scholarly journals Development of an AI-based bot to Tibia MMORPG

2021 ◽  
Vol 2 (2) ◽  
pp. 1-9
Author(s):  
Thiago Castanheira Retes de Sousa ◽  
Rafael Lima de Carvalho
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Template Matching ◽  
Search Algorithm ◽  
Real Life ◽  
Input Image ◽  
Graph Search ◽  
As Graph ◽  
Simulation Results ◽  
And Performance

Artificial Intelligence has always been used in designing of automated agents for playing games such as Chess, Go, Defense of the Ancients 2, Snake Game, billiard and many others. In this work, we present the development and performance evaluation of an automated bot that mimics a real life player for the RPG Game Tibia. The automated bot is built using a combination of AI techniques such as graph search algorithm A* and computer vision tools like template matching. Using four algorithms to get global position of player in game, handle its health and mana, target monsters and walk through the game, we managed to develop a fully automated Tibia bot based in raw input image. We evaluated the performance of the agent in three different scenarios, collecting and analyzing metrics such as XP Gain, Supplies Usage and Balance. The simulation results shows that the developed bot is capable of producing competitive results according to in-game metrics when compared to human players.

scholarly journals Sky-A* Algorithm for Artificial Intelligence

2019 ◽  
Vol 8 (4) ◽  
pp. 5295-5297
Keyword(s):  
Artificial Intelligence ◽  
Shortest Path ◽  
Search Algorithm ◽  
Optimal Path ◽  
Optimal Result ◽  
A Algorithm ◽  
Heuristic Knowledge ◽  
Limited Memory ◽  
And Performance ◽  
High Level

We have discussed variant of informed search in this paper like A* search. The informed search algorithm is developed to run in given limited memory by use of heuristic knowledge with retraction methods. We introduce the sky-A* algorithm for solving shortest path between two nodes. The sky-A* algorithm developed a logic by which the obligatory nodes like A* can extend and return the optimal result joining the nodes. In addition, the method sky-A* is guaranteed to return an optimal path when heuristic information used. We present a number of different methods for both low and high level procedures and analysis their results and performance. Proposed algorithm provides accurate combination of surroundings of video segments in situations when camera movements are complex.

Animal Recognition and Eye Movements

2010 ◽  
Vol 57 (2) ◽  
pp. 117-125 ◽  
Author(s):  
Toby J. Lloyd-Jones ◽  
Juergen Gehrke ◽  
Jason Lauder
Keyword(s):  
Eye Movements ◽  
Response Times ◽  
Real Life ◽  
Object Orientation ◽  
Decision Task ◽  
Line Drawings ◽  
Object Decision ◽  
Individual Features ◽  
And Performance

We assessed the importance of outline contour and individual features in mediating the recognition of animals by examining response times and eye movements in an animal-object decision task (i.e., deciding whether or not an object was an animal that may be encountered in real life). There were shorter latencies for animals as compared with nonanimals and performance was similar for shaded line drawings and silhouettes, suggesting that important information for recognition lies in the outline contour. The most salient information in the outline contour was around the head, followed by the lower torso and leg regions. We also observed effects of object orientation and argue that the usefulness of the head and lower torso/leg regions is consistent with a role for the object axis in recognition.

МЕТОДЫ ДОСТИЖЕНИЯ МАКСИМАЛЬНОЙ ЭФФЕКТИВНОСТИ ПЛАТФОРМЫ ПРОТОТИПИРОВАНИЯ ВЫСОКОПРОИЗВОДИТЕЛЬНЫХ СИСТЕМ НА КРИСТАЛЛЕ НА ЗАДАЧАХ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

2020 ◽  
Vol 96 (3s) ◽  
pp. 585-588
Author(s):  
С.Е. Фролова ◽  
Е.С. Янакова
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Computer Vision ◽  
High Performance ◽  
Systems On Chip ◽  
High Performance Systems ◽  
On Chip ◽  
Network Technologies ◽  
Neural Network Technologies

