Integrating Heuristics and Learning in a Computational Architecture for
  Cognitive Trading
  The successes of Artificial Intelligence in recent years in areas such as
image analysis, natural language understanding and strategy games have sparked
interest from the world of finance. Specifically, there are high expectations,
and ongoing engineering projects, regarding the creation of artificial agents,
known as robotic traders, capable of juggling the financial markets with the
skill of experienced human traders. Obvious economic implications aside, this
is certainly an area of great scientific interest, due to the challenges that
such a real context poses to the use of AI techniques. Precisely for this
reason, we must be aware that artificial agents capable of operating at such
levels are not just round the corner, and that there will be no simple answers,
but rather a concurrence of various technologies and methods to the success of
the effort. In the course of this article, we review the issues inherent in the
design of effective robotic traders as well as the consequently applicable
solutions, having in view the general objective of bringing the current state
of the art of robo-trading up to the next level of intelligence, which we refer
to as Cognitive Trading. Key to our approach is the joining of two
methodological and technological directions which, although both deeply rooted
in the disciplinary field of artificial intelligence, have so far gone their
separate ways: heuristics and learning.
