Temporal difference reinforcement learning algorithms are perfectly suited to autonomous agents because they learn directly from an agent’s experience based on sequential actio...
We address the learning of trust based on past observations and context information. We argue that from the truster's point of view trust is best expressed as one of several ...
We discuss the problem of finding a good state representation in stochastic systems with observations. We develop a duality theory that generalizes existing work in predictive sta...
Christopher Hundt, Prakash Panangaden, Joelle Pine...
In this paper, we address the issue of evaluating decision trees generated from training examples by a learning algorithm. We give a set of performance measures and show how some ...
Modeling learning agents in the context of Multi-agent Systems requires an adequate understanding of their dynamic behaviour. Usually, these agents are modeled similar to the diï¬...