  In this article we present the motivation and the core thesis towards the
implementation of a Quantum Knowledge Seeking Agent (QKSA). QKSA is a general
reinforcement learning agent that can be used to model classical and quantum
dynamics. It merges ideas from universal artificial general intelligence,
constructor theory and genetic programming to build a robust and general
framework for testing the capabilities of the agent in a variety of
environments. It takes the artificial life (or, animat) path to artificial
general intelligence where a population of intelligent agents are instantiated
to explore valid ways of modelling the perceptions. The multiplicity and
survivability of the agents are defined by the fitness, with respect to the
explainability and predictability, of a resource-bounded computational model of
the environment. This general learning approach is then employed to model the
physics of an environment based on subjective observer states of the agents. A
specific case of quantum process tomography as a general modelling principle is
presented. The various background ideas and a baseline formalism are discussed
in this article which sets the groundwork for the implementations of the QKSA
that are currently in active development.
