering by a domain expert, without reducing their performance. The prevalence of e-learning systems and on-line courses has made educational material widely accessible to students of varying abilities and backgrounds. There is thus a growing need to accommodate for individual differences in e-learning systems. This paper presents an algorithm called EduRank for personalizing educational content to students that combines a collaborative filtering algorithm with voting methods. EduRank constructs a difficulty ranking for each student by aggregating the rankings of similar students using different aspects of their performance on common questions. These aspects include grades, number of retries, and time spent solving questions. It infers a difficulty ranking directly over the questions for each student, rather than ordering them according to the student's predicted score. The EduRank algorithm was tested on two data sets containing thousands of students and a million records. It was able to outperform the state-of-the-art ranking approaches as well as a domain expert. EduRank was used by students in a classroom activity, where a prior model was incorporated to predict the difficulty rankings of students with no prior history in the system. It was shown to lead students to solve more difficult questions than an ordering by a domain expert, without reducing their performance. [ABSTRACT FROM AUTHOR]

This article provides new techniques for optimizing domain design for goal and plan recognition using plan libraries. We define two new problems: Goal Recognition Design for Plan Libraries (GRD-PL) and Plan Recognition Design (PRD). Solving the GRD-PL helps to infer which goal the agent is trying to achieve, while solving PRD can help to infer how the agent is going to achieve its goal. For each problem, we define a worst-case distinctiveness measure that is an upper bound on the number of observations that are necessary to unambiguously recognize the agent's goal or plan. This article studies the relationship between these measures, showing that the worst-case distinctiveness of GRD-PL is a lower bound of the worst-case plan distinctiveness of PRD and that they are equal under certain conditions. We provide two complete algorithms for minimizing the worst-case distinctiveness of plan libraries without reducing the agent's ability to complete its goals: One is a brute-force search over all possible plans and one is a constraint-based search that identifies plans that are most difficult to distinguish in the domain. These algorithms are evaluated in three hierarchical plan recognition settings from the literature. We were able to reduce the worst-case distinctiveness of the domains using our approach, in some cases reaching 100% improvement within a predesignated time window. Our iterative algorithm outperforms the brute-force approach by an order of magnitude in terms of runtime. [ABSTRACT FROM AUTHOR]

This article provides new techniques for optimizing domain design for goal and plan recognition using plan libraries. We define two new problems: Goal Recognition Design for Plan Libraries (GRD-PL) and Plan Recognition Design (PRD). Solving the GRD-PL helps to infer which goal the agent is trying to achieve, while solving PRD can help to infer how the agent is going to achieve its goal. For each problem, we define a worst-case distinctiveness measure that is an upper bound on the number of observations that are necessary to unambiguously recognize the agent's goal or plan. This article studies the relationship between these measures, showing that the worst-case distinctiveness of GRD-PL is a lower bound of the worst-case plan distinctiveness of PRD and that they are equal under certain conditions. We provide two complete algorithms for minimizing the worst-case distinctiveness of plan libraries without reducing the agent's ability to complete its goals: One is a brute-force search over all possible plans and one is a constraint-based search that identifies plans that are most difficult to distinguish in the domain. These algorithms are evaluated in three hierarchical plan recognition settings from the literature. We were able to reduce the worst-case distinctiveness of the domains using our approach, in some cases reaching 100% improvement within a predesignated time window. Our iterative algorithm outperforms the brute-force approach by an order of magnitude in terms of runtime. [ABSTRACT FROM AUTHOR]

In many e-learning settings, allowing students to choose which skills to practice encourages their motivation and contributes to learning. However, when given choice, students may prefer to practice skills that they already master, rather than practice skills they need to master. On the other hand, requiring students only to practice their required skills may reduce their motivation and lead to dropout. In this paper, we model this tradeoff as a multi-agent planning task, which we call SWOPP (Supervisor- Worker Problem with Partially Overlapping goals), involving two agents—a supervisor (teacher) and a worker (student)—each with different, yet non-conflicting, goals. The supervisor and worker share joint goals (mastering skills). The worker plans to achieve his/her own goals (completing an e-learning session) at a minimal cost (effort required to solve problems). The supervisor guides the worker towards achieving the joint goals by controlling the problems in the choice set for the worker. We provide a formal model for the SWOPP task and two sound and complete algorithms for the supervisor to guide the worker's plan to achieve their joint goals. We deploy SWOPP for the first time in a real-world study to personalize math questions for K5 students using an e-learning software in schools. We show that SWOPP was able to guide students' interactions with the software to practice necessary skills without deterring their motivation. [ABSTRACT FROM AUTHOR]

Background: This study addresses the suicide risk predicting challenge by exploring the predictive ability of machine learning (ML) models integrated with theory‐driven psychological risk factors in real‐time crisis hotline chats. More importantly, we aimed to understand the specific theory‐driven factors contributing to the ML prediction of suicide risk. Method: The dataset consisted of 17,654 crisis hotline chat sessions classified dichotomously as suicidal or not. We created a suicide risk factors‐based lexicon (SRF), which encompasses language representations of key risk factors derived from the main suicide theories. The ML model (Suicide Risk‐Bert; SR‐BERT) was trained using natural language processing techniques incorporating the SRF lexicon. Results: The results showed that SR‐BERT outperformed the other models. Logistic regression analysis identified several theory‐driven risk factors significantly associated with suicide risk, the prominent ones were hopelessness, history of suicide, self‐harm, and thwarted belongingness. Limitations: The lexicon is limited in its ability to fully encompass all theoretical concepts related to suicide risk, nor to all the language expressions of each concept. The classification of chats was determined by trained but non‐professionals in metal health. Conclusion: This study highlights the potential of how ML models combined with theory‐driven knowledge can improve suicide risk prediction. Our study underscores the importance of hopelessness and thwarted belongingness in suicide risk and thus their role in suicide prevention and intervention. [ABSTRACT FROM AUTHOR]

