Forecasting Future Behavior: Agents in Board Game Strategy

Purpose This paper aims to investigate the effect of artificial intelligence (AI) agent on consumers’ lying behavior. It provides novel insights into the pivotal role of lie type (material egoistic lie versus social egoistic lie) in shaping consumers’ lying behavior toward the AI agent (versus human), and the mediating roles of moral disengagement and perceived emotional ability for each type of lie. Design/methodology/approach Seven studies test the proposed theoretical framework. Based on the results of a pilot study that established the classification of lie (material egoistic lie versus social egoistic lie), six experimental studies using different designs (including an incentive-compatible study) were conducted to test the hypotheses. Study 1 (1a and 1b) and Study 2 (2a and 2b) tested the direct effect of the AI agent (versus human) on individuals’ propensity to tell material egoistic and social egoistic lie, respectively, and the mediating roles of moral disengagement and perceived emotional ability. Study 3 (3a and 3b) further enhanced the robustness and generalizability of the key findings. Findings Consumers are more likely to tell material egoistic lies in the face of the AI agent (versus human) because of increased moral disengagement. By contrast, they are less likely to tell social egoistic lies to the AI agent (versus human) due to the reduced perception of emotional ability of the AI agent. Potential alternative explanations are tested and ruled out. Research limitations/implications Consumers’ lying behavior toward the AI agent − an increasingly ubiquitous phenomenon in the marketplace − is not yet well understood. This research extends prior literature by proposing an integrated framework for understanding the impact of the AI agent (versus human) on consumer lying behavior and by highlighting the crucial moderating role of the type of lie (material egoistic lie versus social egoistic lie). Practical implications This study provides practical insights into effective implementation of AI agents to mitigate consumer lying. The results suggest that consumers are more likely to tell material egoistic lies to AI than to human. To mitigate such risks, firms should ensure that human employees are present in material-reward-related contexts, such as loan services, insurance claims and warranty claims. The results also reveal that, in contrast, consumers are less likely to tell social egoistic lies to AI than to human. Therefore, the deployment of AI agents should be encouraged in social-reward-related contexts, such as health-care diagnosis, performance assessment and the collection of private consumer data. The findings also inform the design of AI with respect to its intended purposes. Originality/value This research sheds light on the impact of the AI agent on consumer dishonesty. Specifically, this research contributes to the emerging literature on consumer lying, human–AI interaction and consumer self-control, and provides practical insights for more effective deployment of AI agents in the marketplace.

With the proliferation of computing technologies and an ongoing trend of introducing digital and blended learning aspects into higher education, innovative approaches to teaching complex topics like artificial intelligence (AI) have emerged. Of particular interest is the use of game-based learning approaches. According to problem-based learning theory, providing students with an interactive problem and encouraging them to independently find solutions promotes deeper understanding and skill acquisition. Thus, game-based approaches offer an engaging way for students to explore challenging concepts. However, despite the growing use of game-based methods in fields like economics, their application in computer science - especially in teaching game AI - remains under-explored.Understanding AI is increasingly critical for game development, as modern games emulate human-like behaviour in areas such as decision-making, character routines, and adaptive strategies. While many mechanisms and approaches of agent-level decision-making and planning are well understood, their application in video games poses unique challenges, such as accommodating unpredictable player interactions and ensuring performance efficiency without degrading the player experience. While strategy games like StarCraft or multiplayer online battle arenas like DotA 2 have been of interest as proving grounds for advanced AI training methods, their use in education has been limited due to their high complexity and associated learning curve.This work proposes the development of a novel interactive application to fill this niche. Taking inspiration from city building and management games, the application simulates the campus of the University of Applied Sciences Mittweida, where students are given control of agents acting as archetypal roles of students. The agents' goal is the acquisition of knowledge, an abstract resource gained by participating in courses, requiring the agent to navigate the campus. Students interact with the system through an API that provides information on the state of the simulation and allows issuing commands to specific agents. For example, agents who continuously acquire knowledge over a prolonged period do so at decreasing efficiency. To remedy this, a student implements a routine checking the learning efficiency of all agents, commanding "tired" agents to take a break. Alternatively, the student could train a machine learning algorithm to do the same task, albeit more adaptive. Additionally, the application enables dynamic changes to the environment at runtime, such as adding or removing courses or buildings, simulating player-driven alterations to the game world. By designing decision-making algorithms for these agents, students gain hands-on experience with fundamental AI concepts, i.e. decision trees, bridging the gap between theoretical knowledge and practical application.To evaluate the effectiveness of the application, a comparative study with undergraduate students is planned. Over the course of two semesters, two groups of students will be taught the basics of game AI - one using traditional teaching methods (primarily lectures), the other using a game-based method incorporating the new application. The learning progress of both groups will be monitored using assignments, with students being given a sample project and tasked to develop a game AI solution, i.e. for a non-player character in a first-person shooter.

