Effectiveness of greedily collecting items in open world games
August 17, 2016 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Andrej Gajduk
arXiv ID
1608.06175
Category
cs.AI: Artificial Intelligence
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Since Pokemon Go sent millions on the quest of collecting virtual monsters, an important question has been on the minds of many people: Is going after the closest item first a time-and-cost-effective way to play? Here, we show that this is in fact a good strategy which performs on average only 7% worse than the best possible solution in terms of the total distance traveled to gather all the items. Even when accounting for errors due to the inability of people to accurately measure distances by eye, the performance only goes down to 16% of the optimal solution.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Artificial Intelligence
π
π
The Cartographer
R.I.P.
π»
Ghosted
Explanation in Artificial Intelligence: Insights from the Social Sciences
R.I.P.
π»
Ghosted
Federated Machine Learning: Concept and Applications
R.I.P.
π»
Ghosted
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
R.I.P.
π»
Ghosted
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
R.I.P.
π»
Ghosted
Rainbow: Combining Improvements in Deep Reinforcement Learning
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted