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Found 10 papers, 4 papers with code
Laser Learning Environment: A new environment for coordination-critical multi-agent tasks
We introduce the Laser Learning Environment (LLE), a collaborative multi-agent reinforcement learning environment in which coordination is central.
Mitigating Biases in Collective Decision-Making: Enhancing Performance in the Face of Fake News
Individual and social biases undermine the effectiveness of human advisers by inducing judgment errors which can disadvantage protected groups.
Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making
Experts advising decision-makers are likely to display expertise which varies as a function of the problem instance.
ACROCPoLis: A Descriptive Framework for Making Sense of Fairness
Fairness is central to the ethical and responsible development and use of AI systems, with a large number of frameworks and formal notions of algorithmic fairness being available.
Dealing with Expert Bias in Collective Decision-Making
Quite some real-world problems can be formulated as decision-making problems wherein one must repeatedly make an appropriate choice from a set of alternatives.
Voluntary safety commitments provide an escape from over-regulation in AI development
With the introduction of Artificial Intelligence (AI) and related technologies in our daily lives, fear and anxiety about their misuse as well as the hidden biases in their creation have led to a demand for regulation to address such issues.
Artificial Intelligence Development Races in Heterogeneous Settings
Regulation of advanced technologies such as Artificial Intelligence (AI) has become increasingly important, given the associated risks and apparent ethical issues.
Mediating Artificial Intelligence Developments through Negative and Positive Incentives
The field of Artificial Intelligence (AI) is going through a period of great expectations, introducing a certain level of anxiety in research, business and also policy.
To regulate or not: a social dynamics analysis of the race for AI supremacy
As a consequence, different actors are urging to consider both the normative and social impact of these technological advancements.
Dynamic Weights in Multi-Objective Deep Reinforcement Learning
In the dynamic weights setting the relative importance changes over time and specialized algorithms that deal with such change, such as a tabular Reinforcement Learning (RL) algorithm by Natarajan and Tadepalli (2005), are required.
Multi-Objective Reinforcement Learning
reinforcement-learning
