Advanced training systems

Training is a major endeavor in all modern societies. Common training methods include training manuals, formal classes, procedural computer programs, simulations, and on-the-job training. NASA's training approach has focussed primarily on on-the-job training in a simulation environment for both crew and ground based personnel. NASA must explore new approaches to training for the 1990's and beyond. Specific autonomous training systems are described which are based on artificial intelligence technology for use by NASA astronauts, flight controllers, and ground based support personnel that show an alternative to current training systems. In addition to these specific systems, the evolution of a general architecture for autonomous intelligent training systems that integrates many of the features of traditional training programs with artificial intelligence techniques is presented. These Intelligent Computer Aided Training (ICAT) systems would provide much of the same experience that could be gained from the best on-the-job training

Intelligent computer-aided training and tutoring

Specific autonomous training systems based on artificial intelligence technology for use by NASA astronauts, flight controllers, and ground-based support personnel that demonstrate an alternative to current training systems are described. In addition to these specific systems, the evolution of a general architecture for autonomous intelligent training systems that integrates many of the features of traditional training programs with artificial intelligence techniques is presented. These Intelligent Computer-Aided Training (ICAT) systems would provide, for the trainee, much of the same experience that could be gained from the best on-the-job training. By integrating domain expertise with a knowledge of appropriate training methods, an ICAT session should duplicate, as closely as possible, the trainee undergoing on-the-job training in the task environment, benefitting from the full attention of a task expert who is also an expert trainer. Thus, the philosophy of the ICAT system is to emulate the behavior of an experienced individual devoting his full time and attention to the training of a novice - proposing challenging training scenarios, monitoring and evaluating the actions of the trainee, providing meaningful comments in response to trainee errors, responding to trainee requests for information, giving hints (if appropriate), and remembering the strengths and weaknesses displayed by the trainee so that appropriate future exercises can be designed

Michael Martin and the moral argument for God\u27s existence

Theism, that is, belief in the existence of God, has, over the last forty or so years, been making a quiet comeback. Whereas for several decades the “death of God” was heralded—culminating, perhaps, with Time magazine’s April 8, 1966, title: “Is God Dead?”—philosophers are once again vigorously debating the rationality of theistic belief. Emerging from amid this renaissance is an increasing number of publications treating the various so-called “theistic proofs” or arguments for God’s existence. These arguments are part of the project of natural theology, that is, the project of establishing the rationality of theistic belief apart from appeal to authoritative divine revelation. One such argument, called the axiological argument or the moral argument, attempts to establish the existence of God a posteriori from the existence of objective moral values. It is the aim of this thesis to defend the moral argument from atheist Michael Martin, one of its most distinguished detractors. Not surprisingly, many atheists have attempted to refute the moral argument. Many (if not most) atheists, such as the late J. L. Mackie, simply reject the objectivity of morals, embracing instead moral relativism. Contemporary atheist Richard Dawkins goes so far as to deny the reality of good and evil altogether. What makes Martin’s response particularly interesting, however, is his moral realism or his agreement that objective morals do exist. The issue, then, is whether Martin’s worldview furnishes him with the metaphysical resources necessary to ground objective morality while denying God’s existence. It is, of course, one thing to see that morality is objective and another thing altogether to ground that fact. I will argue that Martin’s worldview does not provide him the underpinnings necessary to sustain his position, thus rebutting his attack on the moral argument

Extinction is Forever

Edited by Joan Bray and Teri Rosen. Research by La Tanya McNeal. This booklet is an overview of the ice ages, extirpated Animals, extinct animals, endangered animals, and humans as an endangered species. In addition, it explores the impact that humans have on the environment and the problems with climate change.https://digitalcommons.unf.edu/eco_education/1001/thumbnail.jp

On the Impossibility of Learning to Cooperate with Adaptive Partner Strategies in Repeated Games

