A multi-agent decision support system for stock trading

TLDR: The article introduces an architecture, the MASST system architecture, which supports dynamic information and knowledge exchange among the cooperating agents, and uses a dynamic blackboard as an interagent communication paradigm to facilitate factual data, business rule, and command exchange between cooperating MASST agents.

Abstract: A distributed problem solving system can be characterized as a group of individual cooperating agents running to solve common problems. As dynamic application domains continue to grow in scale and complexity, it becomes more difficult to control the purposeful behavior of agents, especially when unexpected events may occur. This article presents an information and knowledge exchange framework to support distributed problem solving. From the application viewpoint the article concentrates on the stock trading domain; however, many presented solutions can be extended to other dynamic domains. It addresses two important issues: how individual agents should be interconnected so that their resources are efficiently used and their goals accomplished effectively; and how information and knowledge transfer should take place among the agents to allow them to respond successfully to user requests and unexpected external situations. The article introduces an architecture, the MASST system architecture, which supports dynamic information and knowledge exchange among the cooperating agents. The architecture uses a dynamic blackboard as an interagent communication paradigm to facilitate factual data, business rule, and command exchange between cooperating MASST agents. The critical components of the MASST architecture have been implemented and tested in the stock trading domain, and have proven to be a viable solution for distributed problem solving based on cooperating agents.