Behavior Of Agents Research Articles

Inferring Occluded Agent Behavior in Dynamic Games From Noise Corrupted Observations

Inferring Occluded Agent Behavior in Dynamic Games From Noise Corrupted Observations

The effect of blue and UV light-emitted diodes (LEDs) on the disturbance of the whitefly natural enemies Macrolophus pygmaeus and Encarsia formosa

Recent studies demonstrated that blue and UV light effectively disturb greenhouse whiteflies (Trialeurodes vaporariorum) from a host plant (“push”), enhancing their capture on attractive traps after dispersal (“pull”). This push–pull technique could contribute to whitefly biocontrol and lead to the development of mass trapping. However, the impact of these wavelengths on the behavior of whitefly natural enemies, commonly deployed for biocontrol, remains underexplored. This study investigated the response of two important whitefly biocontrol agents, Macrolophus pygmaeus and Encarsia formosa, to blue and UV light. Initial experiments were conducted under artificial conditions (no prey/host and plant present) to isolate their visual behavior to these lights, excluding other stimuli. In follow up experiments, the effect of these lights was assessed in a more complex scenario, involving prey/host (whitefly nymphs) on tomato leaves. Results revealed that both M. pygmaeus and E. formosa exhibited avoidance behavior under blue and UV LEDs in the artificial setup, irrespective of ambient light or dark conditions. Contrastingly, in presence of prey/host on tomato leaves, only M. pygmaeus was significantly disturbed by UV light, whereas in all other scenarios there was little to no effect of blue and UV LEDs on the behavior of these natural enemies.. These findings enhance our understanding on the visual behavior of crucial biocontrol agents and highlight the importance of refining targeted whitefly control techniques that minimize impacts on beneficial insects and optimize the use of LEDs in integrated pest management (IPM).

A resilient consensus algorithm with inputs for the distributed monitoringof cyber-physical systems

Recent advancements in multi-agent systems (MASs) have led to the development of numerous algorithms for achieving specific objectives, such as consensus. However, security remains a major challenge in MAS consensus, particularly addressing the adversarial behavior of malicious agents. This paper explores the extension of Mean-Subsequence-Reduced (MSR) algorithm-type mechanisms for resilient dynamic consensus in the presence of input reference signals. We provide necessary and sufficient conditions for resilient dynamic consensus without relying on the presence of trusted agents. Additionally, we experimentally validate the proposed algorithm and related conditions over a small cyber–physical system used for temperature monitoring. Furthermore, we propose and experimentally validate a fault-detection and recovery algorithm to achieve a resilient dynamic average consensus of regular agents.

Nutritional ecology of a predatory stink bug: A comparative analysis of nutrient acquisition from two prey species and an artificial diet

The nutritional content of prey plays a pivotal role in shaping the predatory behavior and effectiveness of natural pest control agents like Eocanthecona furcellata. We assessed the biological performance and life history of E. furcellata on three diets, larvae of Spodoptera frugiperda, larvae of Tenebrio molitor, and an artificial diet. We also analyzed their nutritional content and quantified consumption and nutritional intake by E. furcellata. The three diets differed substantially in water, protein, and fat content. The moisture content of T. molitor larvae was significantly lower (35.0 ± 0.3 %) than that of S. frugiperda larvae (61.0 ± 0.6 %) or the artificial diet (88.7 ± 0.1 %). Female E. furcellata obtained 8.3 mg of protein per day feeding on S. frugiperda, significantly more than on T. molitor (2.4 mg) or the artificial diet (0.4 mg). 15-day fecundity ranged from 140.2 (±9.3) eggs on S. frugiperda, to 103.3 (±4.8) eggs on T. molitor, and 75.1 (±7.1) eggs on the diet. Our study provides insights into the nutritional ecology of E. furcellata and ways to improve the breeding of this beneficial species for augmentation in agricultural applications.

