The core problem that drives my research is to develop computational agents that exhibit human-level intelligence: real-time systems that persist for long periods of time while autonomously contending with, and improving performance on, a variety of complex problems. General intelligence is a fascinating pursuit in its own right, but also supports diverse applications that are rich, interactive, and adaptive, such as training systems and personal robotic assistants.
An important capability that humans have is to integrate higher-order knowledge with lower-level cognitive processing in order to make intelligent decisions. Research in this space is challenging and rare, as it requires bridging numerous processing, representational, and political dichotomies that include symbolic vs. sub-symbolic, discrete vs. continuous, and online/incremental vs. batch.
Human-level intelligence must contend with the infinite complexities of the real world, including sensing multiple modalities (e.g., vision, audition), localization, and actuation such as to achieve goals.
A vital aspect of human-level intelligence long-term memory: people are able to accumulate large amounts of experience and access it later in flexible ways, all while dealing with, in real time, the myriad challenges of everyday life. Long-term memory is crucial to human-level reasoning, as illustrated by the deficits in patients afflicted with anterograde amnesia (e.g. H.M.); however, no computational systems support this capability over long periods of time.
The work described below has been implemented within the Soar Cognitive Architecture, which is open-source, cross-platform, and comes with comprehensive documentation and tutorials.