  Computational agents support humans in many areas of life and are therefore
found in heterogeneous contexts. This means they operate in rapidly changing
environments and can be confronted with huge state and action spaces. In order
to perform services and carry out activities in a goal-oriented manner, agents
require prior knowledge and therefore have to develop and pursue
context-dependent policies. However, prescribing policies in advance is limited
and inflexible, especially in dynamically changing environments. Moreover, the
context of an agent determines its choice of actions. Since the environments
can be stochastic and complex in terms of the number of states and feasible
actions, activities are usually modelled in a simplified way by Markov decision
processes so that, e.g., agents with reinforcement learning are able to learn
policies, that help to capture the context and act accordingly to optimally
perform activities. However, training policies for all possible contexts using
reinforcement learning is time-consuming. A requirement and challenge for
agents is to learn strategies quickly and respond immediately in cross-context
environments and applications, e.g., the Internet, service robotics,
cyber-physical systems. In this work, we propose a novel simulation-based
approach that enables a) the representation of heterogeneous contexts through
knowledge graphs and entity embeddings and b) the context-aware composition of
policies on demand by ensembles of agents running in parallel. The evaluation
we conducted with the "Virtual Home" dataset indicates that agents with a need
to switch seamlessly between different contexts, can request on-demand composed
policies that lead to the successful completion of context-appropriate
activities without having to learn these policies in lengthy training steps and
episodes, in contrast to agents that use reinforcement learning.