Plan recognition algorithms output hypotheses about an agent's plans from its observed actions. Due to imperfect knowledge about the agent's behavior and the environment, it is often the case that there are multiple hypotheses about an agent's plans that are consistent with the observations, though only one of these hypotheses is correct. This paper addresses the problem of how to disambiguate between hypotheses during the recognition process, by querying the acting agent about whether a given plan is part of the correct hypothesis. The main contribution is a sound and complete process for reducing the set of possible hypotheses called Sequential Plan Recognition (SPR). SPR iteratively queries the user and revises the set of possible hypotheses according to the outcome of the query. Several policies are provided for choosing which plans to query the agent. These policies address the problem of how to reduce the number of hypotheses during the recognition process using a minimal number of queries. The proposed policies include policies that use maximum likelihood and information gain measures. The paper provides a complexity analysis of the SPR process and the proposed query policies. It demonstrate its efficiency on two known domains from the literature, describing how performance and runtime are affected by features in the domain. Our results can inform the design of future plan recognition systems that interleave the recognition process with intelligent interventions of their users. [ABSTRACT FROM AUTHOR]

Personalizing the use of educational mathematics applets to fit learners' characteristics poses a great challenge. The present study adopted a unique approach by comparing personalization processes implemented by a machine to those implemented by a human teacher. Given the different affordances—the machine's access to historical log file data, computation and automatization, and the teacher's mathematical knowledge, pedagogical approach and personal acquaintance—the study hypothesized that different considerations would lead to different personalization and learning outcomes. Mathematical applets were assigned to 77 students in the 4th and 5th grades either by an expert teacher or by an algorithm. The assignment took place in a controlled setting in which the teacher was unaware which students were eventually assigned according to her recommendations. The teacher and the machine each recommended an ordered sequence of ten applets per student. The findings suggest that the teacher-assigned group outperformed the machine-assigned group among 5th graders when the applets were sequenced in increasing order of difficulty. In the 4th grade, only the machine recommended a sequence of increasing difficulty and both groups achieved equal performance. The study concludes that in the case of data-driven personalization processes, machines and teachers should learn from each other's affordances and considerations. [ABSTRACT FROM AUTHOR]

Badges are endemic to online interaction sites, from question and answer (Q&A) websites to ride sharing, as systems for rewarding participants for their contributions. This article studies how badge design affects people's contributions and behavior over time. Past work has shown that badges "steer" people's behavior toward substantially increasing the amount of contributions before obtaining the badge, and immediately decreasing their contributions thereafter, returning to their baseline contribution levels. In contrast, we find that the steering effect depends on the type of user, as modeled by the rate and intensity of the user's contributions. We use these measures to distinguish between different groups of user activity, including users who are not affected by the badge system despite being significant contributors to the site. We provide a predictive model of how users change their activity group over the course of their lifetime in the system. We demonstrate our approach empirically in three different Q&A sites on Stack Exchange with hundreds of thousands of users, for two types of activities (editing and voting on posts). [ABSTRACT FROM AUTHOR]

This article proposes a negotiation game, based on the weighted voting paradigm in cooperative game theory, where agents need to form coalitions and agree on how to share the gains. Despite the prevalence of weighted voting in the real world, there has been little work studying people's behavior in such settings. This work addresses this gap by combining game-theoretic solution concepts with machine learning models for predicting human behavior in such domains. We present a five-player online version of a weighted voting game in which people negotiate to create coalitions. We provide an equilibrium analysis of this game and collect hundreds of instances of people's play in the game. We show that a machine learning model with features based on solution concepts from cooperative game theory (in particular, an extension of the Deegan-Packel Index) provide a good prediction of people's decisions to join coalitions in the game. We designed an agent that uses the prediction model to make offers to people in this game and was able to outperform other people in an extensive empirical study. These results demonstrate the benefit of incorporating concepts from cooperative game theory in the design of agents that interact with people in group decision-making settings. [ABSTRACT FROM AUTHOR]

Demonstrability—the extent to which group members can recognize a correct solution to a problem—has a significant effect on group performance. However, the interplay between group size, demonstrability and performance is not well understood. This paper addresses these gaps by studying the joint effect of two factors—the difficulty of solving a problem and the difficulty of verifying the correctness of a solution—on the ability of groups of varying sizes to converge to correct solutions. Our empirical investigations use problem instances from different computational complexity classes, NP-Complete (NPC) and PSPACE-complete (PSC), that exhibit similar solution difficulty but differ in verification difficulty. Our study focuses on nominal groups to isolate the effect of problem complexity on performance. We show that NPC problems have higher demonstrability than PSC problems: participants were significantly more likely to recognize correct and incorrect solutions for NPC problems than for PSC problems. We further show that increasing the group size can actually decrease group performance for some problems of low demonstrability. We analytically derive the boundary that distinguishes these problems from others for which group performance monotonically improves with group size. These findings increase our understanding of the mechanisms that underlie group problem-solving processes, and can inform the design of systems and processes that would better facilitate collective decision-making. [ABSTRACT FROM AUTHOR]