Abstract This paper investigates how large language models can be steered to act more strategically in text-based negotiation settings. Two prompt-based action space designs are compared, namely emotional tone prompts and explicit offer prompts, within a negotiation environment, and outcomes are compared in simulated dialogues. The results show that both approaches improve strategic outcomes compared to a baseline, with tone-based actions yielding higher agreement rates and offer-based actions providing more stable tradeoffs. These findings demonstrate how action space design influences agent behavior, providing insights for deployment of large language models in strategic negotiation scenarios to gain an advantage in, for example, online influence operations.

Abstract In adversarial contexts, success often hinges on understanding not just what the opponent knows, but what they believe and how they revise those beliefs. This study investigates how large language models can be made more resilient and strategically capable by modeling the opponent’s reasoning using Bayesian belief revision. By formalizing negotiations as Bayesian games of incomplete information, it is shown that models equipped with belief revision are better able to counter deceptive or willful-thinking adversaries. The findings underscore the role of second-order reasoning in adversarial settings, with implications for social manipulation in the context of, for example, online communication and intelligence gathering.

Recent experimental studies have explored how well adaptive honeypot allocation strategies defend against human adversaries. As the experimental subjects were drawn from an unknown, nondescript pool of subjects using Amazon Mechanical Turk, the relevance to defense against real-world adversaries is unclear. The present study reproduces the experiments with more relevant experimental subjects. The results suggest that the strategies considered are less effective against attackers from the current population. In particular, their ability to predict the next attack decreased steadily over time, that is, the human subjects from this population learned to attack less and less predictably.

This paper presents our work on adapting and extending natural language interface (NLI) to database technology to support the specification and violation reporting of business rules. The resulting system allows non-technical users to author and manage a rulebook in controlled natural language - serving as a single point of definition that can be compiled into SQL to generate violation reports. To achieve this we represent business rules in tuple calculus, handle negation in our query re-writing algorithms and add support for natural language reflexives (e.g. 'its', 'themselves', etc.). Our results show a large class of business rules can be captured with these extensions. Although our approach is general, we present it applied to compliance checking of regulations over a materiel capability development information system at the Swedish Defence Materiel Administration. At EDBT we will also demonstrate this work over a more generic package delivery domain. While there has been recent effort in pursuing Semantics for Business Vocabulary and Business Rules (SBVR) in the semantic web and description logic communities, to our knowledge ours is the first attempt to provide this capability for ER-modeled relational databases.