Workflow agents versus expert systems: Problem solving methods in work systems design

Abstract

AbstractDuring the 1980s, a community of artificial intelligence researchers became interested in formalizing problem solving methods (PSMs) as part of an effort called “second-generation expert systems.” We provide an example of how we are applying second-generation expert systems concepts in an agent-based system for space flight operations, the orbital communications adapter mirroring system (OCAMS), which was developed in the Brahms multiagent framework. Brahms modeling language provides an ontology for simulating work practices, including groups, agents, activities, communications, movements, and geographic areas.Activitiesare a behavioral unit of analysis to be contrasted withtasks, a functional unit of analysis. Problem solving occurs in the context of activities in the service of tasks; appropriate PSMs depend on the context: which people/roles are participating, what tools are available, how the results will be evaluated, and so forth. A work practice simulation facilitates designing workflow tools that appropriately interact with the physical and organizational context in which work occurs. OCAMS was developed using a simulation-to-implementation methodology, in which a prototype workflow tool was embedded in a Brahms simulation of how people would use the tool. The reusable components in a workflow system like OCAMS include entire “problem solvers” (e.g., a planning subsystem), interoperability frameworks, and agents that inspect and change the world. Thus, a tool kit for building workflow tools requires more than a library of PSMs, which play a relatively small role in the overall multiagent, systems-integration architecture. Our research concern has shifted to situations that may arise that are outside the OCAMS' capability. In practical decision making, people must reflect on the validity of their models. As programs becoming actors in the workplace, we need to develop systems that help people to understand the limitations of the models that drive the automated operations, which means in part detecting when the formalizations in the system are inadequate.