38 citations · 94 across the 24 of their papers we have counts for
25 papers
Not What You Meant: Can LLMs Follow a Specified Negation Semantics?
Qiming Bao, Agnieszka Mensfelt, Michael J. Witbrock +1
Negation does not carry a uniform interpretation across domains. In legal, regulatory, and medical reasoning, the intended interpretation depends on the reading in force -- open- v…
PrologMCP: A Standardized Prolog Tool Interface for LLM Agents
Agnieszka Mensfelt, Adarsh Prabhakaran, Adrian Haret +2
Frontier reasoning-tuned language models still fail on deductive tasks at depth, and the cost of improved performance through extended internal reasoning scales poorly. Symbolic de…
Towards a Common Framework for Autoformalization
Agnieszka Mensfelt, David Tena Cucala, Santiago Franco +3
Autoformalization has emerged as a term referring to the automation of formalization - specifically, the formalization of mathematics using interactive theorem provers (proof assis…
A Survey on Hypergame Theory: Modelling Misaligned Perceptions and Nested Beliefs for Multi-Agent Systems
Vince Trencsenyi, Agnieszka Mensfelt, Kostas Stathis
Classical game-theoretic models typically assume rational agents, complete information, and common knowledge of payoffs - assumptions that are often violated in real-world MAS char…
The Influence of Human-inspired Agentic Sophistication in LLM-driven Strategic Reasoners
Vince Trencsenyi, Agnieszka Mensfelt, Kostas Stathis
The rapid rise of large language models (LLMs) has shifted artificial intelligence (AI) research toward agentic systems, motivating the use of weaker and more flexible notions of a…
Approximating Human Strategic Reasoning with LLM-Enhanced Recursive Reasoners Leveraging Multi-agent Hypergames
Vince Trencsenyi, Agnieszka Mensfelt, Kostas Stathis
LLM-driven multi-agent-based simulations have been gaining traction with applications in game-theoretic and social simulations. While most implementations seek to exploit or evalua…
Generative Agents for Multi-Agent Autoformalization of Interaction Scenarios
Agnieszka Mensfelt, Kostas Stathis, Vince Trencsenyi
Multi-agent simulations are versatile tools for exploring interactions among natural and artificial agents, but their development typically demands domain expertise and manual effo…
Proceedings of the First International Workshop on Next-Generation Language Models for Knowledge Representation and Reasoning (NeLaMKRR 2024)
Ken Satoh, Ha-Thanh Nguyen, Francesca Toni +2
Reasoning is an essential component of human intelligence as it plays a fundamental role in our ability to think critically, support responsible decisions, and solve challenging pr…
Autoformalization of Game Descriptions using Large Language Models
Agnieszka Mensfelt, Kostas Stathis, Vince Trencsenyi
Game theory is a powerful framework for reasoning about strategic interactions, with applications in domains ranging from day-to-day life to international politics. However, applyi…
Towards Logically Sound Natural Language Reasoning with Logic-Enhanced Language Model Agents
Agnieszka Mensfelt, Kostas Stathis, Vince Trencsenyi
Large language models (LLMs) are increasingly explored as general-purpose reasoners, particularly in agentic contexts. However, their outputs remain prone to mathematical and logic…
Towards Explainable Strategy Templates using NLP Transformers
Pallavi Bagga, Kostas Stathis
This paper bridges the gap between mathematical heuristic strategies learned from Deep Reinforcement Learning (DRL) in automated agent negotiation, and comprehensible, natural lang…
Black-Box Analysis: GPTs Across Time in Legal Textual Entailment Task
Ha-Thanh Nguyen, Randy Goebel, Francesca Toni +2
The evolution of Generative Pre-trained Transformer (GPT) models has led to significant advancements in various natural language processing applications, particularly in legal text…
A negation detection assessment of GPTs: analysis with the xNot360 dataset
Ha Thanh Nguyen, Randy Goebel, Francesca Toni +2
Negation is a fundamental aspect of natural language, playing a critical role in communication and comprehension. Our study assesses the negation detection performance of Generativ…
Beyond Logic Programming for Legal Reasoning
Ha-Thanh Nguyen, Francesca Toni, Kostas Stathis +1
Logic programming has long being advocated for legal reasoning, and several approaches have been put forward relying upon explicit representation of the law in logic programming te…
How well do SOTA legal reasoning models support abductive reasoning?
Ha-Thanh Nguyen, Randy Goebel, Francesca Toni +2
We examine how well the state-of-the-art (SOTA) models used in legal reasoning support abductive reasoning tasks. Abductive reasoning is a form of logical inference in which a hypo…
World of Bugs: A Platform for Automated Bug Detection in 3D Video Games
Benedict Wilkins, Kostas Stathis
We present World of Bugs (WOB), an open platform that aims to support Automated Bug Detection (ABD) research in video games. We discuss some open problems in ABD and how they relat…
Learning to Identify Perceptual Bugs in 3D Video Games
Benedict Wilkins, Kostas Stathis
Automated Bug Detection (ABD) in video games is composed of two distinct but complementary problems: automated game exploration and bug identification. Automated game exploration h…
Deep Learnable Strategy Templates for Multi-Issue Bilateral Negotiation
Pallavi Bagga, Nicola Paoletti, Kostas Stathis
We study how to exploit the notion of strategy templates to learn strategies for multi-issue bilateral negotiation. Each strategy template consists of a set of interpretable parame…
Learnable Strategies for Bilateral Agent Negotiation over Multiple Issues
Pallavi Bagga, Nicola Paoletti, Kostas Stathis
We present a novel bilateral negotiation model that allows a self-interested agent to learn how to negotiate over multiple issues in the presence of user preference uncertainty. Th…
A Metric Learning Approach to Anomaly Detection in Video Games
Benedict Wilkins, Chris Watkins, Kostas Stathis
With the aim of designing automated tools that assist in the video game quality assurance process, we frame the problem of identifying bugs in video games as an anomaly detection (…
A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation
Pallavi Bagga, Nicola Paoletti, Bedour Alrayes +1
We present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets. The agent uses an act…
Probabilistic Programming with Gaussian Process Memoization
Ulrich Schaechtle, Ben Zinberg, Alexey Radul +2
Gaussian Processes (GPs) are widely used tools in statistics, machine learning, robotics, computer vision, and scientific computation. However, despite their popularity, they can b…
Computational Logic Foundations of KGP Agents
Antonis Kakas, Paolo Mancarella, Fariba Sadri +2
This paper presents the computational logic foundations of a model of agency called the KGP (Knowledge, Goals and Plan model. This model allows the specification of heterogeneous a…
CLIC: A Framework for Distributed, On-Demand, Human-Machine Cognitive Systems
N. Mavridis, S. Konstantopoulos, I. Vetsikas +6
Traditional Artificial Cognitive Systems (for example, intelligent robots) share a number of limitations. First, they are usually made up only of machine components; humans are onl…