scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

Collaborative Systems for Decision Making for Disaster Preparedness and Response, Department of Information Systems

2010 ◽  
pp. 41-57
Author(s):  
Deidre Hahn ◽  
Jessica Block ◽  
Mark Keith ◽  
Ajay Vinze
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Disaster Preparedness ◽  
Large Scale ◽  
Emergency Situation ◽  
Group Processes ◽  
Group Decision Support Systems ◽  
Emergency Situations ◽  
Collaborative Technologies ◽  
Disaster Preparedness And Response

Real time collaboration solutions are critical during a large scale emergency situation and necessitate the coordination of multiple disparate groups. Collaborative technologies may be valuable in the planning and execution of disaster preparedness and response. Yet, research suggests that specific collaborative technologies, such as group decision support systems, are not often leveraged for decision-making during real time emergency situations in the United States. In this chapter, we propose a theoretical model of the impact of disaster immediacy and collaboration systems on group processes and outcomes. Using a 3D model of the dimensions of space, time, and situation, we explore media richness and group polarization within the context of collaboration technologies and disaster situations. We also present the next generation of collaboration technology extensions in order to address the need for more contemporary decisional settings. This set of principles and theories suggest how collaborative technologies may be positioned to better manage future disasters.

Artificial intelligence based commuter behaviour profiling framework using Internet of things for real-time decision-making

2020 ◽  
Vol 32 (20) ◽  
pp. 16057-16071 ◽  
Author(s):  
Tharindu Bandaragoda ◽  
Achini Adikari ◽  
Rashmika Nawaratne ◽  
Dinithi Nallaperuma ◽  
Ashish Kr. Luhach ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Internet Of Things ◽  
Real Time

Artificial Immune Systems

2008 ◽  
pp. 150-179
Author(s):  
Darryl Charles ◽  
Colin Fyfe ◽  
Daniel Livingstone ◽  
Stephen McGlinchey
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Artificial Immune Systems ◽  
Effective Model ◽  
Simple Games ◽  
Artificial Immune ◽  
Immune Systems ◽  
Strategy Games ◽  
Rts Game

We now consider the problem of introducing more intelligence into the artificial intelligence’s responses in real-time strategy games (RTS). We discuss how the paradigm of artificial immune systems (AIS) gives us an effective model to improve the AI’s responses and demonstrate with simple games how the AIS work. We further discuss how the AIS paradigm enables us to extend current games in ways which make the game more sophisticated for both human and AI. In this chapter, we show how strategies may be dynamically created and utilised by an artificial intelligence in a real-time strategy (RTS) game. We develop as simple as possible RTS games in order to display the power of the method we use.

scholarly journals Real-time analysis of a mass vaccination effort via an Artificial Intelligence platform confirms the safety of FDA-authorized COVID-19 vaccines

2021 ◽  
Author(s):  
Reid McMurry ◽  
Patrick Lenehan ◽  
Samir Awasthi ◽  
Eli Silvert ◽  
Arjun Puranik ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Adverse Effects ◽  
Real Time ◽  
Odds Ratio ◽  
Large Scale ◽  
Vaccination Campaign ◽  
Vaccine Dose ◽  
Incidence Rates ◽  
Low Frequencies ◽  
Clinical Notes

AbstractAs the COVID-19 vaccination campaign unfolds as one of the most rapid and widespread in history,it is important to continuously assess the real world safety of the FDA-authorized vaccines. Curation from large-scale electronic health records (EHRs) allows for near real-time safety evaluations that were not previously possible. Here, we advance context- and sentiment-aware deep neural networks over the multi-state Mayo Clinic enterprise (Minnesota, Arizona, Florida, Wisconsin) for automatically curating the adverse effects mentioned by physicians in over 108,000 EHR clinical notes between December 1st 2020 to February 8th 2021. We retrospectively compared the clinical notes of 31,069 individuals who received at least one dose of the Pfizer/BioNTech or Moderna vaccine to those of 31,069 unvaccinated individuals who were propensity matched by demographics, residential location, and history of prior SARS-CoV-2 testing. We find that vaccinated and unvaccinated individuals were seen in the the clinic at similar rates within 21 days of the first or second actual or assigned vaccination dose (first dose Odds Ratio = 1.13, 95% CI: 1.09-1.16; second dose Odds Ratio = 0.89, 95% CI: 0.84-0.93). Further, the incidence rates of all surveyed adverse effects were similar or lower in vaccinated individuals compared to unvaccinated individuals after either vaccine dose. Finally, the most frequently documented adverse effects within 7 days of each vaccine dose were fatigue (Dose 1: 1.77%, Dose 2: 1.2%),nausea (Dose 1: 1.05%, Dose 2: 0.84%), myalgia (Dose 1: 0.67%; Dose 2: 0.66%), diarrhea (Dose 1: 0.67%; Dose 2: 0.46%), arthralgia (Dose 1: 0.64%; Dose 2: 0.57%), erythema (Dose 1: 0.59%; Dose 2: 0.46%), vomiting (Dose 1: 0.45%, Dose 2: 0.29%) and fever (Dose 1: 0.29%; Dose 2: 0.23%). These remarkably low frequencies of adverse effects recorded in EHRs versus those derived from active solicitation during clinical trials (arthralgia: 24-46%; erythema: 9.5-14.7%; myalgia: 38-62%; fever: 14.2-15.5%) emphasize the rarity of vaccine-associated adverse effects requiring clinical attention. This rapid and timely analysis of vaccine-related adverse effects from contextually rich EHR notes of 62,138 individuals, which was enabled through a large scale Artificial Intelligence (AI)-powered platform, reaffirms the safety and tolerability of the FDA-authorized COVID-19 vaccines in practice.

Quantum-Computing-Inspired Algorithms in Machine Learning

2018 ◽  
pp. 1-26
Author(s):  
Deeksha Kaul ◽  
Harika Raju ◽  
B. K. Tripathy
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Quantum Computing ◽  
Computer Science ◽  
Real Time ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

In this chapter, the authors discuss the use of quantum computing concepts to optimize the decision-making capability of classical machine learning algorithms. Machine learning, a subfield of artificial intelligence, implements various techniques to train a computer to learn and adapt to various real-time tasks. With the volume of data exponentially increasing, solving the same problems using classical algorithms becomes more tedious and time consuming. Quantum computing has varied applications in many areas of computer science. One such area which has been transformed a lot through the introduction of quantum computing is machine learning. Quantum computing, with its ability to perform tasks in logarithmic time, aids in overcoming the limitations of classical machine learning algorithms.

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