Предлагаются методы построения платформ прототипирования высокопроизводительных систем на кристалле для задач искусственного интеллекта. Изложены требования к платформам подобного класса и принципы изменения проекта СнК для имплементации в прототип. Рассматриваются методы отладки проектов на платформе прототипирования. Приведены результаты работ алгоритмов компьютерного зрения с использованием нейросетевых технологий на FPGA-прототипе семантических ядер ELcore. Methods have been proposed for building prototyping platforms for high-performance systems-on-chip for artificial intelligence tasks. The requirements for platforms of this class and the principles for changing the design of the SoC for implementation in the prototype have been described as well as methods of debugging projects on the prototyping platform. The results of the work of computer vision algorithms using neural network technologies on the FPGA prototype of the ELcore semantic cores have been presented.

Embodying algorithms, enactive artificial intelligence and the extended cognition: You can see as much as you know about algorithm

2021 ◽  
pp. 016555152098549
Author(s):  
Donghee Shin
Keyword(s):  
Artificial Intelligence ◽  
Embodied Cognition ◽  
Cognitive Processes ◽  
Process Model ◽  
Extended Cognition ◽  
Dual Process ◽  
Personalized Recommendation ◽  
Normative Values ◽  
And Performance ◽  
Concise Guide

The recent proliferation of artificial intelligence (AI) gives rise to questions on how users interact with AI services and how algorithms embody the values of users. Despite the surging popularity of AI, how users evaluate algorithms, how people perceive algorithmic decisions, and how they relate to algorithmic functions remain largely unexplored. Invoking the idea of embodied cognition, we characterize core constructs of algorithms that drive the value of embodiment and conceptualizes these factors in reference to trust by examining how they influence the user experience of personalized recommendation algorithms. The findings elucidate the embodied cognitive processes involved in reasoning algorithmic characteristics – fairness, accountability, transparency, and explainability – with regard to their fundamental linkages with trust and ensuing behaviors. Users use a dual-process model, whereby a sense of trust built on a combination of normative values and performance-related qualities of algorithms. Embodied algorithmic characteristics are significantly linked to trust and performance expectancy. Heuristic and systematic processes through embodied cognition provide a concise guide to its conceptualization of AI experiences and interaction. The identified user cognitive processes provide information on a user’s cognitive functioning and patterns of behavior as well as a basis for subsequent metacognitive processes.

EXPRESS: Baseline Correction Based on a Search Algorithm from Artificial Intelligence

2020 ◽  
pp. 000370282097751
Author(s):  
Xin Wang ◽  
Xia Chen
Keyword(s):  
Artificial Intelligence ◽  
Objective Function ◽  
Least Squares ◽  
Least Squares Method ◽  
Search Algorithm ◽  
Absolute Error ◽  
Search Problem ◽  
Baseline Correction ◽  
Current Spectrum ◽  
Data Points

Many spectra have a polynomial-like baseline. Iterative polynomial fitting (IPF) is one of the most popular methods for baseline correction of these spectra. However, the baseline estimated by IPF may have substantially error when the spectrum contains significantly strong peaks or have strong peaks located at the endpoints. First, IPF uses temporary baseline estimated from the current spectrum to identify peak data points. If the current spectrum contains strong peaks, then the temporary baseline substantially deviates from the true baseline. Some good baseline data points of the spectrum might be mistakenly identified as peak data points and are artificially re-assigned with a low value. Second, if a strong peak is located at the endpoint of the spectrum, then the endpoint region of the estimated baseline might have significant error due to overfitting. This study proposes a search algorithm-based baseline correction method (SA) that aims to compress sample the raw spectrum to a dataset with small number of data points and then convert the peak removal process into solving a search problem in artificial intelligence (AI) to minimize an objective function by deleting peak data points. First, the raw spectrum is smoothened out by the moving average method to reduce noise and then divided into dozens of unequally spaced sections on the basis of Chebyshev nodes. Finally, the minimal points of each section are collected to form a dataset for peak removal through search algorithm. SA selects the mean absolute error (MAE) as the objective function because of its sensitivity to overfitting and rapid calculation. The baseline correction performance of SA is compared with those of three baseline correction methods: Lieber and Mahadevan–Jansen method, adaptive iteratively reweighted penalized least squares method, and improved asymmetric least squares method. Simulated and real FTIR and Raman spectra with polynomial-like baselines are employed in the experiments. Results show that for these spectra, the baseline estimated by SA has fewer error than those by the three other methods.

