Pat Langley Arizona State University and Institute for the Study of Learning and Expertise

1. Expertise, Transfer, and Innovation in Cognitive Systems Pat Langley Arizona State University and Institute for the Study of Learning and Expertise http://www.isle.org/ 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.

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

3. 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 A 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. A Definition of Transfer performance performance experience experience learning curve for task A different intercept on task B performance performance experience experience different learning rate on task B different asymptote on task B

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

5. Claims about Computational Transfer • Much important transfer concerns goal-directed behavior that involves sequential actions aimed toward an objective. • Transfer benefits from the reuse of cognitive structures. • Organizing structures in a hierarchy aids reuse and transfer. • 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. • Vertical transfer results from skill learning that builds upon structures acquired earlier. • Lateral transfer benefits from knowledge-based inference that recognizes equivalent situations. Experiments with multiple cognitive architectures (ICARUS, Soar, Companions) on multiple testbeds support these claims.

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