In many real-world continuous action domains, human agents must decide which actions to attempt and then execute those actions to the best of their ability. However, humans cannot execute actions without error. Human performance in these domains can potentially be improved by the use of AI to aid in decision-making. One requirement for an AI to correctly reason about what actions a human agent should attempt is a correct model of that human's execution error, or skill. Recent work has demonstrated successful techniques for estimating this execution error with various types of agents across different domains. However, this previous work made several assumptions that limit the application of these ideas to real-world settings. First, previous work assumed that the error distributions were symmetric normal, which meant that only a single parameter had to be estimated. In reality, agent error distributions might exhibit arbitrary shapes and should be modeled more flexibly. Second, it was assumed that the execution error of the agent remained constant across all observations. Especially for human agents, execution error changes over time, and this must be taken into account to obtain effective estimates. To overcome both of these shortcomings, we propose a novel particle-filter-based estimator for this problem. After describing the details of this approximate estimator, we experimentally explore various design decisions and compare performance with previous skill estimators in a variety of settings to showcase the improvements. The outcome is an estimator capable of generating more realistic, time-varying execution skill estimates of agents, which can then be used to assist agents in making better decisions and improve their overall performance.

The difference between success and failure in football often lies in the ability to make the right split-second decisions on the field about where to run and when to tackle, pass or shoot. So how can clubs help players train their brains as well as their bodies? My colleagues and I are working with Chelsea FC academy to develop a system to measure these decision-making skills using artificial intelligence (AI). We're doing this by analysing several seasons of data that tracks players and the ball throughout each game, and developing a computer model of different playing positions. The computer model provides a benchmark to compare the performance of different players.

The difference between success and failure in football often lies in the ability to make the right split-second decisions on the field about where to run and when to tackle, pass or shoot. So how can clubs help players train their brains as well as their bodies? My colleagues and I are working with Chelsea FC academy to develop a system to measure these decision-making skills using artificial intelligence (AI) – a kind of robot coach or scout, if you will. We're doing this by analysing several seasons of data that tracks players and the ball throughout each game, and developing a computer model of different playing positions. The computer model provides a benchmark to compare the performance of different players.

The latest book by Daniel Kahneman, Olivier Sibony and Cass Sunstein has yet to be released, but the talk about Noise is well underway. Interviews, reviews, and essays have been making the rounds. A lot of ground has been covered. The reports go well beyond discussing the concept of noise, how it differs from bias, why this difference matter -- and how it all ties in with decision-making. Noise, defined as random errors caused by inconsistent decision-making, is said to cost organizations a lot of money. And, if accurate, the dollar amounts tossed around do sound quite alarming.

Algorithms are embedded into our technological lives, helping accomplish a variety of tasks like making sure that email makes it to your aunt or that you're matched to someone on a dating website who likes the same bands as you. Sure, such computer code aims to make our lives easier, but experts cited in a new report by Pew Research Center and Elon University's Imagining the Internet Center are worried that algorithms may also make us lose our ability to make decisions. After all, if the software can do it for us, why should we bother? "Algorithms are the new arbiters of human decision-making in almost any area we can imagine, from watching a movie (Affectiva emotion recognition) to buying a house (Zillow.com) to self-driving cars (Google)," Barry Chudakov, founder and principal at Sertain Research and StreamFuzion Corp., says in the report. But despite advances, algorithms may lead to a loss in human judgment as people become reliant on the software to think for them.

The conventional wisdom is that intelligent systems, while good with numbers and maybe facts, are not going to be able to cope with the world of judgment and decision-making. The common assumption is that computers will not be able to deal with the nuance of reasoning that drives the solely human ability to assess what is happening in the world and then make reasoned decisions in reaction to that assessment. And herein lies my problem -- the assumption hidden in this belief is that humans are actually good at this sort of reasoning. And it's not clear that this is true. In particular, we seem prone to reasoning mistakes based on biases in decision-making that hinder us every day.

The conventional wisdom is that intelligent systems, while good with numbers and maybe facts, are not going to be able to cope with the world of judgment and decision-making. The common assumption is that computers will not be able to deal with the nuance of reasoning that drives the solely human ability to assess what is happening in the world and then make reasoned decisions in reaction to that assessment. And herein lies my problem -- the assumption hidden in this belief is that humans are actually good at this sort of reasoning. And it's not clear that this is true. In particular, we seem prone to reasoning mistakes based on biases in decision-making that hinder us every day.