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...
Possible Future Impacts of Artificial Intelligence
Authors: Jerry Kaplan
<p>Is progress in AI accelerating?</p>
<p>Not all subfields of AI proceed at the same pace, in part because they build on progress in other fields. For example, improvements in the physical capabilities of robots have been relatively slow, since they are dependent on advances...</p>
π
Published: 2016-11-24
π Source: CrossRef
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...
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...
Death and Suicide in Universal Artificial Intelligence
Authors: Jarryd Martin, Tom Everitt, Marcus Hutter
Reinforcement learning (RL) is a general paradigm for studying intelligent behaviour, with applications ranging from artificial intelligence to psychology and economics. AIXI is a universal solution to the RL problem; it can learn any computable environment. A technical subtlety of AIXI is that it is defined using a mixture over semimeasures that need not sum to 1, rather than over proper probabil...