Incremental Learning of Discrete Planning Domains from Continuous
  Perceptions
  We propose a framework for learning discrete deterministic planning domains.
In this framework, an agent learns the domain by observing the action effects
through continuous features that describe the state of the environment after
the execution of each action. Besides, the agent learns its perception
function, i.e., a probabilistic mapping between state variables and sensor data
represented as a vector of continuous random variables called perception
variables. We define an algorithm that updates the planning domain and the
perception function by (i) introducing new states, either by extending the
possible values of state variables, or by weakening their constraints; (ii)
adapts the perception function to fit the observed data (iii) adapts the
transition function on the basis of the executed actions and the effects
observed via the perception function. The framework is able to deal with
exogenous events that happen in the environment.