Communal and agentic teacher behavior in teacher-child dyads. A new self-report scale based on a circumplex approach

IntroductionThe goal of this study was to develop a self-report scale measuring the teacher’s interpersonal behavior in teacher-child dyads in elementary schools and to provide evidence of the scale’s validity. Using the framework of interpersonal theory, the Questionnaire on Dyadic Interpersonal Teacher Behavior (DITeB) models teacher behavior in an interpersonal circumplex that allows for the simultaneous analysis of teacher behavior along the two orthogonal dimensions of communion (warmth) and agency (guidance, monitoring).MethodsWe analyzed 440 dyadic teacher-child relationships derived from 88 teacher reports. Teachers rated the 20 items (8 scales) of the DITeB to describe their interpersonal behavior towards a child. To test the scale’s structural validity, we conducted confirmatory circumplex structural analyses. To test the scale’s construct validity, we used teachers ‘descriptions of relationship quality (closeness, conflict, dependency), and of their child related emotions (enjoyment, anger, anxiety). In addition, teachers reported on the child’s academic performance (numeric grades), as well as their professional beliefs about heterogeneity and self-efficacy for adaptive teaching, data which were used for further construct validation.ResultsWe obtained empirical support for both structural and construct validity of the DITeB. Teacher interpersonal behavior correlated significantly with perceived relationship quality and emotions: high relationship quality and positive emotions were associated with high communion and low agency, whereas low relationship quality and negative emotions were associated with low communion and high agency, supporting construct validity. Additionally, as child’s academic performance improved, teachers’ agency decreased. Towards different groups of children, teachers’ interpersonal behaviors varied systematically based on their professional beliefs: teachers with strong heterogeneity beliefs tended to be more communal towards low-performing children compared to high-performing children. Moreover, teachers with strong self-efficacy towards adaptive teaching adjusted their agency more flexibly according to children’s performance in school, further supporting the construct validity of our instrument.DiscussionWe discuss how our instrument enriches the spectrum of existing scales on interpersonal teacher behavior. By focusing dyadic specific teacher behavior, the DITeB allows examining the extent to which the teacher adapts their behavior to child characteristics (adaptive teaching), and the effect of teacher behavior on child outcomes.

Bayesian Mechanics of Synaptic Learning Under the Free-Energy Principle.

The brain is a biological system comprising nerve cells and orchestrates its embodied agent's perception, behavior, and learning in dynamic environments. The free-energy principle (FEP) advocated by Karl Friston explicates the local, recurrent, and self-supervised cognitive dynamics of the brain's higher-order functions. In this study, we continue to refine the FEP through a physics-guided formulation; specifically, we apply our theory to synaptic learning by considering it an inference problem under the FEP and derive the governing equations, called Bayesian mechanics. Our study uncovers how the brain infers weight changes and postsynaptic activity, conditioned on the presynaptic input, by deploying generative models of the likelihood and prior belief. Consequently, we exemplify the synaptic efficacy in the brain with a simple model; in particular, we illustrate that the brain organizes an optimal trajectory in neural phase space during synaptic learning in continuous time, which variationally minimizes synaptic surprisal.

Developing teamwork: transitioning between stages in human-agent collaboration

IntroductionHuman-centric artificial intelligence (HCAI) focuses on systems that support and collaborate with humans to achieve their goals. To better understand how collaboration develops in human-AI teaming, further exploration grounded in a theoretical model is needed. Tuckman's model describes how team development among humans evolves by transitioning through the stages of forming, storming, norming, performing, and adjourning. The purpose of this pilot study was to explore transitions between the first three stages in a collaborative task involving a human and a human-centric agent.MethodThe collaborative task was selected based on commonly performed tasks in a therapeutic healthcare context. It involved planning activities for the upcoming week to achieve health-related goals. A calendar application served as a tool for this task. This application embedded a collaborative agent designed to interact with humans following Tuckman's stages of team development. Eight participants completed the collaborative calendar planning task, followed by a semi-structured interview. Interviews were transcribed and analyzed using inductive content analysis.ResultsThe results revealed that the participants initiated the storming stage in most cases (n = 7/8) and that the agent initiated the norming stage in most cases (n = 5/8). Additionally, three main categories emerged from the content analyses of the interviews related to participants' transition through team development stages: (i) participants' experiences of Tuckman's first three stages of team development; (ii) their reactions to the agent's behavior in the three stages; and (iii) factors important to the participants to team up with a collaborative agent.ConclusionResults suggest ways to further personalize the agent to contribute to human-agent teamwork. In addition, this study revealed the need to further examine the integration of explicit conflict management into human-agent collaboration for human-agent teamwork.