Recent research has shown that consumers tend to behave more unethically when encountering artificial intelligence (AI) agents than with human agents. Nevertheless, few studies have explored the differential impact of AI agents on unethical consumer behavior. From the perspective of the power relationship between AI and consumers, we classify the role of an AI agent as that of a “servant” or “partner.” Across one field study and four scenario-based experiments (offline and online), we reveal that consumers are more likely to engage in unethical behavior when encountering servant AI agents than partner AI agents due to increased anticipatory moral disengagement. We also identify the boundary conditions for the moral disengagement effect of AI agents, finding that this effect is attenuated (a) among consumers with high moral identity, (b) with human-like AI agents, and (c) in the context of high behavioral visibility. This research provides new insight into the AI morality literature and has practical implications for service agencies using AI agents.

Purpose Artificial intelligence (AI) agents and agentic systems have the potential to transform the tourism and hospitality (T&H) industry by automating existing processes and improving operational efficiency. This viewpoint paper aims to explore and analyze the potential opportunities and challenges, and establish a comprehensive research agenda for developing AI agents and agentic systems within the T&H industry. Design/methodology/approach This article comprehensively analyzes current literature, user sentiment analysis and market trends assessment on AI agents and agentic systems. It identifies critical areas where these technologies can transform the T&H industries, providing valuable insights into this emerging field and its potential for driving innovation and efficiency. Findings Sentiment analysis reveals that 65% of social media users positively view OpenAI’s new AI agent, praising its automation capabilities. Meanwhile, 22% raise concerns about technical flaws, accessibility and ethics, while 13% remain neutral. AI agents offer opportunities to enhance efficiency, personalize services and support sustainability in T&H. However, challenges persist in development and implementation, with concerns from businesses, customers and regulators. This article highlights both the opportunities and challenges AI agents present in T&H industries. Research limitations/implications This paper provides valuable insights for stakeholders in the T&H industry, considering the adoption of AI agents. The authors present an overview of the potential challenges involved in adopting this emerging technology and discuss key organizational barriers. Originality/value This study examines the application of AI agents and agentic systems in T&H, highlighting opportunities and challenges may face during their adoption. The paper contributes original insights into how these systems can reshape industry practices, providing a foundation for future research and practical applications.

An Artificial Intelligence (AI) agent is a software entity that autonomously performs tasks or makes decisions based on pre-defined objectives and data inputs. AI agents, capable of perceiving user inputs, reasoning and planning tasks, and executing actions, have seen remarkable advancements in algorithm development and task performance. However, the security challenges they pose remain under-explored and unresolved. This survey delves into the emerging security threats faced by AI agents, categorizing them into four critical knowledge gaps: unpredictability of multi-step user inputs, complexity in internal executions, variability of operational environments, and interactions with untrusted external entities. By systematically reviewing these threats, this article highlights both the progress made and the existing limitations in safeguarding AI agents. The insights provided aim to inspire further research into addressing the security threats associated with AI agents, thereby fostering the development of more robust and secure AI agent applications.

