Automated Explanation Selection for Scientific Discovery

• URL: http://arxiv.org/abs/2407.17454v3
• Date: Tue, 6 Aug 2024 08:52:18 GMT
• Title: Automated Explanation Selection for Scientific Discovery
• Authors: Markus Iser,
• Abstract summary: We propose a cycle of scientific discovery that combines machine learning with automated reasoning for the generation and the selection of explanations. We present a taxonomy of explanation selection problems that draws on insights from sociology and cognitive science.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated reasoning is a key technology in the young but rapidly growing field of Explainable Artificial Intelligence (XAI). Explanability helps build trust in artificial intelligence systems beyond their mere predictive accuracy and robustness. In this paper, we propose a cycle of scientific discovery that combines machine learning with automated reasoning for the generation and the selection of explanations. We present a taxonomy of explanation selection problems that draws on insights from sociology and cognitive science. These selection criteria subsume existing notions and extend them with new properties.

Related papers

• Bio-inspired AI: Integrating Biological Complexity into Artificial Intelligence [0.0]
  The pursuit of creating artificial intelligence mirrors our longstanding fascination with understanding our own intelligence. Recent advances in AI hold promise, but singular approaches often fall short in capturing the essence of intelligence. This paper explores how fundamental principles from biological computation can guide the design of truly intelligent systems.
  arXiv  Detail & Related papers  (2024-11-22T02:55:39Z)
• SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning [0.0]
  A key challenge in artificial intelligence is the creation of systems capable of autonomously advancing scientific understanding. We present SciAgents, an approach that leverages three core concepts. The framework autonomously generates and refines research hypotheses, elucidating underlying mechanisms, design principles, and unexpected material properties. Our case studies demonstrate scalable capabilities to combine generative AI, ontological representations, and multi-agent modeling, harnessing a swarm of intelligence' similar to biological systems.
  arXiv  Detail & Related papers  (2024-09-09T12:25:10Z)
• Comprehensible Artificial Intelligence on Knowledge Graphs: A survey [2.3408308015481665]
  This survey gives a short history to Comprehensible Artificial Intelligence on Knowledge Graphs. We argue that the concept Explainable Artificial Intelligence is overloaded and overlapping with Interpretable Machine Learning. This leads to the introduction of a novel taxonomy for Comprehensible Artificial Intelligence on Knowledge Graphs.
  arXiv  Detail & Related papers  (2024-04-04T14:57:32Z)
• Brain-Inspired Machine Intelligence: A Survey of Neurobiologically-Plausible Credit Assignment [65.268245109828]
  We examine algorithms for conducting credit assignment in artificial neural networks that are inspired or motivated by neurobiology. We organize the ever-growing set of brain-inspired learning schemes into six general families and consider these in the context of backpropagation of errors. The results of this review are meant to encourage future developments in neuro-mimetic systems and their constituent learning processes.
  arXiv  Detail & Related papers  (2023-12-01T05:20:57Z)
• AI for Mathematics: A Cognitive Science Perspective [86.02346372284292]
  Mathematics is one of the most powerful conceptual systems developed and used by the human species. Rapid progress in AI, particularly propelled by advances in large language models (LLMs), has sparked renewed, widespread interest in building such systems.
  arXiv  Detail & Related papers  (2023-10-19T02:00:31Z)
• A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization [55.11642177631929]
  Large neural generative models are capable of synthesizing semantically rich passages of text or producing complex images. We discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition.
  arXiv  Detail & Related papers  (2023-10-14T23:28:48Z)
• Towards Human Cognition Level-based Experiment Design for Counterfactual Explanations (XAI) [68.8204255655161]
  The emphasis of XAI research appears to have turned to a more pragmatic explanation approach for better understanding. An extensive area where cognitive science research may substantially influence XAI advancements is evaluating user knowledge and feedback. We propose a framework to experiment with generating and evaluating the explanations on the grounds of different cognitive levels of understanding.
  arXiv  Detail & Related papers  (2022-10-31T19:20:22Z)
• Learning from learning machines: a new generation of AI technology to meet the needs of science [59.261050918992325]
  We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data.
  arXiv  Detail & Related papers  (2021-11-27T00:55:21Z)
• Directions for Explainable Knowledge-Enabled Systems [3.7250420821969827]
  We leverage our survey of explanation literature in Artificial Intelligence and closely related fields to generate a set of explanation types. We define each type and provide an example question that would motivate the need for this style of explanation. We believe this set of explanation types will help future system designers in their generation and prioritization of requirements.
  arXiv  Detail & Related papers  (2020-03-17T04:34:29Z)
• Foundations of Explainable Knowledge-Enabled Systems [3.7250420821969827]
  We present a historical overview of explainable artificial intelligence systems. We focus on knowledge-enabled systems, spanning the expert systems, cognitive assistants, semantic applications, and machine learning domains. We propose new definitions for explanations and explainable knowledge-enabled systems.
  arXiv  Detail & Related papers  (2020-03-17T04:18:48Z)
• A general framework for scientifically inspired explanations in AI [76.48625630211943]
  We instantiate the concept of structure of scientific explanation as the theoretical underpinning for a general framework in which explanations for AI systems can be implemented. This framework aims to provide the tools to build a "mental-model" of any AI system so that the interaction with the user can provide information on demand and be closer to the nature of human-made explanations.
  arXiv  Detail & Related papers  (2020-03-02T10:32:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.