Abstract: Each person holds numerous values that represent what is
believed to be important. As a result, our values inﬂuence our behavior
and play a role in practical reasoning. Various argumentation approaches
use values to justify actions, but they assume a function that determines
what values a state or action promotes and demotes. However, this is
often open for debate, since values are abstract and can be interpreted
in many ways. After giving an overview of how values are deﬁned in
social psychology, this paper deﬁnes values as preferences and introduces
several argument schemes to reason about preferences. These schemes
are used to give meaning to values and to determine whether values are
promoted or demoted. Furthermore, valuesystems are used for practical
reasoning and allow resolving conﬂicts when pursuing your values. An
example is given of how the new argument schemes can be used to do
practical reasoning using values.
Abstract: Abstract: Reinforcement learning (RL) comprises an array of techniques that learn a control
policy soas to maximize a reward signal. When applied to the control of elevator systems, RL
has the potential of ﬁnding better control policies than classical heuristic, suboptimal policies.
On theother hand, elevator systems oﬀer an interesting benchmark application for the study
of RL. In this paper, RL is applied toa single-elevator system. The mathematical model of
the elevator system is described in detail, making the system easy to re-implement and re-use.
An experimental comparison is made between the performance of the Q-value iteration and
Q-learning RL algorithms, when applied to the elevator system.