Multi-Agent Blackboard Architecture For Supporting Legal Decision Making

Our research objective is to design a system to support legal decision making using the multi-agent blackboard architecture. Agents represent experts that may apply various knowledge-processing algorithms and knowledge sources. Experts cooperate with each other using the blackboard to store facts about a current case. Knowledge is represented as a set of rules. The inference process is based on bottom-up control (forward chaining). The goal of our system is to find rationales for arguments that support different decisions for a given case by using precedents and statutory knowledge. Our system also uses top-down knowledge from statutes and precedents to interactively query the user for additional facts when such facts could affect the judgment. The rationales for various judgments are presented to the user, who may choose the most appropriate one. We present two example scenarios in Polish traffic law to illustrate the features of our system. Based on these results, we argue that the blackboard architecture provides an effective approach to modeling situations where a multitude of possibly conflicting factors must be taken into account in the decision making.