Active User Interfaces For Building Decision-Theoretic Systems

Knowledge elicitation/acquisition continues to be a bottleneck to constructing, decision-theoretic systems. Methodologies and techniques for incremental elicitation/acquisition of knowledge especially under uncertainty in support of users' current goals is desirable. This paper presents PESKI, a probabilistic expert system development environment. PESKI provides users with a highly interactive and integrated suite of intelligent knowledge engineering tools for decision-theoretic systems. From knowledge acquisition, data mining, and verification and validation to a distributed inference engine for querying knowledge, PESKI is based on the concept of active user interfaces actuators to the human-machine interface. PESKI uses a number of techniques to reduce the inherent complexity of developing a cohesive, real-world knowledge-based system. This is accomplished by providing multiple communication modes for human-computer interaction and the use of a knowledge representation endowed with the ability to detect problems with the knowledge acquired and alert the user to these possible problems. We discuss PESKI's use of these intelligent assistants to help users with the acquisition of knowledge especially in the presence of uncertainty.