21 papers · ranked by Valyu relevance
Iqbal H. Sarker
Artificial intelligence (AI) is a leading technology of the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR), with the capability of incorporating human behavior and intelligence into machines or systems. Thus, AI-based modeling is the key to build automated, intelligent, and smart systems…
Authors not listed
Step-by-step thinking is essential in all domains of chemical sciences and engineering. While machine learning tools are broadly used, algorithms that automate reasoning are far less common. We elaborate on seven categories of human reasoning activities and connect each to applications in chemical science and…
Qiguang Chen, Jinhao Liu, Qin Li, Yimeng Zhang + 17 more
Understanding how information is dynamically accumulated and transformed in human reasoning has long challenged cognitive psychology, philosophy, and artificial intelligence. Existing accounts, from classical logic to probabilistic models, illuminate aspects of output or individual modelling, but do not offer a…
Ana-Maria Istrate, Fausto Milletari, Fabrizio Castrotorres, Jakub M. Tomczak + 3 more
Reasoning Models are typically trained against verification mechanisms in formally specified systems such as code or symbolic math. However, in open domains like biology, we do not generally have access to exact rules facilitating formal verification at scale, and oftentimes resolve to testing hypotheses in the lab to…
Manish Bhatt
This paper first restates T 1 and T U with surgical precision, then extends T U to a richer T ∗ U that incorporates causal modelling (Pearl , 2009), metacognition (Flavell , 1979), the fast/slow thinking dichotomy (Kahneman , 2011), and the (currently untestable) question of phenomenal awareness (Baars , 1988).
Authors not listed
Nuclear Magnetic Resonance (NMR) structure determination is an important problem in education, industry, and research. Solving NMR spectra requires expert knowledge, critical thinking, and careful evaluation of multiple features of spectral data. This study explores the capabilities of large language models (LLMs) for…
P. N. Johnson-Laird, Marco Ragni
Everyone reasons about possibilities. This article explains how they could do so using mental models. The theory makes four major claims: 1. Correct inferences are necessary, referring only to facts or possibilities to which the premises refer and not ruling any of them out, for example: She left or hid; Therefore…
Sangeet S. Khemlani, Aron K. Barbey, Philip N. Johnson-Laird
This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to…
Henry Markovits
The question of whether reasoning can, or should, be described by a single normative model is an important one. In the following, I combine epistemological considerations taken from Piaget’s notion of genetic epistemology, a hypothesis about the role of reasoning in communication and developmental data to argue that…
Authors not listed
The scarcity and expense of fatigue data limits optimal design of components and constrains companies to a few well qualified materials when safety-critical applications are concerned. This research investigates different strategies to improve extraction of structured information from unstructured scientific…
Philippe Desjardins-Proulx, Timothée Poisot, Dominique Gravel
Artificial Intelligence presents an important paradigm shift for science. Science is traditionally founded on theories and models, most often formalized with mathematical formulas handcrafted by theoretical scientists and refined through experiments. Machine learning, an important branch of modern Artificial…
Tianfan Jin, Brett M Savoie
Contemporary machine learning algorithms have largely succeeded in automating the development of mathematical models from data. Although this is a striking accomplishment, it leaves unaddressed the multitude of scenarios, especially across the chemical sciences and engineering, where deductive, rather than inductive…
Marcel van Gerven
New developments in AI and neuroscience are revitalizing the quest to understanding natural intelligence, offering insight about how to equip machines with human-like capabilities. This paper reviews some of the computational principles relevant for understanding natural intelligence and, ultimately, achieving strong…
Leonidas A. A. Doumas, Guillermo Puebla, Andrea E. Martin
How a system represents information tightly constrains the kinds of problems it can solve. Humans routinely solve problems that appear to require structured representations of stimulus properties and relations. Answering the question of how we acquire these representations has central importance in an account of human…
Vinod Goel
We consider ourselves to be rational beings. We feel that our choices, decisions, and actions are selected from a flexible array of possibilities, based upon reasons. When we vote for a political candidate, it is because they share our views on certain critical issues. When we hire an individual for a job, it is…
Andrea Galassi, Kristian Kersting, Marco Lippi, Xiaoting Shao + 1 more
Deep learning is bringing remarkable contributions to the field of argumentation mining, but the existing approaches still need to fill the gap toward performing advanced reasoning tasks. In this position paper, we posit that neural-symbolic and statistical relational learning could play a crucial role in the…
Evgenii E. Vityaev, Andrei Mantsivoda
Cognitive imagination is a type of imagination that plays a key role in human thinking. It is not a "picture-in-the-head" imagination. It is a faculty to mentally visualize coherent and holistic systems of concepts and causal links that serve as semantic contexts for reasoning, decision making and prediction. Our…
Stylianos Loukas Vasileiou, William Yeoh, Alessandro Previti, Tran Cao Son
Probabilistic Scenarios Authors: ['Stylianos Loukas Vasileiou' 'William Yeoh' 'Alessandro Previti' 'Tran Cao Son'] Explanation generation frameworks aim to make AI systems' decisions transparent and understandable to human users. However, generating explanations in uncertain environments characterized by incomplete…
James P. Delgrande, Birte Glimm, Thomas Meyer, Mirosław Truszczyński + 1 more
'Frank Wolter'] Knowledge Representation and Reasoning is a central, longstanding, and active area of Artificial Intelligence. Over the years it has evolved significantly; more recently it has been challenged and complemented by research in areas such as machine learning and reasoning under uncertainty. In July 2022 a…
Tatsuji Takahashi, Kuratomo Oyo, Akihiro Tamatsukuri, Kohki Higuchi
We view observational causal induction as a statistical independence test under rarity assumption. This paper complements the two-stage theory of causal induction proposed by 20 with a computational analysis. We show that their dual-factor heuristic (DFH) model has a rational account as the square root of the index of…
Eric Neufeld, David Poole
The multiple extension problem frequently arises in both diagnostic and default reasoning. That is, in many settings it is possible to use any of a number of sets of instances defaults or hypotheses to explain (expected) observations. In some cases, we choose among explanations by making inferences about information…