Learning to cooperate with other agents is challenging when those agents also possess the ability to adapt to our own behavior. Practical and theoretical approaches to learning in cooperative settings typically assume that other agents' behaviors are stationary, or else make very specific assumptions about other agents' learning processes. The goal of this work is to understand whether we can reliably learn to cooperate with other agents without such restrictive assumptions, which are unlikely to hold in real-world applications. Our main contribution is a set of impossibility results, which show that no learning algorithm can reliably learn to cooperate with all possible adaptive partners in a repeated matrix game, even if that partner is guaranteed to cooperate with some stationary strategy. Motivated by these results, we then discuss potential alternative assumptions which capture the idea that an adaptive partner will only adapt rationally to our behavior.Comment: 9 pages, to be published in The Proceedings of the 39th International Conference on Machine Learning, 202

Intelligent Computer-Aided Training (ICAT)

The Software Technology Branch has developed and demonstrated a number of ICAT System for a variety of complex procedural tasks in the NASA operational environment. A general ICAT architecture was developed and shown to be adaptable across this spectrum of tasks. Currently underway is the assembly of a suite of software tools that will permit the training community to rapidly develop and deploy ICAT systems for a variety of Space Station training tasks. The use of ICAT technology for selected training applications within the Space Station Freedom program can significantly reduce the costs of training system development. Once developed ICAT systems can be more readily and efficiently evolved and maintained than many conventional training systems. ICAT systems can be delivered for both ground based and on-orbit training. The availability of sophisticated on-orbit training will serve to reduce EVA time and can be especially useful in preparing crew for the performance on infrequent, mission critical tasks. ICAT systems can deliver uniform but individualized training to large numbers of personnel in a workstation environment

Towards a Unifying Model of Rationality in Multiagent Systems

Multiagent systems deployed in the real world need to cooperate with other agents (including humans) nearly as effectively as these agents cooperate with one another. To design such AI, and provide guarantees of its effectiveness, we need to clearly specify what types of agents our AI must be able to cooperate with. In this work we propose a generic model of socially intelligent agents, which are individually rational learners that are also able to cooperate with one another (in the sense that their joint behavior is Pareto efficient). We define rationality in terms of the regret incurred by each agent over its lifetime, and show how we can construct socially intelligent agents for different forms of regret. We then discuss the implications of this model for the development of "robust" MAS that can cooperate with a wide variety of socially intelligent agents.Comment: 5 Pages, To appear in the OptLearnMAS Workshop at AAMAS 202

Better Exploration with Optimistic Actor-Critic

Actor-critic methods, a type of model-free Reinforcement Learning, have been successfully applied to challenging tasks in continuous control, often achieving state-of-the art performance. However, wide-scale adoption of these methods in real-world domains is made difficult by their poor sample efficiency. We address this problem both theoretically and empirically. On the theoretical side, we identify two phenomena preventing efficient exploration in existing state-of-the-art algorithms such as Soft Actor Critic. First, combining a greedy actor update with a pessimistic estimate of the critic leads to the avoidance of actions that the agent does not know about, a phenomenon we call pessimistic underexploration. Second, current algorithms are directionally uninformed, sampling actions with equal probability in opposite directions from the current mean. This is wasteful, since we typically need actions taken along certain directions much more than others. To address both of these phenomena, we introduce a new algorithm, Optimistic Actor Critic, which approximates a lower and upper confidence bound on the state-action value function. This allows us to apply the principle of optimism in the face of uncertainty to perform directed exploration using the upper bound while still using the lower bound to avoid overestimation. We evaluate OAC in several challenging continuous control tasks, achieving state-of the art sample efficiency.Comment: 20 pages (including supplement

BeeMapper Quick Guide

BeeMapper is an interactive web tool that displays land cover and predicted wild bee abundance throughout the Maine wild blueberry production landscape. Information from BeeMapper can be used to: 1. Determine placement of honey bee hives during blueberry pollination. 2. Establish a pollinator conservation plan for particular crop fields. 3. Understand wild bee communities in different types of land. The Users Guide provides instructions on using the tool, interpreting its data, and suggests wild bee conservation and management actions. View the Bee Mapper Website