Artefact design and societal worldview.

Technological artefacts are created in accordance with the values and worldviews of their designers. In operation, they act as a medium, facilitating and constraining human interaction with, and perception of, the world. When used on a large scale, they may lastingly affect societal ethos. If institutional structures of domination allocate the resources necessary for artefact design and development to some population groups over others, the direction and extent of such an effect may lead to increased disparity and inequity. While the direct influence of technology on opinion is well-studied, the evaluation of non-epistemic values, assumptions and presuppositions is a hurdle in the way of a deeper understanding of the large-scale effects of asymmetries in worldviews embodied by artefacts. Here, we show that artefacts have a strong potential to bias societal worldviews when they are distributed unevenly across the value spectrum. They can affect the clustering behaviour of agents with regard to worldview, both aiding and hindering intra- and inter-cluster diversity, depending on their distribution and frequency. Our findings underline the distributional sensitivity of worldview dynamics to institutional structures of domination. We highlight the importance of procedural interventions such as participatory design, which explicitly acknowledges existing asymmetries and redistributes power accordingly.This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'.

Making a human's trust repair for an agent in a series of tasks through the agent's empathic behavior

As AI technology develops, trust in agents (including robot, AI and anthropomorphic agent) is becoming more important for more AI applications in human society. Possible ways to improve the trust relationship include empathy, success-failure series, and capability (performance). Appropriate trust is less likely to cause deviations between actual and ideal performance. In this study, we focus on the agent's empathic behavior and success-failure series to increase trust in agents. We experimentally examine the effect of empathic behavior from agent to person on changes in trust over time. The experiment was conducted with a two-factor mixed design: empathic behavior (available, not available) and success-failure series (phase 1 to phase 5). An analysis of variance (ANOVA) was conducted using data from 200 participants. The results showed an interaction between the empathic behavior factor and the success-failure series factor, with trust in the agent repairing when empathic behavior was present. This result supports our hypothesis. This study shows that designing agents to be empathic behavior is an important factor for trust and helps humans build appropriate trust relationships with agents.

Skill matters: Dynamic skill learning for multi-agent cooperative reinforcement learning

With the popularization of intelligence, the necessity of cooperation between intelligent machines makes the research of collaborative multi-agent reinforcement learning (MARL) more extensive. Existing approaches typically address this challenge through task decomposition of the environment or role classification of agents. However, these studies may rely on the sharing of parameters between agents, resulting in the homogeneity of agent behavior, which is not effective for complex tasks. Or training that relies on external rewards is difficult to adapt to scenarios with sparse rewards. Based on the above challenges, in this paper we propose a novel dynamic skill learning (DSL) framework for agents to learn more diverse abilities motivated by internal rewards. Specifically, the DSL has two components: (i) Dynamic skill discovery, which encourages the production of meaningful skills by exploring the environment in an unsupervised manner, using the inner product between a skill vector and a trajectory representation to generate intrinsic rewards. Meanwhile, the Lipschitz constraint of the state representation function is used to ensure the proper trajectory of the learned skills. (ii) Dynamic skill assignment, which utilizes a policy controller to assign skills to each agent based on its different trajectory latent variables. In addition, in order to avoid training instability caused by frequent changes in skill selection, we introduce a regularization term to limit skill switching between adjacent time steps. We thoroughly tested the DSL approach on two challenging benchmarks, StarCraft II and Google Research Football. Experimental results show that compared with strong benchmarks such as QMIX and RODE, DSL effectively improves performance and is more adaptable to difficult collaborative scenarios.

