Abstract— Bayesian filtering is a general framework for recursively estimating the state of a dynamical system. The most common instantiations of Bayes filters are Kalman filt...
We present tractable, exact algorithms for learning actions' effects and preconditions in partially observable domains. Our algorithms maintain a propositional logical repres...
Models of agent-environment interaction that use predictive state representations (PSRs) have mainly focused on the case of discrete observations and actions. The theory of discre...
This paper deals with the on-line monitoring of large systems modeled as Petri Nets under partial observation. The plant observation is given by a subset of transitions whose occu...
In this paper, we define an observation model based on optical flow information to track objects using particle filter algorithms. Although the optical flow information enables us...