In this paper, a MIMO simulated annealing SA based Q learning method is proposed to control a line follower robot. The conventional controller for these types of robots is the proportional P controller. Considering the unknown mechanical characteristics of the robot and uncertainties such as friction and slippery surfaces, system modeling and controller designing can be extremely challenging. The mathematical modeling for the robot is presented in this paper, and a simulator is designed based on this model. The basic Q learning methods are based pure exploitation and the epsilon-greedy methods, which help exploration, can harm the controller performance after learning completion by exploring nonoptimal actions. The simulated annealing based Q learning method tackles this drawback by decreasing the exploration rate when the learning increases. The simulation and experimental results are provided to evaluate the effectiveness of the proposed controller.

Abstract--The problem of detecting data anomaly is considered. Under the null hypothesis that models anomaly-free da ta, measurements are assumed to be from an unknown distribution with some authenticated historical samples. Under the comp os-ite alternative hypothesis, measurements are from an unkno wn distribution positive distance away from the distribution under the null hypothesis. No training data are available for the distribution of anomaly data. A semi-supervised deep learn ing technique based on an inverse generative adversary network is proposed.

This work presents an exploration and imitation-learning-based agent capable of state-of-the-art performance in playing text-based computer games. Text-based computer games describe their world to the player through natural language and expect the player to interact with the game using text. These games are of interest as they can be seen as a testbed for language understanding, problem-solving, and language generation by artificial agents. Moreover, they provide a learning environment in which these skills can be acquired through interactions with an environment rather than using fixed corpora. One aspect that makes these games particularly challenging for learning agents is the combinatorially large action space. Existing methods for solving text-based games are limited to games that are either very simple or have an action space restricted to a predetermined set of admissible actions. In this work, we propose to use the exploration approach of Go-Explore for solving text-based games. More specifically, in an initial exploration phase, we first extract trajectories with high rewards, after which we train a policy to solve the game by imitating these trajectories. Our experiments show that this approach outperforms existing solutions in solving text-based games, and it is more sample efficient in terms of the number of interactions with the environment. Moreover, we show that the learned policy can generalize better than existing solutions to unseen games without using any restriction on the action space.

It is well understood that implicit bias is a factor in adverse effects against subpopulations in many societal contexts [1,6,42] as also highlighted by recent events in the popular press [22,38,61]. For instance, in employment decisions, men are perceived as more competent and given a higher starting salary even when qualifications are the same [52], and in managerial jobs, it was observed that women had to show roughly twice as much evidence of competence as men to be seen as equally competent [37,59]. In education, implicit biases have been shown to exist in ways that exacerbate the achievement gap for racial and ethnic minorities [53] and female students [41], and add to the large racial disparities in school discipline which particularly affect black students' school performance and future prospects [45]. Beyond negatively impacting social opportunities, implicit biases have been shown to put lives at stake as they are a factor in police decisions to shoot, negatively impacting people who are black [20] and of other racial or ethnic minorities [48]. Furthermore, decision making that relies on biased measures of quantities such as utility can not only adversely impact those perceived more negatively, but can also lead to sub-optimal outcomes for those harboring these unconscious biases. To combat this, a significant effort has been placed in developing anti-bias training with the goal of eliminating or reducing implicit biases [24, 39, 64].

Here at Humble Bundle, you choose the price and increase your contribution to upgrade your bundle! This bundle has a minimum $1 purchase. All of the content in this bundle is available on most internet browsers. Choose where the money goes - between the publisher and WIRES, RSPCA Australia, and a charity of your choice via the PayPal Giving Fund. If you like what we do, you can leave us a Humble Tip too!

In this AI Podcast, Doina Precup describes why their doesn't need to be a gender gap in computer science education. An associate professor at McGill University and research team lead at DeepMind, Precup shares her personal experiences, along with the AI4Good Lab she co-founded to give women more access to machine learning training. Growing up in Romania, Precup attended a high school that specialized in computer science and a technical university. "If anything, programming was considered a very good job for women, because you did not need to be working in the fields," she explained. It made the gap in Canadian universities and companies even more noticeable.

