35 citations · 60 across the 27 of their papers we have counts for
27 papers
Training-Free Cultural Alignment of Large Language Models via Persona Disagreement
Huynh Trung Kiet, Dao Sy Duy Minh, Tuan Nguyen +5
Large language models increasingly mediate decisions that turn on moral judgement, yet a growing body of evidence shows that their implicit preferences are not culturally neutral.…
A Multi-Agent Framework for Medical AI: Leveraging Fine-Tuned GPT, LLaMA, and DeepSeek R1 for Evidence-Based and Bias-Aware Clinical Query Processing
Naeimeh Nourmohammadi, Md Meem Hossain, The Anh Han +2
Large language models (LLMs) show promise for healthcare question answering, but clinical use is limited by weak verification, insufficient evidence grounding, and unreliable confi…
When Numbers Start Talking: Implicit Numerical Coordination Among LLM-Based Agents
Alessio Buscemi, Daniele Proverbio, Alessandro Di Stefano +3
LLMs-based agents increasingly operate in multi-agent environments where strategic interaction and coordination are required. While existing work has largely focused on individual…
Social welfare optimisation in well-mixed and structured populations
Van An Nguyen, Vuong Khang Huynh, Ho Nam Duong +10
Research on promoting cooperation among autonomous, self-regarding agents has often focused on the bi-objective optimisation problem: minimising the total incentive cost while maxi…
Understanding LLM Agent Behaviours via Game Theory: Strategy Recognition, Biases and Multi-Agent Dynamics
Trung-Kiet Huynh, Duy-Minh Dao-Sy, Thanh-Bang Cao +13
As Large Language Models (LLMs) increasingly operate as autonomous decision-makers in interactive and multi-agent systems and human societies, understanding their strategic behavio…
Many-to-One Adversarial Consensus: Exposing Multi-Agent Collusion Risks in AI-Based Healthcare
Adeela Bashir, The Anh han, Zia Ush Shamszaman
The integration of large language models (LLMs) into healthcare IoT systems promises faster decisions and improved medical support. LLMs are also deployed as multi-agent teams to a…
Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning
Shubham Mishra, The Anh Han, Bruno Silvester Lopes +2
Antimicrobial resistance (AMR) poses a significant public health and economic challenge, increasing treatment costs and reducing antibiotic effectiveness. This study employs machin…
Can LLMs effectively provide game-theoretic-based scenarios for cybersecurity?
Daniele Proverbio, Alessio Buscemi, Alessandro Di Stefano +3
Game theory has long served as a foundational tool in cybersecurity to test, predict, and design strategic interactions between attackers and defenders. The recent advent of Large…
Strategic Communication and Language Bias in Multi-Agent LLM Coordination
Alessio Buscemi, Daniele Proverbio, Alessandro Di Stefano +3
Large Language Model (LLM)-based agents are increasingly deployed in multi-agent scenarios where coordination is crucial but not always assured. Research shows that the way strateg…
Co-evolutionary Dynamics of Attack and Defence in Cybersecurity
Adeela Bashir, Zia Ush Shamszaman, Zhao Song +1
In the evolving digital landscape, it is crucial to study the dynamics of cyberattacks and defences. This study uses an Evolutionary Game Theory (EGT) framework to investigate the…
KERAIA: An Adaptive and Explainable Framework for Dynamic Knowledge Representation and Reasoning
Stephen Richard Varey, Alessandro Di Stefano, The Anh Han
In this paper, we introduce KERAIA, a novel framework and software platform for symbolic knowledge engineering designed to address the persistent challenges of representing, reason…
Do LLMs trust AI regulation? Emerging behaviour of game-theoretic LLM agents
Alessio Buscemi, Daniele Proverbio, Paolo Bova +15
There is general agreement that fostering trust and cooperation within the AI development ecosystem is essential to promote the adoption of trustworthy AI systems. By embedding Lar…
FAIRGAME: a Framework for AI Agents Bias Recognition using Game Theory
Alessio Buscemi, Daniele Proverbio, Alessandro Di Stefano +3
Letting AI agents interact in multi-agent applications adds a layer of complexity to the interpretability and prediction of AI outcomes, with profound implications for their trustw…
Media and responsible AI governance: a game-theoretic and LLM analysis
Nataliya Balabanova, Adeela Bashir, Paolo Bova +15
This paper investigates the complex interplay between AI developers, regulators, users, and the media in fostering trustworthy AI systems. Using evolutionary game theory and large…
Benchmarking Classical, Deep, and Generative Models for Human Activity Recognition
Md Meem Hossain, The Anh Han, Safina Showkat Ara +1
Human Activity Recognition (HAR) has gained significant importance with the growing use of sensor-equipped devices and large datasets. This paper evaluates the performance of three…
Quantifying detection rates for dangerous capabilities: a theoretical model of dangerous capability evaluations
Paolo Bova, Alessandro Di Stefano, The Anh Han
We present a quantitative model for tracking dangerous AI capabilities over time. Our goal is to help the policy and research community visualise how dangerous capability testing c…
Trust AI Regulation? Discerning users are vital to build trust and effective AI regulation
Zainab Alalawi, Paolo Bova, Theodor Cimpeanu +8
There is general agreement that some form of regulation is necessary both for AI creators to be incentivised to develop trustworthy systems, and for users to actually trust those s…
Evolutionary game theory: the mathematics of evolution and collective behaviours
The Anh Han
This brief discusses evolutionary game theory as a powerful and unified mathematical tool to study evolution of collective behaviours. It summarises some of my recent research dire…
Discriminatory or Samaritan -- which AI is needed for humanity? An Evolutionary Game Theory Analysis of Hybrid Human-AI populations
Tim Booker, Manuel Miranda, Jesús A. Moreno López +6
As artificial intelligence (AI) systems are increasingly embedded in our lives, their presence leads to interactions that shape our behaviour, decision-making, and social interacti…
Social Diversity Reduces the Complexity and Cost of Fostering Fairness
Theodor Cimpeanu, Alessandro Di Stefano, Cedric Perret +1
Institutions and investors are constantly faced with the challenge of appropriately distributing endowments. No budget is limitless and optimising overall spending without sacrific…
Understanding Emergent Behaviours in Multi-Agent Systems with Evolutionary Game Theory
The Anh Han
The mechanisms of emergence and evolution of collective behaviours in dynamical Multi-Agent Systems (MAS) of multiple interacting agents, with diverse behavioral strategies in co-p…
Voluntary safety commitments provide an escape from over-regulation in AI development
The Anh Han, Tom Lenaerts, Francisco C. Santos +1
With the introduction of Artificial Intelligence (AI) and related technologies in our daily lives, fear and anxiety about their misuse as well as the hidden biases in their creatio…
Promoting Fair Proposers, Fair Responders or Both? Cost-Efficient Interference in the Spatial Ultimatum Game
Theodor Cimpeanu, Cedric Perret, The Anh Han
Institutions and investors face the constant challenge of making accurate decisions and predictions regarding how best they should distribute their endowments. The problem of achie…
When to (or not to) trust intelligent machines: Insights from an evolutionary game theory analysis of trust in repeated games
The Anh Han, Cedric Perret, Simon T. Powers
The actions of intelligent agents, such as chatbots, recommender systems, and virtual assistants are typically not fully transparent to the user. Consequently, using such an agent…