scholarly journals Random Forest with Adaptive Local Template for Pedestrian Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Tao Xiang ◽  
Tao Li ◽  
Mao Ye ◽  
Zijian Liu
Keyword(s):  
Computer Vision ◽  
Random Forest ◽  
Classification Accuracy ◽  
Template Matching ◽  
Detection Method ◽  
State Of The Art ◽  
Pedestrian Detection ◽  
Sliding Window ◽  
Experimental Results ◽  
Training Samples

Pedestrian detection with large intraclass variations is still a challenging task in computer vision. In this paper, we propose a novel pedestrian detection method based on Random Forest. Firstly, we generate a few local templates with different sizes and different locations in positive exemplars. Then, the Random Forest is built whose splitting functions are optimized by maximizing class purity of matching the local templates to the training samples, respectively. To improve the classification accuracy, we adopt a boosting-like algorithm to update the weights of the training samples in a layer-wise fashion. During detection, the trained Random Forest will vote the category when a sliding window is input. Our contributions are the splitting functions based on local template matching with adaptive size and location and iteratively weight updating method. We evaluate the proposed method on 2 well-known challenging datasets: TUD pedestrians and INRIA pedestrians. The experimental results demonstrate that our method achieves state-of-the-art or competitive performance.

Motivational clusters and performance in a real-life setting

2009 ◽  
Vol 33 (1) ◽  
pp. 49-62 ◽  
Author(s):  
Nicolas Gillet ◽  
Robert J. Vallerand ◽  
Elisabeth Rosnet
Keyword(s):  
Real Life ◽  
Real Life Setting ◽  
And Performance

scholarly journals The Role of Steps and Game Elements in Gamified Fitness Tracker Apps: A Systematic Review

2021 ◽  
Vol 5 (2) ◽  
pp. 5
Author(s):  
Aatish Neupane ◽  
Derek Hansen ◽  
Jerry Alan Fails ◽  
Anud Sharma
Keyword(s):  
Social Influence ◽  
Design Space ◽  
Real Life ◽  
Network Clustering ◽  
And Performance ◽  
Fitness Tracker ◽  
Game Elements ◽  
The Relationship ◽  
Contingent Rewards

This article reviews 103 gamified fitness tracker apps (Android and iOS) that incorporate step count data into gameplay. Games are labeled with a set of 13 game elements as well as meta-data from the app stores (e.g., avg rating, number of reviews). Network clustering and visualizations are used to identify the relationship between game elements that occur in the same games. A taxonomy of how steps are used as rewards is provided, along with example games. An existing taxonomy of how games use currency is also mapped to step-based games. We show that many games use the triad of Social Influence, Competition, and Challenges, with Social Influence being the most common game element. We also identify holes in the design space, such as games that include a Plot element (e.g., Collaboration and Plot only co-occur in one game). Games that use Real-Life Incentives (e.g., allow you to translate steps into dollars or discounts) were surprisingly common, but relatively simple in their gameplay. We differentiate between task-contingent rewards (including completion-contingent and engagement-contingent) and performance-contingent rewards, illustrating the differences with fitness apps. We also demonstrate the value of treating steps as currency by mapping an existing currency-based taxonomy onto step-based games and providing illustrations of nine different categories.

scholarly journals Deep learning-enabled medical computer vision

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Andre Esteva ◽  
Katherine Chou ◽  
Serena Yeung ◽  
Nikhil Naik ◽  
Ali Madani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Medical Imaging ◽  
Real World ◽  
Recent Progress ◽  
Medical Applications ◽  
Modern Computer ◽  
Medical Computer

AbstractA decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

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