The learning intelligent distribution agent (LIDA) and medical agent X (MAX): Computational intelligence for medical diagnosis

Abstract

The complexity of medical problem solving presents a formidable challenge to current theories of cognition. Building on earlier work, we claim that the systemslevel cognitive model LIDA (for “Learning Intelligent Distribution Agent”) offers a number of specific advantages for modeling diagnostic thinking. The LIDA Model employs a consciousness mechanism in an iterative cognitive cycle of understanding, attention, and action, endowing it with the ability to integrate multiple sensory modalities into flexible, dynamic, multimodal representations according to strategies that support specific task demands. These representations enable diverse, asynchronous cognitive processes to be dynamically activated according to rapidly changing contexts, much like in biological cognition. The recent completion of the LIDA Framework, a software API supporting the domain-independent LIDA Model, allows the construction of domain-specific agents that test the Model and/or enhance traditional machine learning algorithms with human-style problem solving. Medical Agent X (MAX) is a medical diagnosis agent under development using the LIDA Model and Framework. We review LIDA's approach to exploring cognition, assert its appropriateness for problem solving in complex domains such as diagnosis, and outline the design of an initial implementation for MAX.