Learning Automata (LA) were recently shown to be valuable tools for designing Multi-Agent Reinforcement Learning algorithms. One of the principal contributions of LA theory is tha...
This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of fini...
In this paper, we study a particular subclass of partially observable models, called quasi-deterministic partially observable Markov decision processes (QDET-POMDPs), characterize...
Analyzing sequence data has become increasingly important recently in the area of biological sequences, text documents, web access logs, etc. In this paper, we investigate the pro...
Solomonoff unified Occam’s razor and Epicurus’ principle of multiple explanations to one elegant, formal, universal theory of inductive inference, which initiated the field...