Othalia Larue’s research while affiliated with Wright State University and other places

Case augmentation is often desirable when applying case-based reasoning to real-world problems. Initially explored for explainability, counterfactuals were recently recommended as a strategy to augment data. In this work, we implement an existing approach for generating counterfactuals, propose one variant of the original approach, and pro-pose a third approach based on the literature on algorithmic recourse. We apply these three approaches to two datasets in military medical triage.To assess generalization, we also examine one of our approaches on three publicly available datasets. We compare the approaches based on the number of counterfactuals they produce, their resulting accuracy, over-lapping counterfactuals, and domain knowledge. Experimental results are encouraging for the proposed approaches and bring up opportunities for future research.

We present an approach to algorithmic decision-making that emulates key facets of human decision-making, particularly in scenarios marked by expert disagreement and ambiguity. Our system employs a case-based reasoning framework, integrating learned experiences, contextual factors, probabilistic reasoning, domain-specific knowledge, and the personal traits of decision-makers. A primary aim of the system is to articulate algorithmic decision-making as a human-comprehensible reasoning process, complete with justifications for selected actions.

Knowledge engineering is an important task for creating and maintaining a knowledge base for cognitive models. It involves acquiring, representing, and organizing knowledge in a form that computers can use to make decisions and solve problems. However, this process can be a bottleneck for designing and using cognitive models. Knowledge engineering is a time-consuming and resource-intensive task that requires subject matter experts to provide information about a domain. In addition, models can acquire knowledge but require significant mechanisms to structure that information in a structured format appropriate for general use. Given the knowledge engineering bottleneck, we propose a solution that relies on natural language processing to extract key entities, relationships, and attributes to automatically generate chunks encoded as triples or chunks from unstructured text. Once generated, the knowledge can be used to create or add to a knowledge base within cognitive architectures to reduce knowledge engineering and task-specific models.

We describe the current state of world-class memory competitions, including the methods used to prepare for and compete in memory competitions, based on the subjective report of World Memory Championship Grandmaster and co-author Nelson Dellis. We then explore the reported experiences through the lens of the Simulated, Situated, and Structurally coherent Qualia (S3Q) theory of consciousness, in order to propose a set of experiments to help further understand the boundaries of expert memory performance.

Computational metacognition represents a cognitive systems perspective on high-order reasoning in integrated artificial systems that seeks to leverage ideas from human metacognition and from metareasoning approaches in artificial intelligence. The key characteristic is to declaratively represent and then monitor traces of cognitive activity in an intelligent system in order to manage the performance of cognition itself. Improvements in cognition then lead to improvements in behavior and thus performance. We illustrate these concepts with an agent implementation in a cognitive architecture called MIDCA and show the value of metacognition in problem-solving. The results illustrate how computational metacognition improves performance by changing cognition through meta-level goal operations and learning.

Geopolitical forecasting is the process of generating judgments of probability for a wide variety of future geopolitical events, such as political elections, international conflict, disease outbreaks, and macro-economic indicators. Governmental policy-makers, private organizations, and individuals use forecasting to aid their strategic decision-making. For example, a government agency may forecast the likelihood of a disease outbreak; business leaders may forecast how the market will respond if they launch a new product; individuals may employ forecasting to aid their decisions about what career to choose or how to invest for retirement. Recent research in geopolitical forecasting showed that instruction, practice, and peer interaction made a big difference in forecasting accuracy. In this chapter, we review relevant literature from the areas of decision-making, psychology, and human–machine interaction and suggest how findings from these areas could contribute to improvements in forecasters’ performance. We also present data and insights gained from our experience as competitors in a government-funded forecasting tournament.

Recent studies in geopolitical forecasting have identified psychological variables that predict forecasting accuracy. We studied the effect of providing human forecasters with automated information search and task management support tools. Our research aimed to determine whether use of the support tools could explain additional variance in forecasting performance above and beyond psychological variables. We found that the provided tools encouraged participants to do more work (i.e., information search, communication, reflection , etc.), which in turn resulted in improved forecasting performance.

Current autonomous systems have the ability to adapt to environmental changes in real-time, but limited ability to engage in anticipatory thinking (AT) with the flexibility to generalize and consider hypothetical future situations. We argue that metacognitive processes are important for and provide supporting literature primarily from psychology. As an example, we present a metacognitive monitoring mechanism implemented in a cognitive model and discuss ways to extend the mechanism to allow for dynamic behavior and anticipatory thinking capabilities.

A proposal for a unified theory of learned trust implemented in a cognitive architecture is presented. The theory is instantiated as a computational cognitive model of learned trust that integrates several seemingly unrelated categories of findings from the literature on interpersonal and human-machine interactions and makes unintuitive predictions for future studies. The model relies on a combination of learning mechanisms to explain a variety of phenomena such as trust asymmetry, the higher impact of early trust breaches, the black-hat/white-hat effect, the correlation between trust and cognitive ability, and the higher resilience of interpersonal as compared to human-machine trust. In addition, the model predicts that trust decays in the absence of evidence of trustworthiness or untrustworthiness. The implications of the model for the advancement of the theory on trust are discussed. Specifically, this work suggests two more trust antecedents on the trustor's side: perceived trust necessity and cognitive ability to detect cues of trustworthiness.