Herein we build statistical foundations for tackling the navigation problem users encounter during web interaction, based on a formal model of the web in terms of a probabilistic automaton, which can also be viewed as a finite ergodic Markov chain. In our model of the web the probabilities attached to state transitions have two interpretations, namely, they can denote the proportion of times a ...

In a wide range of complex networks, the links between the nodes are temporal and may sporadically appear and disappear. This temporality is fundamental to analyzing the formation of paths within such networks. Moreover, the presence of the links between the nodes is a random process induced by nature in many real-world networks. In this paper, we study random temporal networks at a microscopic...

The theory of complex networks provides effective mathematical tools for analyzing properties of communication systems. This paper discusses an approach for modeling fault-tolerance in self-organizing peer-to-peer overlays. The evolution of the network is characterized by resorting to a failure and an attachment rate probabilities, together with equations modeling the protocol that peers adopt ...

In this paper, we show how our AI opponents learn internal representations of probabilities. We use a Bayesian interpretation of such subjectivist probabilities but do not implement full Bayesian methods of parameter estimation since we wish the AIs to be as human-like as possible. Thus the parameters of the subjectivist probabilities are learned incrementally.

In this paper we demonstrate that machine learning (using Abductive ILP) can generate plausible and testable food webs from ecological data. In this approach, unlike previous applications of Abductive ILP, the abductive predicate ‘eats’ is entirely undefined before the start of the learning. We also explore a new approach, called Hypothesis Frequency Estimation (HFE), for estimating probabiliti...