The trust game, a simple two-player economic exchange, is extensively used as an experimental measure for trust and trustworthiness of individuals. We construct deep neural network–based artificial intelligence (AI) agents to participate a series of experiments based upon the trust game. These artificial agents are trained by playing with one another repeatedly without any prior knowledge, assumption, or data regarding human behaviors. We find that, under certain conditions, AI agents produce actions that are qualitatively similar to decisions of human subjects reported in the trust game literature. Factors that influence the emergence and levels of cooperation by artificial agents in the game are further explored. This study offers evidence that AI agents can develop trusting and cooperative behaviors purely from an interactive trial-and-error learning process. It constitutes a first step to build multiagent-based decision support systems in which interacting artificial agents are capable of leveraging social intelligence to achieve better outcomes collectively. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: Y. (D.) Wu extends her gratitude for the financial support provided through the RSCA Seed [Grant 22-RSG-01-004] from the San Jose State University. Supplemental Material: Data are available at https://doi.org/10.1287/mnsc.2023.4782 .

What happens in industries where firms delegate strategy choices to private artificial intelligence (AI) agents? Would markets spiral into hypercompetition or settle into a comfortable status quo? We develop a formal model in which AI agents consider large business-model catalogs, predict performance, select, and learn from realized outcomes. Our representation accommodates existing AI paradigms, allowing for substantial increases in scale and computational capacity. We show that market dynamics converge to a self-confirming equilibrium; along the realized path, AI agents become well calibrated, and their choices become subjectively optimal—even though objectively superior business models may remain unexplored. This convergence can indeed sustain high profits. However, it also produces strategic lock-in; novel business-model implementations become rare long before catalogs are exhausted. This creates a distinct role for humans. A single episode of human-driven frame expansion—introducing a genuinely new business model to a catalog—can disrupt the AI-induced equilibrium and initiate strategic renewal. Yet, the ability to do so does not imply that it will be done. When the prevailing equilibrium is sufficiently lucrative, managers rationally refrain from triggering renewed learning. Our results clarify where humans still matter in AI-enabled strategy: deciding when to change the frame and not merely optimizing within it. History: Accepted for the Special Issue: Can AI Do Strategy?

With the advancements of artificial intelligence (AI), emerging scenarios involving close collaboration between AI and other unknown agents are becoming increasingly common. This requires sometimes training AI agents to collaborate with unknown agents in the absence of a reward function -- which may be unavailable to the AI agents or even undefined by the unknown agents themselves -- thus posing news challenges to existing learning algorithms that often require knowing the shared reward. In this paper, we show that effective teaming with unknown agents can be achieved in the absence of a reward function, through actively modeling other unknown agents and reasoning about their latent rewards from available interaction/observation history. In particular, we propose a novel framework that leverages a kernel density Bayesian inverse learning method for active reward/goal inference and prove that multi-agent reinforcement learning guided by the inferred reward signals can converge to an optimal policy teaming with unknown agents. The result enables us to develop an adaptive policy update strategy, through the use of a family of pre-trained, goal-conditioned policies, further eliminating the need for online retraining. The proposed solution is evaluated using a wide range of diverse unknown agents of latent and even non-stationary reward. Our solution significantly increases the teaming performance between AI and unknown agents in the absence of reward.

Artificial intelligence (AI) is increasingly discussed as an innovation enabler for the enhancement of circular economy (CE) approaches in industries. The further deployment of intelligent technologies is considered to be very promising particularly in remanufacturing, which can be regarded as an implementation approach of CE at a firm level. AI’s potential to contribute to advancements in remanufacturing can be traced back to these modern technologies’ extended capacities of supporting and assisting humans during rather manual processes which are regarded as more common in remanufacturing than in traditional linear production. As a result, we argue that in future application scenarios, humans are going to interact more often with AI agents who may direct and assist humans’ behaviour and decision-making processes. We assume that a better understanding of the specific dynamics and novel aspects of these kind of newly emerging human-AI systems is a key prerequisite for sustainable process innovation, particularly in remanufacturing organisations. However, empirical-based contributions about humans’ behavioural changes in interaction with AI agents have so far been rather rare and limited, especially in the field of remanufacturing and CE. In this article, we seek to contribute to this gap in research by exploring the interaction between shop floor workers and an AI agent based on a case study research approach at a plant of a German automotive supplier that is remanufacturing used parts. We conducted semi-structured interviews among the shop floor workers who are involved in a joint decision-making task with an AI agent. We interpret the findings of our qualitative data in the light of related research in the field of AI in CE, AI implementation in organisation and human-AI interaction literature. In summary, our analysis reveals 13 behavioural patterns that shop floor workers reported on referring to their interaction with the AI agent. The behavioural patterns are systemised into a cognitive, emotional and social dimension of a competence framework. These findings shall contribute to a more specific understanding about how humans interact with AI agents at work, while considering the specific context variables of the interaction paradigm and the AI agent’s role during joint decision-making in a human-AI system. Implications for literature in the field of human-AI interaction as well as AI implementation in organisations with a particular focus on CE are discussed.

