Artificial Intelligence Framework for Simulating Clinical Decision-Making: A Markov Decision Process Approach
Authors: Casey C. Bennett, Kris Hauser
In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (non-disease-specific) computational/artificial intelligence (AI) framework to ad...
Modeling Belief in Dynamic Systems, Part II: Revision and Update
Authors: N Friedman, J. Y. Halpern
The study of belief change has been an active area in philosophy and AI. In recent years two special cases of belief change, belief revision and belief update, have been studied in detail. In a companion paper (Friedman & Halpern, 1997), we introduce a new framework to model belief change. This framework combines temporal and epistemic modalities with a notion of plausibility, allowing us to exami...
A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?
Authors: Subrato Bharati, M. Rubaiyat Hossain Mondal, Prajoy Podder
Artificial intelligence (AI) models are increasingly finding applications in the field of medicine. Concerns have been raised about the explainability of the decisions that are made by these AI models. In this article, we give a systematic analysis of explainable artificial intelligence (XAI), with a primary focus on models that are currently being used in the field of healthcare. The literature s...
Games for Artificial Intelligence Research: A Review and Perspectives
Authors: Chengpeng Hu, Yunlong Zhao, Ziqi Wang
Games have been the perfect test-beds for artificial intelligence research for the characteristics that widely exist in real-world scenarios. Learning and optimisation, decision making in dynamic and uncertain environments, game theory, planning and scheduling, design and education are common research areas shared between games and real-world problems. Numerous open-source games or game-based envi...
Probability Judgement in Artificial Intelligence
Authors: Glenn Shafer
This paper is concerned with two theories of probability judgment: the Bayesian theory and the theory of belief functions. It illustrates these theories with some simple examples and discusses some of the issues that arise when we try to implement them in expert systems. The Bayesian theory is well known; its main ideas go back to the work of Thomas Bayes (1702-1761). The theory of belief function...