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Found 9 papers, 0 papers with code
Predicting Plans and Actions in Two-Player Repeated Games
The obtained results show that the proposed Bayesian approach is well suited for modeling agents in two-player repeated games.
E-HBA: Using Action Policies for Expert Advice and Agent Typification
Past research has studied two approaches to utilise predefined policy sets in repeated interactions: as experts, to dictate our own actions, and as types, to characterise the behaviour of other agents.
An Empirical Study on the Practical Impact of Prior Beliefs over Policy Types
To address this problem, researchers have studied learning algorithms which compute posterior beliefs over a hypothesised set of policies, based on the observed actions of the other agents.
Information Design in Crowdfunding under Thresholding Policies
We introduce a heuristic algorithm to dynamically compute information-disclosure policies for the entrepreneur, followed by an empirical evaluation to demonstrate its competitiveness over the widely-adopted immediate-disclosure policy.
Regulating Highly Automated Robot Ecologies: Insights from Three User Studies
As in the study of political systems in which governments regulate human societies, our studies analyze how interactions between HARE and regulators are impacted by regulatory power and individual (robot or agent) autonomy.
Cooperating with Machines
Here, we combine a state-of-the-art machine-learning algorithm with novel mechanisms for generating and acting on signals to produce a new learning algorithm that cooperates with people and other machines at levels that rival human cooperation in a variety of two-player repeated stochastic games.
An Online Mechanism for Ridesharing in Autonomous Mobility-on-Demand Systems
With proper management, Autonomous Mobility-on-Demand (AMoD) systems have great potential to satisfy the transport demands of urban populations by providing safe, convenient, and affordable ridesharing services.
Belief and Truth in Hypothesised Behaviours
The idea is to hypothesise a set of types, each specifying a possible behaviour for the other agents, and to plan our own actions with respect to those types which we believe are most likely, given the observed actions of the agents.
Non-myopic learning in repeated stochastic games
In repeated stochastic games (RSGs), an agent must quickly adapt to the behavior of previously unknown associates, who may themselves be learning.
