Pat Langley Arizona State University and Institute for the Study of Learning and Expertise Expertise, Transfer, and Innovation in Cognitive Systems Thanks to D. Aha, D. Choi, K. Forbus, J. Laird, S. Rogers, and T. Senator for useful discussions. This talk reports research funded by a grant from DARPA and AFRL, which are not responsible for its contents.

Expertise, Transfer, and Innovation Expertise is knowledge learned for some task/domain that benefits performance on that task/domain. Expertise is knowledge learned for some task/domain that benefits performance on that task/domain. Transfer involves a change in learning on a task/domain that: Transfer involves a change in learning on a task/domain that: results from expertise acquired on previous task/domains; results from expertise acquired on previous task/domains; occurs in an inherently sequential, incremental manner; occurs in an inherently sequential, incremental manner; can improve (positive) or worsen (negative) behavior. can improve (positive) or worsen (negative) behavior. Innovation extends or combines expertise to achieve goals. Innovation extends or combines expertise to achieve goals. Thus, it benefits from transfer but must move beyond it. Thus, it benefits from transfer but must move beyond it. Also, transfer is often automatic, whereas innovation usually requires conscious problem solving. Also, transfer is often automatic, whereas innovation usually requires conscious problem solving.

experience performance A learner exhibits transfer of knowledge from a source task/domain A to a target task/domain B when, after it has trained on A, it shows altered learning on B. learning curve for task A experience performance experience performance different intercept on task B different asymptote on task B different learning rate on task B A Definition of Transfer experience w/training on A w/o training on A w/training on A w/o training on A w/training on A w/o training on Aperformance

The degree of transfer depends on the structure shared with the training tasks. Transfer requires the ability to compose these knowledge elements dynamically. Transfer requires that knowledge be represented in a modular fashion. Representations/Processes that Support Transfer Transfer across domains requires abstract relations among representations.

Claims about Computational Transfer Much important transfer concerns goal-directed behavior that involves sequential actions aimed toward an objective. Much important transfer concerns goal-directed behavior that involves sequential actions aimed toward an objective. Transfer benefits from the reuse of cognitive structures. Transfer benefits from the reuse of cognitive structures. Organizing structures in a hierarchy aids reuse and transfer. Organizing structures in a hierarchy aids reuse and transfer. Indexing skills by goals they achieve determines relevance. Indexing skills by goals they achieve determines relevance. Learning hierarchical, relational, goal-directed skills can occur by analyzing traces of expert behavior and problem solving. Learning hierarchical, relational, goal-directed skills can occur by analyzing traces of expert behavior and problem solving. Vertical transfer results from skill learning that builds upon structures acquired earlier. Vertical transfer results from skill learning that builds upon structures acquired earlier. Lateral transfer benefits from knowledge-based inference that recognizes equivalent situations. Lateral transfer benefits from knowledge-based inference that recognizes equivalent situations. Experiments with multiple cognitive architectures (I CARUS, Soar, Companions) on multiple testbeds support these claims.

Opportunities for Mutual Benefit psychological experiments that reveal human behavior psychological experiments that reveal human behavior theories of the human cognitive architecture theories of the human cognitive architecture examinations of important educational domains examinations of important educational domains logical analyses of domains and representations logical analyses of domains and representations AI methods for retrieval, reasoning, and problem solving AI methods for retrieval, reasoning, and problem solving machine learning and creation of cognitive structures machine learning and creation of cognitive structures Studies of expertise, transfer, and innovation can benefit from: Our research has combined these paradigms to study the nature of expertise and transfer.