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The successes of Artificial Intelligence in recent years in areas such asimage analysis, natural language understanding and strategy games have sparkedinterest 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 theskill of experienced human traders. Obvious economic implications aside, thisis certainly an area of great scientific interest, due to the challenges thatsuch a real context poses to the use of AI techniques. Precisely for thisreason, we must be aware that artificial agents capable of operating at suchlevels 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 ofthe effort. In the course of this article, we review the issues inherent in thedesign of effective robotic traders as well as the consequently applicablesolutions, having in view the general objective of bringing the current stateof the art of robo-trading up to the next level of intelligence, which we referto as Cognitive Trading. Key to our approach is the joining of twomethodological and technological directions which, although both deeply rootedin the disciplinary field of artificial intelligence, have so far gone theirseparate ways: heuristics and learning.

Integrating Heuristics and Learning in a Computational Architecture for Cognitive Trading