Distributed misbehavior monitors for socially organized autonomous systems

In systems in which many heterogeneous agents operate autonomously, with competing goals and without a centralized planner or global information repository, safety and performance can only be guaranteed by “social” rules imposed on the behavior of individual agents. Social laws are structured in a way that they can be verified just by using local information made available to an agent by a small number of its neighbors. Automobile mobility with traffic rules and logistics robots in warehouses are canonical examples of such “regulated autonomy”, but many other fairly-competing autonomous systems are to be expected shortly. In these systems, detecting whether an agent is not abiding by the rules is crucial for raising an alert and taking appropriate countermeasures. However, the limited visibility due to the local nature of the information makes the problem of misbehavior detection hard for any single agent, and only an exchange of information between agents can provide sufficient clues to arrive at a decision. This paper attacks the misbehavior detection problem for a class of socially organized autonomous systems, where the behavior of agents depends on the presence or absence of other neighbors. We propose a solution involving a “local monitor”, which runs on each agent and includes a hybrid observer and a set-valued consensus node. Based on whatever visibility is available, it reconstructs a set-valued occupancy estimate of nearby regions and combines it with communicating neighbors to reach a shared view and a mismatch discovery. We provide a formal framework for describing social rules that unify many different applications and a tool to generate code automatically for local monitors. The technique is demonstrated in various systems, including self-driving cars, autonomous forklifts, and distributed power plants.

DOMINANTS FOR THE NATIONAL ECONOMIES’ COMPETITIVENESS

This article is devoted to innovation as a prerequisite for the national economies' sustainable development and a domain for their global competitiveness. The paper aims to highlight the internal essence of mutual relations between innovation, competition, and economic growth. It was assumed that innovation positively affected the competitive behaviour of economic agents and eventually contributed to an increase in public welfare. Regarding the period from 2007 to 2022, we considered the sample of Central European countries and the Baltic states. Theoretical generalization has been applied to systematize the factors determining competition and innovation. We used statistical methods and comparative analysis to investigate the indicators' dynamics. The interdependencies between macroeconomic dynamics and a set of competition and innovation indicators have been assessed. It was proved that the proportion of medium and high-tech industry value added in the total value added of manufacturing was quite important, yet not the ultimate criterion for ensuring high growth rates and sustainable development. The dynamics of commercial spending on research and development had been ambiguous and unsustainable. To ensure strategic competitiveness, national governments should encourage innovation in their respective business communities. The sampled economies have been examined considering a set of innovation and competition indicators (e.g., gross capital formation, research and development expenditures, logistics performance, export technological structure, carbon dioxide emission, and renewable energy consumption). Considering the implications of globalization, tax burden as an integral characteristic of the national economy's competitiveness has been evaluated as well. It was pointed out that: a) a stable, understandable and transparent tax policy should be implemented to protect the investors' property rights; b) a system of total anti-corruption measures aimed at preventing the practices of receiving undue benefits should be set; and c) the business sector should improve own environmental and social responsibility complexly, introduce resource-saving, green and circular economy.

Study on the Performances of Toughening UV-LED-Cured Epoxy Electronic Encapsulants

This study aims to investigate the effects of three toughening agents—core–shell rubber particles (CSR), nano-silica particles (NSPs), and epoxidized polybutadiene (EPB)—on the performance of UV-LED-cured epoxy electronic encapsulants. By systematically comparing the curing behavior, thermomechanical properties, and impact resistance of different toughening agents in alicyclic epoxy resins, their potential applications in more environmentally friendly UV-cured electronic encapsulation are evaluated. The results show that NSP and CSR toughened samples have fast cured speed under 365 nm UV-LED light, but it affects the depth of curing under low energy conditions. They maintain high Tg, high modulus, and low thermal expansion coefficient (CTE), especially in the NSP-toughened sample. The EPB-toughened sample has good transparency for LED, but it has negative effects on Tg and CTE. This research provides essential theoretical and experimental data to support the development of high-performance UV-LED-cured epoxy encapsulation materials.

Don't touch me! Mating not always preceded by courtship behavior in predatory coccinellids.

Knowledge of the ecology and behavior of biological control agents is essential for their effective use in biocontrol and Integrated Pest Management (IPM)programs. There is a lack of information regarding the courtship and mating traits that are crucial for successful mating in biocontrol population of coccinellids. To expand our current understanding in this area, 2 coccinellid species commonly used for the biocontrol of soft-bodied insects, Cryptolaemus montrouzieri (Mulsant) and Propylea quatuordecimpunctata (Linnaeus) (Coleoptera: Coccinellidae), were studied. A quantitative analysis was performed to compare their courtship and mating displays. Key behavioral traits of the precopulatory, copulatory, and postcopulatory phases were investigated. The courtship and mating sequences of C. montrouzieri and P. quatuordecimpunctata were similar to each other. During the mating phase, the key displays were the opening of the elytrae and wings followed by body shaking in C. montrouzieri and leg tapping followed by body shaking in P. quatuordecimpunctata. The mating success of both species was not correlated with any courtship display, such as antennal tapping. Only in C. montrouzieri mounting attempt showed a higher frequency from the backside without affecting male mating success. This research adds baseline knowledge about the courtship and mating behavior of the biocontrol population of coccinellids, contributing to the identification of potentiallyuseful benchmarks (e.g., body shaking, leg tapping) for monitoring prolonged mass-rearing processes, thusreducing mating failures.

