Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if
harnessed appropriately, may deliver the best of expectations over many application sectors …
harnessed appropriately, may deliver the best of expectations over many application sectors …
[HTML][HTML] Autonomous agents modelling other agents: A comprehensive survey and open problems
SV Albrecht, P Stone - Artificial Intelligence, 2018 - Elsevier
Much research in artificial intelligence is concerned with the development of autonomous
agents that can interact effectively with other agents. An important aspect of such agents is …
agents that can interact effectively with other agents. An important aspect of such agents is …
Model-free opponent shaping
In general-sum games the interaction of self-interested learning agents commonly leads to
collectively worst-case outcomes, such as defect-defect in the iterated prisoner's dilemma …
collectively worst-case outcomes, such as defect-defect in the iterated prisoner's dilemma …
A survey of real-time strategy game AI research and competition in StarCraft
This paper presents an overview of the existing work on AI for real-time strategy (RTS)
games. Specifically, we focus on the work around the game StarCraft, which has emerged in …
games. Specifically, we focus on the work around the game StarCraft, which has emerged in …
In the blink of an eye: leveraging blink-induced suppression for imperceptible position and orientation redirection in virtual reality
Immersive computer-generated environments (aka virtual reality, VR) are limited by the
physical space around them, eg, enabling natural walking in VR is only possible by …
physical space around them, eg, enabling natural walking in VR is only possible by …
COLA: consistent learning with opponent-learning awareness
Learning in general-sum games is unstable and frequently leads to socially undesirable
(Pareto-dominated) outcomes. To mitigate this, Learning with Opponent-Learning …
(Pareto-dominated) outcomes. To mitigate this, Learning with Opponent-Learning …
A survey of opponent modeling in adversarial domains
S Nashed, S Zilberstein - Journal of Artificial Intelligence Research, 2022 - jair.org
Opponent modeling is the ability to use prior knowledge and observations in order to predict
the behavior of an opponent. This survey presents a comprehensive overview of existing …
the behavior of an opponent. This survey presents a comprehensive overview of existing …
Greybox XAI: A Neural-Symbolic learning framework to produce interpretable predictions for image classification
Abstract Although Deep Neural Networks (DNNs) have great generalization and prediction
capabilities, their functioning does not allow a detailed explanation of their behavior …
capabilities, their functioning does not allow a detailed explanation of their behavior …
[PDF][PDF] Identifying patterns in combat that are predictive of success in MOBA games.
P Yang, BE Harrison, DL Roberts - FDG, 2014 - ciigar.csc.ncsu.edu
ABSTRACT Multiplayer Online Battle Arena (MOBA) games rely primarily on combat to
determine the ultimate outcome of the game. Combat in these types of games is highly …
determine the ultimate outcome of the game. Combat in these types of games is highly …
Artificial intelligence and virtual worlds–toward human-level AI agents
VM Petrović - IEEE Access, 2018 - ieeexplore.ieee.org
Artificial Intelligence (AI) has a long tradition as a scientific field, with tremendous
achievements accomplished in the decades behind us. At the same time, in the last few …
achievements accomplished in the decades behind us. At the same time, in the last few …