This paper provides an in-depth examination of the emerging domain of no-code artificial intelligence (AI) agent development, with a specific emphasis on N8N, a robust open-source workflow automation platform. N8N allows users with little to no programming expertise to design and deploy intelligent, autonomous agents by visually constructing workflows that integrate AI models and external APIs. The study elaborates on N8N’s architectural framework, highlighting its core features and modular node-based design that simplifies AI agent creation. Through detailed analysis, the paper identifies key application areas for these AI agents, including marketing automation, helpdesk operations, data enrichment processes, and content generation tasks. Furthermore, it investigates how N8N facilitates the democratization of AI development by lowering technical barriers, accelerating deployment times, and enabling non-experts to harness AI capabilities effectively. Despite its advantages, the paper also addresses several challenges inherent in using no-code platforms like N8N. These include managing workflow complexity, latency issues, security vulnerabilities, and the difficulties encountered during debugging. The discussion extends to future prospects and improvements that can enhance the scalability and functionality of such platforms. By critically discussing both the transformative potential and the limitations of N8N, this research highlights the platform's role in widening access to AI technologies, promoting innovation, and driving efficiency across various industry sectors through adaptive, user-friendly automation tools.

Artificial Intelligence (AI) agents are revolutionizing industries by enabling autonomous decision-making, task execution, and multi-agent collaboration. This paper provides a comprehensive review of AI agent frameworks, focusing on their architectures, applications, and challenges in financial services. We conduct a comparative analysis of leading frameworks, including LangGraph, CrewAI, and AutoGen, evaluating their strengths, limitations, and suitability for complex financial tasks such as trading, risk assessment, and investment analysis. The integration of AI agents in financial markets presents both opportunities and challenges, particularly in terms of regulatory compliance, ethical considerations, and model robustness. We examine agentic AI design patterns, multi-agent systems, and the deployment of AI agents advancing the proposal to use them for fraud detection and risk management. By synthesizing insights from academic research and industry practices, this review identifies key trends and future directions in AI agent development. This work contributes to the growing discourse on AI-driven automation by outlining technical considerations and open challenges in deploying AI agents at scale. We highlight the need for enhanced transparency, interpretability, and security in AI-driven Agentic systems. Our findings provide valuable insights for researchers and practitioners seeking to harness AI agents for more efficient and intelligent decision-making.

Artificial Intelligence (AI) agents represent a significant paradigm shift, offering novel methodologies to enhance efficiency and productivity across functions and industries. This study introduces a novel architectural framework for AI agents, with a focus on leveraging Large Language Models (LLMs), such as OpenAI’s GPT-4, to create a foundation for advanced autonomous functionality. The architecture is designed to enhance the adaptability, efficiency, and intelligence of AI agents across various domains, with software development serving as a primary case study. The framework delineates critical components including the integration mechanism for LLMs, task execution protocols, continuous learning processes, and interaction models, aiming to facilitate the seamless incorporation of AI agents into complex environments. By applying this framework within a software development context, the research demonstrates significant improvements: a 50% reduction in debugging time, a 75% decrease in version control conflicts, and a 35% increase in coding standards compliance. These outcomes not only validate the framework’s effectiveness in real-world applications but also underscore its potential to revolutionize the capabilities of AI agents beyond software development. The proposed architecture promises to empower AI agents with a higher degree of autonomy and intelligence, making them invaluable assets in tackling diverse challenges. The implications of this work are vast, setting a new benchmark for AI agent design and deployment and opening avenues for future research and development in AI-driven innovations.

