We devise an algorithm to generate sets of propositions that objectively instantiate graphs that support coherence-driven inference. We then benchmark the ability of large language models (LLMs) to reconstruct coherence graphs from (a straightforward transformation of) propositions expressed in natural language, with promising results from a single prompt to models optimized for reasoning. Combining coherence-driven inference with consistency evaluations by neural models may advance the state of the art in machine cognition.

Explanatory inference is the creation and evaluation of hypotheses that provide explanations, and is sometimes known as abduction or abductive inference. Generative AI is a new set of artificial intelligence models based on novel algorithms for generating text, images, and sounds. This paper proposes a set of benchmarks for assessing the ability of AI programs to perform explanatory inference, and uses them to determine the extent to which ChatGPT, a leading generative AI model, is capable of making explanatory inferences. Tests on the benchmarks reveal that ChatGPT performs creative and evaluative inferences in many domains, although it is limited to verbal and visual modalities. Claims that ChatGPT and similar models are incapable of explanation, understanding, causal reasoning, meaning, and creativity are rebutted.

Regardless of training, most people who come in contact with the field of AI are at least partially motivated by the glimmer of hope that they will get a better understanding of the mind. This quest, of course, is a rich and complex one. It is easy to get mired in minutiae along the way, be they the optimization of an algorithm, the details of a mental model, or the intricacies of a logical argument. Thagard's book attempts to call us back to the larger picture and to draw in new devotees--and, in general, he succeeds. This book begins, "Cognitive science is the interdisciplinary study of mind and intelligence..." (p.

In AI, psychology, and education, a growing body of research supports the view that learning is a goal-directed process. Psychological experiments show that people with varying goals process information differently, studies in education show that goals have a strong effect on what students learn, and functional arguments in machine learning support the necessity of goalbased focusing of learner effort. At the Fourteenth Annual Conference of the Cognitive Science Society, a symposium brought together researchers in AI, psychology, and education to discuss goaldriven learning. This article presents the fundamental points illuminated at the symposium, placing them in the context of open questions and current research directions in goal-driven learning. Learning is a central area of study for researchers interested in human cognition as well as those interested in machine intelligence.

Of course, the book's authors, psychologist Keith Holyoak and philosopher Paul Thagard, have good reason for this discussion: to focus on the "analogy war" that went on for years in the upper echelons of the U.S. government. Politicians think by analogy all the time, and the fates of nations hang on their idiosyncratic analogical instincts, wise or not. Military leaders, too, are guided by precedents, and Holyoak and Thagard ironically note that generals often prepare for the war that they last fought. However, they also point out that one can select one's precedents in a deeper manner than that. In fact, they devote three pages to George Ball, undersecretary of state in the Johnson administration, "who history must now credit as the greatest American political analogist of his time" (p. To be sure, Ball saw the appeal of the Korea, Munich, and dominochain analogies, but in each, he also saw serious weaknesses; more important, he felt he saw deeper similarities to the situation the ...

EMPATHICA is a computer program under development to facilitate cognitive-affective mapping using visual representations. A cognitive-affective map is a concept graph that includes information about the positive and negative emotional values of what is represented. Potential applications include conflict resolution, literary analysis, cross-cultural understanding, ethical assessment, authoring systems, and cognitive modeling.

Understanding the mind is one of the great "holy grails" of twentieth-century research. Regardless of training, most people who come in contact with the field of AI are at least partially motivated by the glimmer of hope that they will get a better understanding of the mind. This quest, of course, is a rich and complex one. It is easy to get mired in minutiae along the way, be they the optimization of an algorithm, the details of a mental model, or the intricacies of a logical argument. Thagard's book attempts to call us back to the larger picture and to draw in new devotees -- and, in general, he succeeds.

In his model, requirements needs, it must be able to represent is done unintentionally; a problem for filling system knowledge solver attempting to solve a gaps also direct explanation generation what these needs are. Ram proposed problem simply stores a trace of its by guiding retrieval and revision representations that include processing without attention to its of explanations during case-based the desired knowledge (possibly partially future relevance. However, Ng's previously explanation construction (Leake specified) and the reason that mentioned studies show that 1992). In the context of analogical the knowledge is sought. Leake for a different class of task, learning mapping, Thagard pointed out that focused on the representation of the goals have a strong effect on the goals, semantic constraints, and syntactic knowledge required to resolve anomalies learning performance of human constraints all affect analogical (which depends on a vocabulary learners. A future question is to identify mapping (Holyoak and Thagard 1989) of anomaly characterization structures the limits of goal-driven processing and the retrieval of potential analogs to describe the information in human learners.