Formal contracts mitigate social dilemmas in multi-agent reinforcement learning

Multi-agent Reinforcement Learning (MARL) is a powerful tool for training autonomous agents acting independently in a common environment. However, it can lead to sub-optimal behavior when individual incentives and group incentives diverge. Humans are remarkably capable at solving these social dilemmas. It is an open problem in MARL to replicate such cooperative behaviors in selfish agents. In this work, we draw upon the idea of formal contracting from economics to overcome diverging incentives between agents in MARL. We propose an augmentation to a Markov game where agents voluntarily agree to binding transfers of reward, under pre-specified conditions. Our contributions are theoretical and empirical. First, we show that this augmentation makes all subgame-perfect equilibria of all Fully Observable Markov Games exhibit socially optimal behavior, given a sufficiently rich space of contracts. Next, we show that for general contract spaces, and even under partial observability, richer contract spaces lead to higher welfare. Hence, contract space design solves an exploration-exploitation tradeoff, sidestepping incentive issues. We complement our theoretical analysis with experiments. Issues of exploration in the contracting augmentation are mitigated using a training methodology inspired by multi-objective reinforcement learning: Multi-Objective Contract Augmentation Learning. We test our methodology in static, single-move games, as well as dynamic domains that simulate traffic, pollution management, and common pool resource management.

Risks from Language Models for Automated Mental Healthcare: Ethics and Structure for Implementation (Extended Abstract)

In the United States and other countries exists a “national mental health crisis”: Rates of suicide, depression, anxiety, substance use, and more continue to increase – exacerbated by isolation, the COVID pandemic, and, most importantly, lack of access to mental healthcare. Therefore, many are looking to AI-enabled digital mental health tools, which have the potential to reach many patients who would otherwise remain on wait lists or without care. The main drive behind these new tools is the focus on large language models that could enable real-time, personalized support and advice for patients. With a trend towards language models entering the mental healthcare delivery apparatus, questions arise about how a robust, high-level framework to guide ethical implementations would look like and whether existing language models are ready for this high-stakes application where individual failures can lead to dire consequences. This paper addresses the ethical and practical challenges custom to mental health applications and proposes a structured framework that delineates levels of autonomy, outlines ethical requirements, and defines beneficial default behaviors for AI agents in the context of mental health support. We also evaluate fourteen state-of-the-art language models (ten off-the-shelf, four fine-tuned) using 16 mental health-related questions designed to reflect various mental health conditions, such as psychosis, mania, depression, suicidal thoughts, and homicidal tendencies. The question design and response evaluations were conducted by mental health clinicians (M.D.s) with defined rubrics and criteria for each question that would define "safe," "unsafe," and "borderline" (between safe and unsafe) for reproducibility. We find that all tested language models are insufficient to match the standard provided by human professionals who can navigate nuances and appreciate context. This is due to a range of issues, including overly cautious or sycophantic responses and the absence of necessary safeguards. Alarmingly, we find that most of the tested models could cause harm if accessed in mental health emergencies, failing to protect users and potentially exacerbating existing symptoms. We explore solutions to enhance the safety of current models based on system prompt engineering and model-generated self-critiques. Before the release of increasingly task-autonomous AI systems in mental health, it is crucial to ensure that these models can reliably detect and manage symptoms of common psychiatric disorders to prevent harm to users. This involves aligning with the ethical framework and default behaviors outlined in our study. We contend that model developers are responsible for refining their systems per these guidelines to safeguard against the risks posed by current AI technologies to user mental health and safety. Our code and the redacted data set are available on Github (github.com/maxlampe/taimh_eval, MIT License). The full, unredacted data set is available upon request due to the harmful content contained.