A multi-agent artificial intelligence (AI) model is developed to automate the discovery of new metallic alloys, integrating multimodal data and external knowledge, including insights from physics via atomistic simulations. The system consists of (a) large language models (LLMs) for tasks such as reasoning and planning, (b) AI agents with distinct roles collaborating dynamically, and (c) a newly developed graph neural network (GNN) model for rapid retrieval of physical properties. We chose the ternary NbMoTa body-centered-cubic alloy as our model system and developed the GNN to predict two fundamental materials properties: the Peierls barrier and the solute/screw dislocation interaction energy. Our GNN model efficiently predicts these properties, reducing reliance on costly brute-force calculations and alleviating the computational demands on the multi-agent system. By combining the predictive capabilities of GNNs with the collaborative intelligence of LLM-driven reasoning agents, the system autonomously explores vast alloy design spaces, identifies trends in atomic-scale properties, and predicts macroscale mechanical strength, as demonstrated by several computational experiments. This synergistic approach accelerates the discovery of advanced alloys and holds promise for broader applications in other complex systems, marking a step forward in automated materials discovery and design. Impact statement Traditional deep learning models, such as graph neural networks and convolutional neural networks, operate within the confines of their training data sets, making single-step inferences for regression or classification. Our work introduces a multi-agent strategy that transcends these limitations by integrating deep learning with reasoning and decision-making capabilities. This intelligent system actively interprets results, determines subsequent actions, and iteratively refines predictions, accelerating the materials design process. We demonstrate its effectiveness in exploring the vast compositional space of a ternary alloy, where the model dynamically solicits data, analyzes trends, generates visualizations, and derives insights into materials behavior. By enabling accurate predictions of key alloy characteristics, our approach advances the discovery of novel metallic systems and underscores the critical role of solid-solution alloying. More broadly, it represents a major step toward integrating artificial intelligence with scientific reasoning, moving closer to artificial general intelligence in engineering. This paradigm shift has profound implications for materials science, enabling more efficient, autonomous, and intelligent exploration of complex materials spaces. Graphical Abstract

With the increasing air traffic density and complexity in terminal sector, air traffic controllers will face more challenges and pressures to ensure the safe and efficient operation of air traffic. In this work, an artificial intelligence (AI) agent based on deep reinforcement learning is built to mimic air traffic controllers, such that the dense, complex and dynamic air traffic flows in terminal airspace can be handled sequentially and separated. To simplify the problem, the complex three-dimensional terminal sector is projected onto the vertical plane by dispersing state space and action space. And then, the typical reinforcement learning algorithm, double deep Q-network, is taken to realize the AI agent. Results show that the built AI agent can guide 6 aircrafts safely and efficiently through Sector 01 of Nanjing Terminal, simultaneously.

This experimental study aimed to evaluate the affective and cognitive engagement of users when interacting with a conversational artificial intelligence (AI) agent using a humanized voice—specifically, that of Brazilian congressman Rubens Pereira Jr. Twenty participants were divided into two groups: one familiar with the politician and another without prior familiarity. During five minutes of free interaction with the AI agent, neurophysiological data were collected via a portable EEG device (Brainlink Dual), recording signals across delta, theta, alpha, beta, and gamma bands. The findings reveal that the humanized voice produced a significant increase in high-beta and low-gamma activity—markers of cognitive engagement—especially among younger participants and those unfamiliar with the politician. Conversely, participants who recognized the voice exhibited greater theta and low-alpha activation, indicating higher affective engagement and memory recall. Gender differences were also observed: women showed higher affective activation, while men displayed greater cognitive focus. The results suggest that voice personalization in AI agents fosters emotional bonds and enhances attention, making it a promising strategy for public communication, political marketing, and personalized education. The study advances the field by integrating continuous EEG data with qualitative self-reports, offering a multidimensional perspective on human-AI interaction. In practical terms, humanizing AI agents with familiar and expressive voices can strengthen user trust, empathy, and engagement, including among older participants. These findings highlight the value of human-like voice strategies for more effective digital communication and citizen engagement.