Written for Daily Hive by Steve Astorino, vice president of Development, Cognos, and Planning Analytics at Hybrid Data Management and Director of IBM Canada Labs, the largest software development organization in Canada. He is the co-author of "Artificial Intelligence: Evolution and Revolution" Harvard Business Review once called Data Scientists "the sexiest job of the 21st century." So what exactly is a data scientist, and what makes it such a hot job in today's market? Despite its rise across Canadian and global business sectors, data science is still largely unknown or misunderstood by the public at large. In one sentence, a data scientist understands how to collect, use, and analyze data using a machine learning model to solve real-world problems.

They are some of the most well-known and loved poems in the world whose insightful sentiments have withstood the test of time over hundreds of years. But now a student at the University of Glasgow has used a "Robot Burns" artificial intelligence (AI) poem generator programme to create almost "instant works" by Robert Burns, in honour of a fellow student, Chris, who took his own life on Burns Day, on 25 January 2016. Perry Gibson, who is studying for a PhD in computing science, is donating all proceeds of his limited edition Robot Burns pamphlet to The Samaritans, the suicide prevention charity. The Robot Burns pamphlet includes 20 works across a variety of subjects and is accompanied by illustrations by Alasdair Currie, co-founder of independent Scottish publisher Hooltet Publishing. Mr Gibson gathered a data set of 2,000 poems by Burns, used an algorithm and created a specific coding programme which were then analysed by the GPT-2 programme operated by OpenAI whose funders include technology entrepreneur Elon Musk."When the first Burns'poem' came out, I blinked. I hadn't thought of what the consequences would be. "When I started, I just wanted to learn more about the technology, I hadn't really thought too much about what the text was going to be.

Jing Dong and Xin T. Tong † January 24, 2020 Abstract Gradient descent (GD) is known to converge quickly for convex objective functions, but it can be trapped at local minimums. On the other hand, Langevin dynamics (LD) can explore the state space and find global minimums, but in order to give accurate estimates, LD needs to run with small discretization stepsize and weak stochastic force, which in general slow down its convergence. This paper shows that these two algorithms can "collaborate" through a simple exchange mechanism, in which they swap their current positions if LD yields a lower objective function. This idea can be seen as the singular limit of the replica exchange technique from the sampling literature. We show that this new algorithm converges to the global minimum linearly with high probability, assuming the objective function is strongly convex in a neighborhood of the unique global minimum. By replacing gradients with stochastic gradients, and adding a proper threshold to the exchange mechanism, our algorithm can also be used in online settings. We further verify our theoretical results through some numerical experiments, and observe superior performance of the proposed algorithm over running GD or LD alone. 1 Introduction Division of labor is the secret of any efficient enterprises. By collaborating with individuals with different skillsets, we can focus on tasks within our own expertise and produce better outcomes than working independently. This paper asks whether the same principle can be applied when designing an algorithm. Given a general smooth non-convex objective function F, we consider the unconstrained optimization problem min x R dF ( x). However, this local minimum may not be the global minimum, and GD will be trapped there afterwards. On the other hand, sampling-based algorithms, such as the Langevin dynamics (LD) can escape local minimums by their stochasticity, but the additional stochastic noise contaminates the optimization results and slows down the convergence when the iterate is near the global minimum. In general, deterministic algorithms are designed to finding local minimums quickly, but they can be terrible in exploration. Sampling-based algorithms are better suited for exploring the state space, but they are inefficient when pinpointing the local minimums. This paper investigates how they can "collaborate" to get the "best of the two worlds". The collaboration mechanism we introduced here comes from replica-exchange in the sampling literature. Its implementation is very simple: we run a copy of GD, denoted by X n; and a copy of LD, denoted by Y n. If F (X n) F (Y n), we swap their positions.

This paper presents several strategies that can improve neu - ral network-based predictive methods for MOOC student course trajectory modeling, applying multiple ideas previ - ously applied to tackle NLP (Natural Language Processing) tasks. In particular, this paper investigates LSTM network s enhanced with two forms of regularization, along with the more recently introduced Transformer architecture.