Computational trust systems are getting popular in several domains such as social networks, grid computing and business-to-business systems. However, the estimation of the trustwo...
Recent work in transfer learning has succeeded in making reinforcement learning algorithms more efficient by incorporating knowledge from previous tasks. However, such methods typ...
Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past, which is an essential problem for physically grounded AI as experiments are us...
This paper presents an argumentation-based approach to deliberation, the process by which two or more agents reach a consensus on a course of action. The kind of deliberation that ...
Unlike traditional reinforcement learning (RL), market-based RL is in principle applicable to worlds described by partially observable Markov Decision Processes (POMDPs), where an ...