PPS: Personalized Policy Summarization for Explaining Sequential Behavior of Autonomous Agents

AI-enabled agents designed to assist humans are gaining traction in a variety of domains such as healthcare and disaster response. It is evident that, as we move forward, these agents will play increasingly vital roles in our lives. To realize this future successfully and mitigate its unintended consequences, it is imperative that humans have a clear understanding of the agents that they work with. Policy summarization methods help facilitate this understanding by showcasing key examples of agent behaviors to their human users. Yet, existing methods produce “one-size-fits-all” summaries for a generic audience ahead of time. Drawing inspiration from research in pedagogy, we posit that personalized policy summaries can more effectively enhance user understanding. To evaluate this hypothesis, this paper presents and benchmarks a novel technique: Personalized Policy Summarization (PPS). PPS discerns a user’s mental model of the agent through a series of algorithmically generated questions and crafts customized policy summaries to enhance user understanding. Unlike existing methods, PPS actively engages with users to gauge their comprehension of the agent behavior, subsequently generating tailored explanations on the fly. Through a combination of numerical and human subject experiments, we confirm the utility of this personalized approach to explainable AI.

Digital transition, data-and-tasks crowd-based economy, and the shared social progress: Unveiling a new political economy from a European perspective

The current unequal distribution of the benefits of digital transition and data-and-tasks crowd-based economy is directly linked to an incomplete interpretation of the behaviour of economic agents and their effects. This article proposes a new economic vision of digitalisation based on overcoming the postulates of neoclassical (automation-oriented and implemented mainly in the US) and autocratic (control-oriented and implemented mainly in China) economics. It suggests recovering the evolutionary, social, institutional, ethical and humanistic precepts of political economy. Following this more plural and European conceptual orientation, the analysis shows that current inequality arises from a digital R&D&I process plagued by network, platform, bias and polarisation effects, from business models that foster automation, and from the emergence of large superstar firms or control organisations with excessive market and political power. The redirection of technological progress towards more democratic individual values and shared social welfare requires digital governance based on new incentives and taxes, a greater strategic and political orientation towards social sustainability, and a new transformative role for the State.

Large language models for whole-learner support: opportunities and challenges.

In recent years, large language models (LLMs) have seen rapid advancement and adoption, and are increasingly being used in educational contexts. In this perspective article, we explore the open challenge of leveraging LLMs to create personalized learning environments that support the "whole learner" by modeling and adapting to both cognitive and non-cognitive characteristics. We identify three key challenges toward this vision: (1) improving the interpretability of LLMs' representations of whole learners, (2) implementing adaptive technologies that can leverage such representations to provide tailored pedagogical support, and (3) authoring and evaluating LLM-based educational agents. For interpretability, we discuss approaches for explaining LLM behaviors in terms of their internal representations of learners; for adaptation, we examine how LLMs can be used to provide context-aware feedback and scaffold non-cognitive skills through natural language interactions; and for authoring, we highlight the opportunities and challenges involved in using natural language instructions to specify behaviors of educational agents. Addressing these challenges will enable personalized AI tutors that can enhance learning by accounting for each student's unique background, abilities, motivations, and socioemotional needs.

A multi-theoretical approach to supply chain management

The article examines the supply chain as a modeling object. The multi-criteria and contradictory goals in supply chain management are: on the one hand, the maximum level of service, on the other hand, minimizing logistics costs. Solving strategic optimization problems for this class of objects using classical optimization methods is difficult. The theoretical and methodological basis of the supply chain was considered from the perspective of a set-theoretic approach. The purpose of the study is to analyze the behavior of many different agents (suppliers, clients, etc.) with different needs and goals when making decisions. An agent is an active element of the system, possessing a certain autonomy and the ability to make independent decisions, relying on the information it has about the state of the environment and the actions of other agents. In turn, supply chains are a complex organizational (active) system, i.e. associated with human participation. The study of behavioral aspects and interorganizational interaction, the formation of new organizational forms are an extremely important aspect in the management and modeling of supply chains, a factor in the dynamic behavior of the logistics system. The conducted research represents an important contribution to the development of knowledge about the application of the set-theoretic approach in supply chain management. It demonstrates the applicability of this approach in practice through an analysis of a specific company, LLP «Knauf», on the Kazakhstan market.