Opponent Modeling Under Partial Observability in StarCraft with Deep Convolutional Encoder-Decoders

2019 ◽  
pp. 751-759
Author(s):  
Hyungu Kahng ◽  
Seoung Bum Kim
Keyword(s):  
Partial Observability ◽  
Opponent Modeling ◽  
Convolutional Encoder

scholarly journals Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network

2019 ◽  
Vol 11 (10) ◽  
pp. 1157 ◽  
Author(s):  
Jorge Fuentes-Pacheco ◽  
Juan Torres-Olivares ◽  
Edgar Roman-Rangel ◽  
Salvador Cervantes ◽  
Porfirio Juarez-Lopez ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Image Data ◽  
Ground Truth ◽  
Aerial Images ◽  
Aerial Image ◽  
Data Set ◽  
Visual Appearance ◽  
Aerial Robots ◽  
Lighting Conditions ◽  
Convolutional Encoder

Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms.

scholarly journals Programming and reasoning with partial observability

2020 ◽  
Vol 4 (OOPSLA) ◽  
pp. 1-28
Author(s):  
Eric Atkinson ◽  
Michael Carbin
Keyword(s):  
Partial Observability

scholarly journals Can Social Agents Efficiently Perform in Automated Negotiation?

2021 ◽  
Vol 11 (13) ◽  
pp. 6022
Author(s):  
Victor Sanchez-Anguix ◽  
Okan Tunalı ◽  
Reyhan Aydoğan ◽  
Vicente Julian
Keyword(s):  
Utility Function ◽  
Nash Bargaining ◽  
Equilibrium Strategy ◽  
Automated Negotiation ◽  
Social Agents ◽  
Social Agent ◽  
Opponent Modeling ◽  
Social Metrics ◽  
Tit For Tat ◽  
Final Agreement

In the last few years, we witnessed a growing body of literature about automated negotiation. Mainly, negotiating agents are either purely self-driven by maximizing their utility function or by assuming a cooperative stance by all parties involved in the negotiation. We argue that, while optimizing one’s utility function is essential, agents in a society should not ignore the opponent’s utility in the final agreement to improve the agent’s long-term perspectives in the system. This article aims to show whether it is possible to design a social agent (i.e., one that aims to optimize both sides’ utility functions) while performing efficiently in an agent society. Accordingly, we propose a social agent supported by a portfolio of strategies, a novel tit-for-tat concession mechanism, and a frequency-based opponent modeling mechanism capable of adapting its behavior according to the opponent’s behavior and the state of the negotiation. The results show that the proposed social agent not only maximizes social metrics such as the distance to the Nash bargaining point or the Kalai point but also is shown to be a pure and mixed equilibrium strategy in some realistic agent societies.

scholarly journals SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

2017 ◽  
Vol 39 (12) ◽  
pp. 2481-2495 ◽  
Author(s):  
Vijay Badrinarayanan ◽  
Alex Kendall ◽  
Roberto Cipolla
Keyword(s):  
Image Segmentation ◽  
Decoder Architecture ◽  
Convolutional Encoder
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