Gerardo I. Simari’s research while affiliated with Universidad Nacional del Sur and other places

We consider behavior of agents in a long-term multiagent coopetitive setting in which agents vary their cooperative and competitive stances over time. Using the game of Diplomacy as a testbed, we study how successful agents vary their coopetitive behavior, developing a new “style of play” (SoP) characterization of player behavior. We assess five novel SoP hypotheses about successful behavior. We propose two algorithms to automatically compute an agent’s SoP vector and describe the important factors in this computation. As an agent’s SoP depends on the game state and its perception of threat, we develop a novel “means, motive, and opportunity” (MMO) model of threat and show that we can predict threats effectively using this model. We provide novel insights into how agents should behave to more successfully achieve their goals in long-term coopetitive settings.

Causal learning is the cognitive process of developing the capability of making causal inferences based on available information, often guided by normative principles. This process is prone to errors and biases, such as the illusion of causality, in which people perceive a causal relationship between two variables despite lacking supporting evidence. This cognitive bias has been proposed to underlie many societal problems, including social prejudice, stereotype formation, misinformation, and superstitious thinking. In this research, we investigate whether large language models (LLMs) develop causal illusions, both in real-world and controlled laboratory contexts of causal learning and inference. To this end, we built a dataset of over 2K samples including purely correlational cases, situations with null contingency, and cases where temporal information excludes the possibility of causality by placing the potential effect before the cause. We then prompted the models to make statements or answer causal questions to evaluate their tendencies to infer causation erroneously in these structured settings. Our findings show a strong presence of causal illusion bias in LLMs. Specifically, in open-ended generation tasks involving spurious correlations, the models displayed bias at levels comparable to, or even lower than, those observed in similar studies on human subjects. However, when faced with null-contingency scenarios or temporal cues that negate causal relationships, where it was required to respond on a 0-100 scale, the models exhibited significantly higher bias. These findings suggest that the models have not uniformly, consistently, or reliably internalized the normative principles essential for accurate causal learning.

In this paper, we study the problem of visual question answering (VQA) where the image and query are represented by ASP programs that lack domain data. We provide an approach that is orthogonal and complementary to existing knowledge augmentation techniques where we abduce domain relationships of image constructs from past examples. After framing the abduction problem, we provide a baseline approach, and an implementation that significantly improves the accuracy of query answering yet requires few examples.

The ability to generate artificial human movement patterns while meeting location and time constraints is an important problem in the security community, particularly as it enables the study of the analog problem of detecting such patterns while maintaining privacy. We frame this problem as an instance of abduction guided by a novel parsimony function represented as an aggregate truth value over an annotated logic program. This approach has the added benefit of affording explainability to an analyst user. By showing that any subset of such a program can provide a lower bound on this parsimony requirement, we are able to abduce movement trajectories efficiently through an informed (i.e., A*) search. We describe how our implementation was enhanced with the application of multiple techniques in order to be scaled and integrated with a cloud-based software stack that included bottom-up rule learning, geolocated knowledge graph retrieval/management, and interfaces with government systems for independently conducted government-run tests for which we provide results. We also report on our own experiments showing that we not only provide exact results but also scale to very large scenarios and provide realistic agent trajectories that can go undetected by machine learning anomaly detectors.

This work presents an overview of a research project focused on R&D for ap- proximate query answering in probabilistic structured argumentation based on DeLP [2]. The ultimate goal is to develop a suite of algorithms for tackling the computational cost of this task and selection criteria for choosing the best one based on the analysis of available information

Capturing the uncertain aspects in cyber threat analyses is an important part of a wide range of efforts, including diagnostics, threat evaluation, and preventing attacks. However, there has been insufficient research and development of modeling approaches that are able to correctly capture and handle such uncertainty. In this work, we present an application example of the DeLP3E framework-a formalism that extends structured argumentation based on logic programming-in the domain of cyber threat analysis; in particular, near real-time analyses such as incident response in enterprise networks. The DeLP3E framework provides a unique combination of dialectical reasoning, rule-based inference , and probabilistic modeling to not only offer suggested responses to given situations, but also to explain to the analyst why the system reaches its conclusions.